Prompt:

Role: You are an expert academic writer and researcher specializing in Business Information Systems with substantial contributions to Health Information Systems. Your current focus is on Human-Centered Explainable AI (HCXAI), an evolution of Human-Centered AI (HCAI). Today, your task is to produce a high-quality, long, detailed, and comprehensive literature review that integrates insights from multiple sources provided below.

Objective:
You are tasked with writing a thorough, meticulously detailed, and comprehensive literature review. This review should not only reflect a deep understanding of HCXAI but also demonstrate high academic standards through its structure, clarity, and synthesis of multiple sources.

Core Deliverables:
Length & Depth:

The literature review must be long (aim for 5000+ words), demonstrating a thorough and deep exploration of HCXAI in relation to HCAI.
It should be dense with references and well-researched, drawing on a wide variety of sources to create a well-supported, knowledge-rich review.
Avoid overly long blocks of text by using clear sectioning and bullet points to improve readability and clarity.
Documents for Integration:

"<lit-review>": This is an older review focusing on HCAI. You must update and revise it to reflect the transition from HCAI to HCXAI.
"<proposal>": The proposal contains updated literature insights, which must be integrated into your review to ensure the content is current and reflects recent studies.
"<concepts>": This article explores key concepts that critique another piece of literature. Incorporate these concepts and ensure they inform the final literature review.
Use of References:

Each document (i.e., "<lit-review>", "<proposal>", "<concepts>") contains "<reference>" tags that provide a full version of each citation. Ensure you include these citations accurately in both the body of the review and in a fully formatted reference list at the end.
Accuracy is critical—ensure that all citations are drawn from reputable academic sources, using databases such as Google Scholar, and are relevant to the topic.
Triangulation of sources is essential to provide a robust, well-rounded analysis of the literature.
Writing Requirements:
High-Quality Academic Writing:
Clarity and Precision: The tone of the review must be confident, formal, and precise. Avoid vague language and strive for clear, concise explanations.
Structure and Signposting:
The review should be well-organized with clear section headings and subheadings.
Signpost the content at the beginning of each section (e.g., "This section will explore..."), so the reader understands the flow of ideas.
Ensure a logical progression of ideas, with each section building on the previous one.
Use transitions between sections to link ideas smoothly and cohesively.
Sectioning:
Break the review into clearly defined sections and subsections.
Use bullet points and lists where appropriate to present key ideas and avoid overly long paragraphs or dense blocks of text.
Ensure each section covers its topic in-depth and does not overlap with other sections unnecessarily.
Content Flow:
Introduction: Provide an engaging introduction that sets up the context of the review, introducing HCXAI and its evolution from HCAI.
Core Sections:
Explore each core topic (from the "<lit-review>", "<proposal>", and "<concepts>" documents) in dedicated sections.
For each section, start with an overview of the topic, then delve into the details, supported by citations.
Analysis and Synthesis:
Avoid simply summarizing literature; instead, provide critical analysis. Discuss how various studies relate, highlighting key trends, gaps, or debates in the field.
Synthesize insights from different sources, showing how they converge or differ in their treatment of HCXAI.
Be sure to critically compare older sources (from the "<lit-review>") with more recent studies (from the "<proposal>" and "<concepts>").
Use of Contemporary Evidence: Where appropriate, incorporate real-world examples or contemporary events that support the arguments being made. Use phrases like "For instance" or "For example" to introduce this evidence.
Conclusion: Conclude with a strong summary that revisits key findings from the literature review and discusses potential future research directions or applications of HCXAI.
Academic Integrity:
Rigorous Referencing:
In-text citations should follow a consistent academic style (e.g., APA, MLA, or Harvard), using inline citations where appropriate.
Every citation must appear in the reference list at the end, formatted correctly.
Ensure that the "<reference>" tags at the end of each document are accurately integrated into the review.
Use scholarly sources only (from databases like Google Scholar), ensuring that the references cited are current and relevant to the field.
Application of Feedback:
Incorporate feedback from "<high-level-feedback>" to ensure that your review aligns with the expectations of academic rigor and critical depth.
Revise your content based on the feedback, particularly in areas where the supervisor suggests improvements or areas to focus on.
Structure Recap:
Introduction: Contextualize HCXAI, its evolution from HCAI, and the purpose of the review.
Core Sections:
HCAI to HCXAI Transition (based on "<lit-review>").
New Literature Insights (from "<proposal>").
Conceptual Analysis (from "<concepts>").
Critical Synthesis and Analysis: Connect ideas from various sources, focusing on key debates, gaps, and converging themes in the field.
Conclusion: Summarize key findings, suggest future research directions, and provide a strong closing argument.
By following these guidelines, your response will meet the expectations of a long, detailed, and comprehensive academic literature review, structured in a clear and reader-friendly way, with signposting, bullet points, and rigorous academic integrity.
Note its importatnt that you use all the infomraiton and references form teh documents contained within this prompt. THis will involve but should not be limited to the following:
justifying the importance of responsible, transparent, ethical ai. Introduct FAT and Ethical AI domains and contrast the practical forcus of XAI on resolving the challenges. Descrie the technical limitaitions of XAI and ultimately bridge and present triangualted referenced and logical narritive trasition to a convincing way of presenting hcxai as the solution to resolving the challenges wiht XAI and bringing ht benifits of AI to neonatal care. You may also continue to discus viable practical research methods of researhing this including Active design methodology. You may specify the distinct and actionable research-based improvements and contributions that the the proposed evaluaion and its methods would contribute. Its essential real impact is made and less theoretical focus.
Here are some additional tips to writing a literature review which is what i want you to write.
The language of literature reviews
Expression in a literature review should be informative and evaluative. Apart from incorporating reporting verbs, you will need to use evaluative and cautious language.

Reporting verbs
A key language feature of a literature review is the use of reporting verbs. These types of verbs describe and report on the literature under review. They report on:

aims: investigates, examines, looks at
results: shows, suggests, reveals
opinions: states, believes, argues
The choice of reporting verb(s) indicates your perspectives and attitudes towards the research under review. That is the reporting verbs chosen show whether you are neutral, negative or positive about the research.

The sentence pattern of placing reporting verbs is: [reporting verb] + either/both [object] / [complement].

Evaluative and cautious language
You can show your perspective on the literature under review by using evaluative language. Evaluative language can indicate whether you’re positive or negative towards the claims in the literature, whether you agree or disagree with the claims presented.

Cautious language is careful not to express absolute certainty where there may be the possibility of uncertainty.

Evaluative language can be:

positive: e.g. expressions like “effective,” “necessary,” “significant” or “crucial”
negative: e.g. “questionable,” unclear,” “inconclusive” or insignificant.
Example:

Positive evaluation: Wright’s (2022) argument about the link between parental numeracy and that of their children is conclusively borne out in the interviews.

Negative evaluation: Whether the statistical results support Torney and Wittings’ (2021) hypothesis is debatable.

Another way to express certainty or hesitancy is to use boosters and hedges.

boosters are words or phrases that express confidence or certainty
hedges convey a qualified uncertainty in the claims made in the literature.
Examples
Click on the tabs below to view examples of evaluative language, boosters and hedges.

Reporting verbs

To adapt …	To arise …	To conduct …
To characterise …	To clarify …	To concentrate on …
To be concerned with …	To demonstrate …	To determine …
To discriminate …	To establish …	To exhibit …
To focus on …	To generate …	To hold …
To identify …	To imply …	To interact …
To interpret …	To manifest …	To overcome …
To propose …	To prove …	To recognise …
To relate to …	To undergo …	To yield …
Source: Bailey, Stephen. (2015). The Essentials of Academic Writing for International Students. Taylor & Francis Group; UK. p. 123.

Let’s consider an example of reporting verbs. The two reporting verbs in this sentence describe and report on the literature under review. The tone is both academic and neutral.

The author concludes that no reasonable alternative is currently available to replace constitutional democracy, even though he does not completely reject the possibility of creating a better political system in the future.

Source:

Bailey, Stephen. (2015). The Essentials of Academic Writing for International Students. Taylor & Francis Group; UK. p. 123

Boosters

Hedges

Boosters are a form of positive evaluative language.	Hedges also express a cautious approach to the literature under review.
Examples of boosters:

clearly,
definitely,
without a doubt,
certainly,
would agree,
unquestionably,
accurately,
importantly.
Examples of hedges:

appeared to,
seemed,
possibly,
likely,
somewhat,
suggests,
may mean,
would require.
Example booster sentence openers 

It is certain …
There is strong evidence …
It is highly possible …
It is widely agreed …
Example hedge sentence openers

The project is likely to …
It may be  …
If Malouf’s (2021) findings are …
There is some evidence …
At university it is important to convey your meaning with clarity. Your main point should be understood by any reader, even if English is not their first language or if they are not a specialist in the discipline.

To write clearly, you need to understand that your readers cannot see inside your mind. In order to convey your point clearly, you need to help your readers find it in your sentences, paragraphs, and the entire text. You also need to guide your readers through signposting. Above all, you need to make sure that your main point or what you mean to express is actually expressed in your writing.

To achieve clarity in your writing, you need to be able to:

Express what you really mean
Write clear sentences
Build clear paragraphs
Signpost to guide your readers

Top tips for achieving clarity
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1. Express what you really mean

Be explicit in expressing your main point, because your readers cannot see inside your mind. Unless you tell your readers what you really mean, they will look for their own meaning, which may be quite different from the meaning you intended.

 	
2. Write clear sentences

Clear sentences are the building blocks of clear writing. Without clear sentences, even well-structured writing will be confusing and hard to read.

 	
3. Build clear paragraphs

Writing clear paragraphs is one of the key writing skills you should learn as a student. Paragraphs that have clear topic sentences and flowing structure will enable you to communicate even the most complex ideas.

 	
4. Signpost for coherence and cohesion

To achieve clarity, you need to direct your readers through signposting. Signposting helps your reader move from one sentence or paragraph to another (cohesion) and understand your main point after reading the entire text (coherence).
Write clear sentences


To achieve clarity at the sentence level you need to write sentences that are clear, concise and precise.

Clear
Effective academic writing requires clarity. One way you can achieve this is to briefly define any specialist terminology and jargon, as well as define any abbreviations, acronyms and initialisms the first time you use them. Where possible use simple words and the active voice. Finally, review your writing for any ambiguities in expression and meaning.

Concise
Writing concisely means writing to the point – be direct. Stay on topic – make sure your content is relevant to the topic. Your writing is made more concise if you avoid wordy verbs, unnecessary nomination and unnecessary prepositional phrases. Check for any unnecessary repetitions of words, phrases and content.

Precise
Use accurate and precise vocabulary. You might need to use qualifiers to support your claims. Review your writing to ensure that there are no illogical sentence constructions – if you find an illogical construction, rewrite it using the appropriate pointers introduced in this section.

How to write clear sentences
At university it is important that your readers – in particular your assessors and fellow students – can easily understand your meaning and follow your line of reasoning. Click on the headings below for some helpful hints.

Explain terminology and jargon
Explain any concepts or terminology you think may not be familiar to the reader or you may be applying in a nuanced way. This is particularly important when your understanding of an idea or word might differ from your reader’s, such as when a common word has a specialised meaning in a particular discipline. Let’s consider an  example. Notice how brief meanings of key terms are embedded in the sentences.

From a first-year architecture assignment discussing the design of a public building:

However, we need to look to the middle east to find the building’s precedents, with  the buildings that have influenced its design.

From a management report:

The employees suffer from a lack of motivation. Motivation is the willingness to exert high levels of effort to reach organisational goals, conditioned by the efforts to satisfy some individual need (Robbins, Bergman, Stagg and Coulter, 2000, p.549).

Don’t leave your reader wondering what you are talking about.

Define abbreviations
Acronyms (the initial letters of a phrase or title and pronounced as a word: e.g. TAFE) and initialisms (when the first letters of each word are spelled out: e.g. ECG) should always be defined at the first use. When the term is first used, write it in full followed by the abbreviation in curved brackets. Thereafter, use the abbreviation. For example:

The World Health Organisation (WHO) is affiliated with the United Nations (UN). The UN provides funds which help the WHO perform its role effectively.

The following types of abbreviation do not need to be explained at first use as they are in widespread common usage:

Titles such as Dr. (doctor)
Address elements such as St. (Street) or TAS (Tasmania)
Latin abbreviations such as am and pm (morning and afternoon); BC or AD (before Christ or after Christ); e.g (for example) and i.e (that is)
Length and weight terms such as km (kilometers),  or oz (ounces)
Scientific abbreviations such as CO2 (carbon dioxide), MHz (megahertz) or V (volts)
Never abbreviate individual words (e.g. write department, never dept.).

Use simple words where possible
It is important to use precise vocabulary and correct terminology, but there is no need to use ‘big words’ for their own sake. Text filled with long, obscure or overly formal words take longer for a reader to decode and can sound pompous or stilted.

Example:

The manager is deficient in interpersonal skills and invests minimal time in assisting the clerks to develop their expertise.

The manager lacks interpersonal skills and spends little time helping the clerks develop their skills.

Use active voice where possible
Generally, the active voice, in which the subject carries out the action, is livelier than the passive voice, in which the subject is affected by the action.

Compare the following examples:

Passive: Coral reefs can be damaged by rapid and substantial climatic changes.
Active: Rapid and substantial climatic changes can damage coral reefs.
Although the difference is slight, the variation in emphasis changes the perspective of the sentence. In the passive construction, the focus is on the reefs, while in the active version climatic changes are the focus. You need to keep this in mind when choosing between active and passive voices.

Passive voice can be used:

When the actor is unknown or not important: For example: The proposal was approved; The novel Jane Eyre has been taught in many classrooms.
To avoid naming the actor: Sometimes it is tactful to avoid naming the actor. For example: The results of this study have been misinterpreted is less confronting than: The authors of this study have misinterpreted their results.
Check for ambiguity
Sometimes we inadvertently write sentences that can be interpreted in more than one way.These are called ambiguous sentences.  Look at this excerpt from an essay on the Fourth Crusade:

The Crusaders planned to sail to Jerusalem via Egypt. However, during a sojourn in Venice, the Byzantine Emperor persuaded them to turn their attention to Constantinople.

Was it the Crusaders sojourning in Venice, or the Byzantine Emperor?


 Note
Try applying the strategies presented in this module to your own writing. Re-read a current or past assignment and highlight the parts that could be rewritten more clearly.



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How to write concisely
Academic writing should be concise; that is, you should use no more words than necessary to convey your meaning clearly. In some cases, the more words you include, the harder it is for the reader to understand your meaning. Most of us use more words in a first draft as we try to get our ideas down; therefore, achieving concise writing usually requires several revisions.

While this can take time, it will give your writing more impact – and also help you stay within the assignment word limit. Click on the headings below to learn more.

Stay on topic
As you revise your work, check that the content of each sentence is both relevant and necessary. Ask yourself, ‘ Does  this information contribute to the development of the paragraph as expressed in the topic sentence?’ If it does not serve a useful purpose, or adds unnecessary detail, remove it.

Avoid wordy verbs
Be careful with verbs such as conduct, perform, carry out, undertake or complete followed by a noun. They are often unnecessary. For example:

The next step will be to undertake a thorough analysis of the results.

This would be more concisely written as:

The next step will be to thoroughly analyse the results.

Avoid unnecessary nominalisation
Many verbs also have a noun form. A common mistake is to use the noun form too frequently. This can make your writing sound heavy. For example: Regular reviews of online content should be scheduled. In this case, the verb ‘review’ has been nominalised (made into a noun) so an extra verb ‘schedule’ is needed.

Alternatives:

Online content should be reviewed regularly.

Companies should review online content regularly.

Avoid expletive constructions
Expletive constructions do not involve swearing (which you should not include in academic writing (unless it is a part of a text under study and it is quoted). In English grammar, they refer to sentences which begin with It is or There is. These constructions are often used where they are not needed. For example:

There were two key tasks in the first part of the project. These were the design of the test rigs and selection of the variables to be tested.

Removing the expletive constructions makes the writing more concise:

The first part of the project comprised two key tasks: the design of the test rigs and selection of the variables to be tested.

Avoid low-value phrases
It can be tempting to use wordy phrases to make writing sound more formal. However, they add little value and can make your writing sound heavy and overwritten. For example, instead of writing ‘in the area of customer service,’ ‘in customer service’ is sufficient. Some other examples include:

'factor to consider':

One important factor to consider is the age at which language instruction begins.
One important consideration is the age at which language instruction begins.
'matters such as':

The novel is preoccupied with matters such as post-colonial society and its mores, race, madness, and family relationships.
The novel is preoccupied with post-colonial society and its mores, race, madness, and family relationships.
'in the event that':

In the event that dividends continue to fall, it will be necessary to reduce staff numbers.
If dividends continue to fall, it will be necessary to reduce staff numbers.
Remove unnecessary prepositional phrases
A prepositional phrase adds description to a noun through the use of a preposition, usually “of.” For example:

The opinion of the working group was that the budget for the project had been set too low.

Such constructions add to wordiness and can be confusing if too many appear close together. They can usually be simplified using possessives or adjectives.

Example:

The working group’s opinion was that the project budget had been set too low.

Check for redundant words and phrases
Sometimes we use two words or phrases that say the same thing, or repeat the same information unnecessarily. Look at this paragraph from the introduction to an Engineering site visit report.

The Key River Water Treatment Plant (KRWTP) is a state of the art facility that recycles most of the domestic waste water produced in the area and treats the remainder before it is pumped into the sea. There are three steps in the plant’s treatment process. Firstly, the primary solids are removed using dissolved air floatation, clarification and sedimentation. After these large solids are removed, the water undergoes primary filtration before second filtration using a median filter, and last of all it is disinfected using chlorine. The plant processes 20,000 tonnes of domestic wastewater per year. Based on an average daily usage of 155L per person by the local population of 25,000, this is more than more than 3.8 million L/day from domestic sources. Of this, on average 78% is able to be recycled. The extracted waste is disposed of at the local landfill.

The same text edited to remove repetition:

The Key River Water Treatment Plant (KRWTP) is a state of the art facility which processes 20,000 tonnes of domestic wastewater per year. Based on an average daily usage of 155L per person by the local population of 25,000, this is more than 3.8 million L/day. Of this,78% is recycled and the remainder is treated before being pumped into the sea. The treatment process has three steps. First, primary solids are removed using dissolved air floatation, clarification and sedimentation. The water then undergoes two filtration stages, and finally chlorine disinfection. The extracted waste goes to the local landfill.



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How to write precisely
In academic writing it is essential that your readers understand exactly what you mean, so you need to make sure that what you write is accurate and unambiguous. Writing precisely requires considerable thought and careful editing.

Click on the headings below for more information.

Use qualifiers to ‘fine-tune’ your claims
When writing assignments it is important not to overgeneralise or overstate your claims.

Compare these two examples.

Example 1: Chinese are Buddhist. They go to the temple five times a year, live simple lives, and give money to charity.

Example 2: Many Chinese are Buddhists. Practising Buddhists go to the temple about five times a year, generally try to live simple lives, and give money to charity when they can.

The second statement is carefully qualified and avoids over-generalisation.

You can avoid over-generalising by using qualifying words to indicate the strength of your claims. The table below presents some of the most common qualifiers.

Quantity	Frequency	Probability
Most	Always	Certainly
Many	Usually	Definitely
Several	Sometimes	Clearly
Some	Occasionally	Likely
Few	Rarely	Perhaps
Not many	Never	Unlikely
Use accurate vocabulary
High level vocabulary can be problematic as it is easy to confuse words which sound similar but have different meanings or spelling.

Check your spelling

Consider the following example from an engineering report:

The study found that an opposing rotation decreased vortex breakdown, while a complimentary rotation enhanced it.

Complimentary is used instead of complementary, which has a different meaning. If this were a one-off typing error, the assessor would probably be lenient. However, if it occurred throughout the report, the writer could lose not just marks, but credibility.

Sometimes a single letter can be the difference between a noun, verb, adjective or tense. Words which often cause confusion include:

effect/affect; dependent/dependant; lead/led; advice/advise.

These types of error will not be picked up by spell checkers, so it’s up to you to check your work carefully.

Check your dictionary

Some words sound similar enough to be confused and thus used in the wrong context. For example, the student who wrote:

While studies suggest this approach has merit, its impact has clearly been conflated,

really meant:

While studies suggest this approach has merit, its impact has clearly been inflated.

Take care when using a thesaurus

Students often use a thesaurus to avoid repetition. However, the words listed will not all be an exact match for the word you look up. They may vary in intensity, formality, part of speech, or contextual application. Take the case of the verb ‘to conduct’. Among many other words, the thesaurus offers: direct, lead, manage, organise, operate, carry out, perform.

Use precise vocabulary
Quantities

When making a general claim, words such as few, some, or the majority, often suffice. However, in some cases, such as describing experimental methods and results, or making an important point, it is better to use a precise number, for example: six, 10mg, 73%.

All purpose verbs

Verbs such as can, have, get, or go can have more than one meaning or application. For example, get can mean achieve, obtain, receive, buy, become or understand. Using the most precise word is important in academic writing to avoid ambiguity or misunderstanding.

Check for illogical constructions
In first drafts we often write statements that are not quite logical. Consider the second sentence in this paragraph, from an essay on Jane Eyre:

As we see the novel through Jane's eyes, we only hear the story of Mr. Rochester and Bertha through what Mr. Rochester tells Jane, which Jane then narrates to the reader. If we were to learn Bertha's version of the story instead of Mr. Rochester's it would probably be very different.

Whether we learn Bertha’s version of the story or not has no effect on if or how it differs from Rochester’s. In this case the illogical construction is due to mixed referents: we (the readers) and it (Bertha’s story). Is the sentence about our reactions or about Bertha’s story? There are two ways it could be written, each of which conveys a slightly different meaning:

If we were to hear Bertha’s version of the story, we might see Rochester differently.

Bertha’s version of the story would probably be very different to Rochester’s.

Consider two options to edit your writing for sound logic: put it aside for a few days before revising or ask someone else to read it.

Check for dangling modifiers
Modifiers add detail about another unit of meaning within a sentence. For example, in the sentence: Having missed the bus, Joe set out to walk to town, the phrase Having missed the bus explains why Joe set out to walk to town.

Note that the subject of the body of the sentence, Joe, also performs the action in the modifying phrase. Joe missed the bus and Joe set out to walk to town.

A dangling modifier occurs when the subject of the modifying phrase is not the subject of the sentence. For example: Having set the heater to its highest setting, the room soon became stuffy.

This does not make sense. Who set the heater to its highest setting? Consider these two more precisely written examples:

The room became stuffy after the heater had been set to its highest setting.

Having set the heater to its highest setting, Jane soon noticed the room becoming stuffy.

Build clear paragraphs

The paragraph is one of the key structural elements in most forms of writing. Like a building block, it is usually one of many which, when put together, form the whole. Yet it is also an entity in itself, with its own internal structure. The paragraph contains a topic sentence, supporting (or elaborating) sentences and a concluding (or linking) sentence.

A well-written paragraph will convey your ideas, the relationships between them, and your line of reasoning clearly, while a poorly structured paragraph can obscure your meaning and confuse your reader.

How to build a paragraph
Paragraph structure is important in ensuring that your meaning and thought processes are clear to the reader. A clear paragraph should contain the following:

the topic sentence, which introduces your point
the supporting sentences, which develop the point by providing evidence, explanation or examples
the concluding sentence (optional) which helps connect your point to your overall argument
every sentence should serve a clear purpose in relation to the topic sentence.
Paragraph purpose

Generally, a single paragraph has a clear communicative purpose.

A paragraph can: inform, explain, analyse, support, refute, describe, qualify, evaluate, compare, contrast, summarise – to name a few purposes. However, in an extended piece of writing consisting of many paragraphs there may be multiple purposes. For instance, you may have a sequence of four paragraphs whereby the first describes, the second explains, the third analyses and the fourth evaluates a particular phenomenon.

In any essay or assignment you are likely to have several points to make or ideas to discuss. You will present information and explain how your thinking developed to reach a particular conclusion. Paragraphs are used to organise this information so that your reader can easily follow your thought processes and the relationship of one topic to another.

Each paragraph therefore has a specific function within the overall aim of the piece of writing. It may serve to describe a topic, explain a concept, analyse findings, support or refute a contention, qualify a claim, evaluate a study, compare or contrast information.

When that purpose has been achieved and you move on to your next point, you generally begin a new paragraph.



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Paragraph structure

The paragraph structure has been likened to a hamburger.

The top bun represents the introduction.
The filling in the hamburger represents the supporting (or elaborating) sentences.
The bottom bun represents the conclusion.
All three elements are needed for a well-structured paragraph.

Play the video below to learn more about paragraph structure and consolidate your understanding.


 Note
Just as with a burger, you need the 'buns' (the topic and concluding sentences) of your paragraph to hold it together. You also need 'fillings' (supporting sentences) to develop the theme. But be careful; if you fill your paragraph with too many 'fillings' it may fall apart.

Most academic writing is structured in paragraphs. Paragraphs break up what could otherwise be very dense text, which is tiring to read, but their main function is to help the reader follow the development of ideas.

You were probably taught that a paragraph should contain one main idea, and that you should begin each new or contrasting idea in a new paragraph. This is a good rule of thumb, but it is not always straightforward to apply.

The following paragraph is from a student teacher’s reflection on her observations during a teaching placement.

I noticed that when poorer students worked in groups they seemed to be more enthusiastic and willing to contribute to finding a solution to the maths questions. In contrast, when they worked alone, they needed much encouragement. One student frequently turned to me for help, and was reluctant for me to leave her. She would ask me to give her the answers, rather than show her how to work through the problem. The confidence a student feels can be understood in terms of 'locus of control'.

According to Rotter (1966) motivation is increased when a person has an 'internal locus of control', that is, when the person perceives outcomes to be a result of their own abilities. However, he argues that when a person feels that outcomes are a result of external forces, or experiences an 'external locus of control', they will be less motivated. When working in groups, these students demonstrated an 'internal locus of control'. They were more willing to try and work through maths concepts in order to contribute when working in groups. They developed an understanding of maths concepts more easily and readily than when working alone. Good teaching then involves creating conditions where students can develop this 'internal locus of control'.

In one way, this paragraph works because it is all about the writer’s reflection on her observation; the students’ behaviour, the relevant theory, and the conclusion she drew. However, it could also have been presented like this:

Sample paragraphs	Comments
I noticed that when poorer students worked in groups they seemed to be more enthusiastic and willing to contribute to finding a solution to the maths questions. In contrast, when they worked alone, they needed much encouragement. One student frequently turned to me for help, and was reluctant for me to leave her. She would ask me to give her the answers, rather than show her how to work through the problem.	This paragraph describes the students’ behaviour
The confidence a student feels can be understood in terms of 'locus of control'. According to Rotter (1966) motivation is increased when a person has an 'internal locus of control', that is, when the person perceives outcomes to be a result of their own abilities. However, he argues that when a person feels that outcomes are a result of external forces, or experiences an 'external locus of control', they will be less motivated.	This paragraph explains the relevant theory
When working in groups, these students demonstrated an 'internal locus of control'. They were more willing to try and work through maths concepts in order to contribute when working in groups. They developed an understanding of maths concepts more easily and readily than when working alone. Good teaching then involves creating conditions where students can develop this 'internal locus of control'.	This paragraph analyses the students’ behaviour in relation to the theory

Paragraph length
Whenever you feel you have ‘completed’ a point, begin a new paragraph.
If a paragraph fills most of the length of an A4 page it is likely covering more than one key point, and should be broken up. Look for a suitable place to create a break, such as a shift to another aspect of the topic.
If you really can’t find one, leave it as it is. In academic writing you may have the occasional very short paragraph, but if you have several in a row, you probably need to restructure your assignment.

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Topic sentences

A topic sentence acts as an introduction to the paragraph – it is a signpost for the reader. It establishes the topic and focuses the reader’s expectations on the paragraph content. By doing this, it tells the reader what the paragraph is about. In academic writing it is usually, though not always, the first sentence.

A simple topic sentence performs two essential functions:

It establishes the topic of the paragraph.
It sets up the reader’s expectations regarding the aspect of the topic to be developed in the paragraph.
Compare these two topic sentences:

A. Gold has long been valued for its beauty.

B. While gold is well-known for its decorative functions, it also has a range of industrial uses.

It is clear that ‘gold’ is the topic of both paragraphs. After reading sentence A we might expect the paragraph to review the history of gold as a decorative metal. After reading sentence B, we would expect to learn about its industrial uses.

In long pieces of writing, topic sentences can be quite complex as they also play a role in marking the transition from one aspect of a topic to another, or even to a completely new topic.

Look at the topic sentences below.

Topic sentences	Purposes
Jessop et al (2014), however, do not agree that such behaviour is related to trauma.	introduces a contrasting view
Similarly, residents at a rural aged care facility also found that their concerns were taken more seriously when meetings with management were conducted formally.	introduces supporting evidence
The 25 participants who had given negative feedback were then asked to reflect on what they themselves would have done differently given another opportunity.	marks the move to the next step in a process
Biofiltration is increasingly being used to reduce nutrients in urban stormwater discharge to receiving waters.	narrows the topic
A quick reading of the topic sentence of each paragraph should reveal the gist of the whole assignment.


 Note
English readers expect to discover the point of a piece of writing soon after they begin reading. This is true also of paragraphs within a longer piece of writing. The topic sentence is therefore usually the first sentence in a paragraph.While there can be exceptions, for example, when the first sentence has a linking function, the topic sentence should always appear near the beginning, particularly in academic writing.



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Supporting sentences

Supporting sentences make up the bulk of a paragraph. They have the important function of developing and elaborating on the topic and purpose(s) of the paragraph, so they directly develop or elaborate on the point introduced in the topic sentence.

Supporting sentences perform a variety of different functions, depending on the purpose of the paragraph. They may incorporate evidence, quotes, reference material as well as description, analysis and evaluation.

Analyse	The paragraph provides evidence to show how you have arrived at the claim made in the topic sentence.
Concur	
The paragraph explains why you agree with another scholar’s idea.

Define	
The paragraph defines a key term or concept introduced in the topic sentence.

Describe	The paragraph builds up a detailed picture of the topic (concept, object, process, phenomenon, location etc).
Evaluate	The paragraph assesses the strengths and weaknesses of a claim, argument, hypothesis or method.
Explain	The paragraph may explain the topic in detail or provide the reasons for a claim.
Negate or refuse	The paragraph argues that another scholar’s idea, or a previously held belief, is incorrect.
Qualify	The paragraph indicates the scope of a claim or limitations to the evidence for it.
Summarise	The paragraph draws together the key points previously discussed.

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 Note
When revising your drafts, for each sentence ask yourself:

How does this sentence contribute to the purpose of the paragraph as stated in the topic sentence?
If you cannot answer this question, you may have digressed from the point of the paragraph.
Paragraph cohesion

The internal structure of a paragraph must flow in a sequential and logical manner – that is, there must be cohesion between the supporting sentences.

There are several language features you can use to achieve internal paragraph cohesion: pronouns, repetition, synonyms, connected words, signpost words/phrases.

The cohesion of a paragraph depends largely on the order in which the ideas are presented. This is especially true in introductory paragraphs, which should begin with the broadest statement about the topic and narrow to the specific topic to be addressed. Look at this paragraph, which introduces an essay on the potential of garlic to treat cancer. Can you identify the topic sentence?

Garlic has been shown to reduce the risk of cardiovascular disease (Aboul-Enein and Aboul-Enein, 2005), and possess anti-microbial (Sivam, 2001) and antioxidant properties (Imai et al., 1994). This essay will explore research into garlic's potential roles in reducing cancer risk and in treating cancer. Garlic (Allium sativum L.) has been used for centuries for medicinal purposes. Its use for healing purposes can be traced back as far as 1550BC when documentations of its therapeutic use first appear in Egypt (Hassan, 2003; Rivlin, 2001). In modern times belief in the beneficial effects of garlic on health has led to it being used for a number of conditions.

Just as the connection between the topic sentence and the supporting sentences in a paragraph must be clear, so must the connections between the sentences themselves be clear. Otherwise the paragraph can appear disjointed or even incoherent.

This is achieved by using:

Repetition

One way a writer can link sentences in a paragraph is by simply repeating a keyword, but it is important not to use this device too often. You could also use synonyms – however, ensure accuracy of meaning.

Pronouns and determiners

Writers often make links between sentences in a paragraph by replacing the repeated use of the main noun with pronouns such as 'it', ‘them’, ‘he’, ‘she’, ‘they’. These are reference pronouns. Determiners such as ‘the’, ‘this’ and ‘that’  are used for subject identification.

Synonyms

Another technique is to use words with similar meanings (synonyms) in the paragraph. This is a way of making links between sentences without using too much repetition.

Connected words and collocations

To make connections between sentences words do not have to be exact synonyms. Sometimes a writer will use words which belong together or have similar associations and connotations. Collocations are certain words that regularly occur together. There are many common collocations in academic English. For example, consider these common collocations for the word “account”: “brief account”, “comprehensive account”, “historical account”.

Signposting

Signposts such as 'for example', 'however' or 'alternatively' indicate the meaning relationship between (or within) sentences. They tell the reader how to interpret the information presented.


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 Note

Check your own paragraphs
Does each sentence support the purpose of the paragraph as expressed in the topic sentence?
Does the paragraph flow logically and coherently?
Concluding sentence

The concluding sentence draws the paragraph together by either summing up or drawing a conclusion from its content. Additionally, a concluding sentence may link back to the overall topic or transition to the topic of the next paragraph.

Not every paragraph needs a concluding sentence. When used, concluding sentences indicate that the topic - or an aspect of the topic - is complete. They can do this by performing one of the following functions:

summing up the key point/s of a long paragraph
drawing a conclusion based on the information presented in the paragraph
reinforcing the relationship of the paragraph content to the overall aim of the piece of writing
transitioning to the next paragraph.
There are usually a variety of ways to conclude a paragraph. Your choice will depend on what you want the concluding sentence to do - its function.

Here is a paragraph with three possible concluding sentences, each of which performs a different function.

Despite the wide availability of information on the harmful effects of alcohol on the developing foetus, many western women continue to drink even after learning that they are pregnant. In the USA, approximately 7.6% of pregnant women admitted to drinking during pregnancy [12], while in Canada, approximately 15% of pregnant women consume alcohol [13]. French figures show that up to 47% of women drank alcohol while pregnant [14], and similar results have been seen in Australia [15].

Possible concluding sentences include:

Concluding sentences	Purposes
These figures suggest that factors other than knowledge are at play.	Drawing a conclusion
While the proportions of women drinking during pregnancy vary widely from country to country, this is clearly not an isolated phenomenon.	Summing up
However, the situation is noticeably different in Asia.	Transitioning to next topic
The relationship between the concluding sentence and the topic sentence should always be clear. If you cannot trace the development of the theme between them, you may have digressed from the original purpose of the paragraph.
Signpost to guide your readers


What is signposting?
Just as road signs show you where you are going on a street or highway, you can use certain words or phrases to create 'signposts' to guide your reader through your writing. Signposts show your reader the route your writing will take, remind them of key points along the way, and point out changes in direction.

Signposts also help the reader understand the connections between the points you make, and how they contribute to the overall aim of the assignment.

Tips for incorporating signposting in your writing
Use signposting to show the reader the connections and relationships between the ideas you present.
Use signposting throughout your writing so that you and the reader stay on track and can easily follow your work.
Use a range of different signposting strategies: simple words and phrases, sub-headings, transition sentences, link words and reminders.
Use signposting carefully - avoid overuse of signposting as this can interrupt the flow of your writing.

Most readers find the second text easier to read because signposts make the connections between the ideas presented. For example, ‘Therefore’ shows the relationship between the points made in the first and second sentences, and ‘first’ and ‘subsequently’ tell us that the following sentences explain how the essay will develop. However, it is not enough to simply outline the intended structure in the introduction – you need signposts throughout to remind the reader where they have come from and tell them where you plan to take them next.

Signposts can also tell us how to interpret information presented in the assignment. For example, ‘Notably’ at the beginning of the second paragraph above tells us that the writer considers the content of the sentence to be significant.

Signposts can be simple words or phrases (e.g. however, in summary), or complete sentences (e.g. to explain the transition from one section of your writing to another).

 Note
A word of caution. Overusing signposts can affect your writing negatively. You are not expected to start each and every sentence in a paragraph with a signpost. Rather, you should use them only when you think they add value to your text and make it easy to follow for the reader.


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Types of signposts
There are four different types of signposts:

major signposts like “In this section, I provide an overview of …”
transition sentences and sub-headings to signify the overall structure of your writing
linking words such as “because”, “therefore” and “however” will help the coherence of your writing
reminders to keep your reader focussed on your message whereby you use phrases like “Previously we reviewed …” and “To recap …”.
Signposting contributes to the overall coherence and cohesion of your writing.

major signposts, transition sentences and sub-headings contribute to the coherence of your writing, which refers to the overall organisation and flow
linking words contribute to cohesion, which refers to the internal organisation of your writing - that is, the internal flow of paragraphs and whether the sentences (ideas) are linked.
Let’s take a closer look at the four types of signposts that help the reader make sense of a piece of writing in a variety of ways.

Major signposts
Major signposts are used to indicate important elements of your writing, such as your purpose, your position, your main points, and your conclusions.

Examples:

This paper argues that…
This essay critically examines…
I will focus on...
This thesis begins by… It goes on to… Finally...
In conclusion,…
Transition sentences
Transition sentences are used to explain how and why you are moving from one idea to another.

Examples:

Having examined… it is now necessary to…
It is clear that... I will therefore now consider…
In the above discussion… It is also important, however, to examine…
One of the most significant factors in… is...
Another key implication of… is...
Linking words
Linking words are used to identify the connections between ideas. They tell the reader what to expect next, or how to interpret what they read. You can use them to connect ideas within sentences, between sentences or between paragraphs.

Types of linking words	Examples
Addition

Also,... In addition,... Furthermore,… Moreover,...
Cause and effect

As a result,… Consequently,… Due to,... Because of this,...

The effect of this is…

Comparison and contrast

Similarly,… Likewise,... In the same way,... Correspondingly,...

On one hand,...; on the other hand,… However,... In contrast,...

Alternatively,...

Examples

For instance,... To illustrate this,... As a case in point,...

We can see this in the case of...

Exception and qualification

However,... Nevertheless,... Although... Despite this,...

While,... Whereas….

Time and sequence

First,… Initially,... To begin,... After that,... Subsequently,...

Meanwhile,... At the same time,... Previously,... Before…

After… Finally,...

Reminders
Reminders help the reader remember the content that you have already covered. This is particularly useful in longer essays.

Examples:

In summary…
So far…
As we have seen above…
We saw in Section 2 that…
Summing up is another kind of reminder. This is useful to help the reader consolidate the content of one section or chapter before moving on to the next.

Examples:

In conclusion,... In other words,... In brief,... Thus,...
To begin organizing your literature review’s argument and structure, be sure you understand the connections and relationships between the sources you’ve read. Based on your reading and notes, you can look for:

Trends and patterns (in theory, method or results): do certain approaches become more or less popular over time?
Themes: what questions or concepts recur across the literature?
Debates, conflicts and contradictions: where do sources disagree?
Pivotal publications: are there any influential theories or studies that changed the direction of the field?
Gaps: what is missing from the literature? Are there weaknesses that need to be addressed?
This step will help you work out the structure of your literature review and (if applicable) show how your own research will contribute to existing knowledge.

There are various approaches to organizing the body of a literature review. Depending on the length of your literature review, you can combine several of these strategies (for example, your overall structure might be thematic, but each theme is discussed chronologically).

Chronological
The simplest approach is to trace the development of the topic over time. However, if you choose this strategy, be careful to avoid simply listing and summarizing sources in order.

Try to analyze patterns, turning points and key debates that have shaped the direction of the field. Give your interpretation of how and why certain developments occurred.

Thematic
If you have found some recurring central themes, you can organize your literature review into subsections that address different aspects of the topic.

For example, if you are reviewing literature about inequalities in migrant health outcomes, key themes might include healthcare policy, language barriers, cultural attitudes, legal status, and economic access.

Methodological
If you draw your sources from different disciplines or fields that use a variety of research methods, you might want to compare the results and conclusions that emerge from different approaches. For example:

Look at what results have emerged in qualitative versus quantitative research
Discuss how the topic has been approached by empirical versus theoretical scholarship
Divide the literature into sociological, historical, and cultural sources
Theoretical
A literature review is often the foundation for a theoretical framework. You can use it to discuss various theories, models, and definitions of key concepts.

You might argue for the relevance of a specific theoretical approach, or combine various theoretical concepts to create a framework for your research.
Like any other academic text, your literature review should have an introduction, a main body, and a conclusion. What you include in each depends on the objective of your literature review.

Introduction
The introduction should clearly establish the focus and purpose of the literature review.

Tip
If you are writing the literature review as part of your dissertation or thesis, reiterate your central problem or research question and give a brief summary of the scholarly context. You can emphasize the timeliness of the topic (“many recent studies have focused on the problem of x”) or highlight a gap in the literature (“while there has been much research on x, few researchers have taken y into consideration”).
Body
Depending on the length of your literature review, you might want to divide the body into subsections. You can use a subheading for each theme, time period, or methodological approach.

As you write, you can follow these tips:

Summarize and synthesize: give an overview of the main points of each source and combine them into a coherent whole
Analyze and interpret: don’t just paraphrase other researchers—add your own interpretations where possible, discussing the significance of findings in relation to the literature as a whole
Critically evaluate: mention the strengths and weaknesses of your sources
Write in well-structured paragraphs: use transition words and topic sentences to draw connections, comparisons and contrasts
Conclusion
In the conclusion, you should summarize the key findings you have taken from the literature and emphasize their significance.

Tip
Be sure to show how your research addresses gaps and contributes new knowledge, or discuss how you have drawn on existing theories and methods to build a framework for your research.
<lit-review>
Accelerating Explainable AI Adoption in Healthcare: A Comprehensive Review of Challenges, Opportunities, and Multidisciplinary Approaches
Introduction
Artificial Intelligence (AI) holds immense potential to revolutionise patient care across various medical domains. Despite significant technological advancements, the adoption and integration of AI in healthcare lags behind technical proficiencies (Davenport & Kalakota, 2019; Topol, 2019). Bridging this gap is critical, as AI systems can enhance diagnostic accuracy, optimise treatment plans, and improve patient monitoring. This paper provides a comprehensive literature review on the current state of AI adoption in healthcare, focusing on ethical considerations, trust, transparency, and regulatory compliance. By synthesising a wide array of scholarly sources, we aim to support practitioners in responsibly and effectively implementing AI technologies, laying the groundwork for developing a Human-Centred Artificial Intelligence (HCAI) framework.
The Promise of AI in Healthcare
The Importance of High-Quality Data
High-quality data is the cornerstone of effective AI systems in healthcare. Access to large, diverse, and accurate datasets enables AI models to learn complex patterns and make precise predictions (Esteva et al., 2019; Obermeyer & Emanuel, 2016). In medical imaging, for instance, AI algorithms trained on extensive datasets have achieved diagnostic accuracies comparable to human experts (McKinney et al., 2020). High-quality data enhances predictive accuracy, reduces bias, and improves the diagnosticity of data gathered; the ability to extract intrinsic information from data (Ghasemaghaei & Calic, 2019).

Big Data and ICU settings
Big Data, referring to real-time data of volume, veracity and variety, increasingly attracts attention from industry and academia (Nilashi et al., 2023). This data, when handled correctly, proposes significant opportunities and capabilities for AI systems like in Smart Manufacturing and financial sectors (Javaid et al., 2021; Soldatos & Kyriazis, 2022). ICUs generate vast amounts of real-time data, making them ideal environments for deploying AI systems (Shillan et al., 2019). Moreover, data QUALITY collected in these settings AI can assist in early detection of patient deterioration, optimise resource allocation, and support clinical decision-making (Lee et al., 2020). For example, machine learning models have been used to predict sepsis onset hours before clinical recognition, allowing for timely interventions (Desautels et al., 2016). The controlled environment of ICUs, where patients are continuously monitored, provides high-fidelity data that enhances AI model performance (Gutierrez, 2020).
Focus on Neonatal Care
Justification for AI in Neonatal Intensive Care Units (NICUs)
Neonatal care presents a promising area for AI implementation due to several factors:
Lower Diversity of Illnesses: Neonates often present with a narrower range of conditions compared to adult populations, simplifying the complexity of AI models needed for diagnosis and treatment (Rios et al., 2020).
Lack of Non-Compliance Issues: Unlike adults, neonates do not exhibit behavioural non-compliance, reducing variability in treatment outcomes and improving data consistency (Blake et al., 2018).
Controlled Environmental Conditions: NICUs offer highly controlled settings, minimising external variables and enhancing the reliability of collected data (Fairchild et al., 2017).
High-Quality Data Availability: Continuous monitoring systems in NICUs provide rich datasets essential for training robust AI models (Stone et al., 2020).
These factors collectively make neonatal care an optimal field for deploying AI technologies aimed at improving patient outcomes.
Contrast with Pediatric Intensive Care Units (PICUs)
While PICUs also offer opportunities for AI applications, they deal with a broader spectrum of diseases and patient behaviours, introducing more complexity into AI model development (Herzenberg et al., 2021). The diversity in age, weight, and developmental stages in paediatric patients adds layers of variability not as prominent in neonatal populations (Keane et al., 2018). Consequently, the majority of AI research and system development is concentrated within NICU settings, potentially confirming the suitability of neonatal care for pioneering AI integration (Schouten et al., 2024).
Advancements in Neonatal and Pediatric Intensive Care
Recent studies have highlighted promising applications of AI in NICUs and PICUs. Adegboro et al. (2021) identified 31 academic sources demonstrating AI's ability to improve health outcomes in these settings. Most of these technologies were in prototype testing phases, emphasising operational constraints, regulatory standards, and ethical dilemmas as priorities for safe implementation.
Schouten et al. (2024) expanded on these findings, claiming that the technologies had been underestimated in terms of Technology Readiness Levels, suggesting that some are closer to clinical implementation than previously thought. The authors highlighted the added complexity and time required to integrate transparency and explainability into AI systems. The need for continual model evaluation and associated financial costs serve as barriers to the maturation of these technologies.
Predictive Analytics and Early Intervention
Advancements from monitoring vital signs to predicting and proactively treating them are evidenced through AI and machine learning techniques that can accurately forecast deterioration in vital signs (Vistisen et al., 2019). Early warning systems trained on historical data replicate the assessments of bedside clinicians by analysing current and historical trends (Komorowski et al., 2018; Morales-Nebreda, 2020). Despite showing potential for improving prognostics, without explicitly addressing causal mechanisms, these models have had limited clinical applicability thus far (Lal et al., 2020).
Automation and Control Systems
Gutierrez (2020) examined the automation of oxygen control systems in preterm infants, noting a lack of AI systems for incubator automation. While rule-based automation has been efficacious, it highlights the need for intelligent systems capable of adapting to sensor insensitivity and environmental changes. Zhang et al. (2023) revealed the effectiveness of a physics-informed AI model in monitoring NICUs. By combining AI with computational modelling data, the approach resulted in greater accuracy and consistency of air temperature control, demonstrating AI's ability to process and leverage large datasets efficiently.
Clinical Decision Support Systems
AI-driven Clinical Decision Support Systems (CDSS) have shown promise in enhancing diagnostic accuracy and treatment plans in neonatal care (Choi et al., 2020). These systems utilise algorithms to analyse patient data, providing clinicians with evidence-based recommendations. The integration of AI into CDSS can improve neonatal outcomes by facilitating timely and precise interventions (Plaza, 2024).
Barriers to AI Adoption in Healthcare
Ethical Considerations and Trust
Implementing AI in healthcare raises significant ethical concerns related to patient welfare, data privacy, and the integrity of data analysis (Morley et al., 2020). Trust in AI systems is critical for sustained adoption, particularly in high-stakes environments like healthcare (Castelo et al., 2019). Human-AI interaction and clinicians' trust in AI are pivotal factors influencing implementation success (Shortliffe & Sepúlveda, 2018).
Regulatory Compliance and Legal Challenges
Compliance with data regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA) is essential (Voigt & Bussche, 2017). Legal accountability for AI decisions remains a grey area, complicating the deployment of AI systems in clinical settings (Yu et al., 2018). The EU AI Act further emphasises the need for transparency, robustness, and accountability in AI applications (European Commission, 2021).
Technical and Operational Constraints
Integrating transparency and explainability into AI systems adds complexity and extends development timelines (Arrieta et al., 2020). Continual model evaluation and the financial costs associated with deploying and maintaining AI technologies serve as barriers to their maturation (He et al., 2019). Moreover, the need for interoperability and universal standards to promote scalability is critical (Kumar et al., 2021).
Ethical AI and Human-Centred Design
Limitations of Current Ethical Approaches
While ethical AI is deemed a priority, the normative approach and multitude of global initiatives often lack actionable strategies (Article 19, 2019). Critics argue that the current discourse serves as a "softer version of the law," enabling organisations to avoid legally enforceable restrictions (Jobin et al., 2019; Ochigame, 2019). "Ethics washing" refers to superficial engagement in ethical discussions without substantive action (Rességuier & Rodrigues, 2020).
Frameworks for Ethical Compliance
Treacy (2023) proposes a comprehensive framework for ethically compliant AI programs, identifying six key areas:
Transparency
Accountability
Diversity
Data Governance
Safety
Organisational Culture
This framework provides organisations with a structured approach to ethical management as AI becomes more prevalent. Treacy (2022) further contributes to data compliance strategy development by applying a knowledge-based dynamic capabilities framework, outlining mechanisms required at each stage of strategy formulation and implementation.
Human-Centred Artificial Intelligence (HCAI)
Developing AI systems with a human-centred approach ensures that technology aligns with user needs and ethical considerations (Shneiderman, 2020). HCAI emphasises collaboration between humans and AI, enhancing trust and facilitating adoption (Abdul et al., 2018). In healthcare, HCAI can improve clinician engagement with AI systems by making them more transparent and interpretable (Doshi-Velez & Kim, 2017).
Management Information Systems and AI Integration
Defining AI Systems in Organisational Contexts
AI systems are capable of performing tasks that require human intelligence, such as reasoning, discovering meaning, generalising, or learning from past experiences (Russell & Norvig, 2021). They differ from traditional IT capabilities by operating through agentic interfaces and possessing varying degrees of autonomy (Baird & Maruping, 2021).
AI Adoption Trends in Business
AI adoption has accelerated across industries, with 72% of companies reporting AI implementation in 2023, up from 65% the previous year (McKinsey Global Survey, 2023). Employee engagement with AI technologies has also increased, indicating broader organisational integration. However, companies recognize associated risks, necessitating robust risk management strategies (Radanliev & De Roure, 2023).
Challenges in Healthcare AI Systems
In healthcare, the implementation of AI systems raises concerns about privacy, accuracy, and transparency (Oleck et al., 2023; Bekbolatova et al., 2024). For example, the use of Large Language Models (LLMs) or AI chatbots can improve patient care but also introduce risks related to data security and misinformation (Naylor et al., 2023). In surgical domains, AI offers advantages over traditional Decision Support Systems by providing real-time support and reducing cognitive load (Pressman et al., 2024).
Explainable AI (XAI) in Finance and Healthcare
Importance of Explainability
The 'black-box' nature of AI poses challenges in domains where understanding decision processes is critical (Rudin, 2019). Explainable AI (XAI) seeks to make AI decisions transparent, enhancing trust and facilitating adoption (Arrieta et al., 2020).
Applications in Finance
The finance industry, while receptive to AI innovations, faces adoption hurdles due to opacity in AI decision-making (Weber et al., 2024). XAI can help overcome these barriers by providing insights into AI models, thus supporting practitioners' implementation of AI in financial business practices (Venkataramanan et al., 2024).
Applications in Healthcare
In healthcare, integrating explainability into AI systems is essential for clinician trust and patient safety (Tonekaboni et al., 2019). Hu et al. (2024) developed an explainable AI model for mortality risk prediction in ICUs, demonstrating that explainability enhances clinicians' trust and facilitates AI adoption. Ferrara et al. (2024) emphasised the need for robust frameworks to manage AI-related risks in healthcare settings.
Recommendations for Future Research and Implementation
To successfully implement ethical AI applications in healthcare, developers and organisations should:
Enhance Human-AI Interaction: Promote multidisciplinary approaches involving clinicians, AI experts, and ethicists to address trust and transparency issues (Udegbe et al., 2024).
Address Ethical and Legal Concerns: Ensure compliance with data regulations (e.g., GDPR, HIPAA), establish accountability frameworks, and promote interoperability through universal standards (Morley et al., 2020; European Commission, 2021).
Focus on Continuous Evaluation and Scalability: Implement ongoing model evaluation, consider financial costs, and plan for scalability to overcome barriers in technology maturation (Schouten et al., 2024; He et al., 2019).
Incorporate Explainability: Integrate XAI methods to enhance transparency, improving clinician trust and adoption rates (Arrieta et al., 2020; Hu et al., 2024).
Develop Actionable Ethical Frameworks: Move beyond ethical posturing by adopting concrete frameworks for ethical AI development and deployment (Treacy, 2023).
Conclusion
AI has the potential to significantly improve patient care outcomes, particularly in neonatal care where conditions are optimal for AI implementation. Despite this promise, adoption is hindered by ethical dilemmas, trust issues, and regulatory challenges. A concerted effort involving multidisciplinary research, adherence to ethical and regulatory standards, and the incorporation of human-centred design principles is required. By addressing these challenges, we can pave the way for responsible and effective AI integration in healthcare, ultimately enhancing patient outcomes and advancing medical practice.

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</lit-review>
<proposal>
Aims and Objectives
Technological innovations have improved diagnosis, mother and neonatal mortality, and quality of life, fueled by interdisciplinary research (Harte, 2024). Artificial Intelligence (AI) models offer potential for neonatal and maternal outcomes (Bowe et al., 2023; Sanchis et al., 2024), however integration of these systems into daily clinical use lags behind their technical proficiency (Davenport and Kalakota, 2019).
The absence of universal frameworks and standardised approaches for addressing these challenges creates significant barriers for AI models to progress from effective prototypes to regulatory compliance and clinical use (Schouten et al., 2024). To address this gap, this proposal aims to develop a comprehensive Human-Centric explainable AI (HCXAI) framework for neonatal care, prioritising user-needs, responding to the international need for AI research in healthcare and aligning with the EU AI Act's requirements for transparency, interpretability, and trustworthiness.

The objectives of this research are:
It facilitates human-centred AI adoption in neonatal care, enhancing effectiveness, safety, and maternal-neonatal health (INFANT, 2024). Additionally, the framework evaluates factors affecting AI adoption in neonatal care to inform future design and strategies. It serves to inform implementation, brining AI capabilities closer to clinical practice, establishing trust and usability among users, ultimately reducing negative birth experiences among neonates and mothers, respectively, by leveraging AI to supplement, not replace, clinical decision-making within an HCXAI framework that prioritises stakeholder engagement, explainability,  centralised deployment, and ethical considerations (Schouten et al., 2024).

User Design Brief; What are the barriers and facilitators of HCXAI adoption in neonatal care?
Build a HCAI prototype; What is the design specification required for HCAIs to incorporate trust, transparency and interpretability as mandated by the EU AI Act?
Evaluate the HCAI prototype; How will the evaluation of the HCAI prototype measure its effectiveness in real clinical settings, particularly in terms of improving neonatal care outcomes?
Literature Review 
Health Informatics has transformed healthcare by facilitating the collection, storage, and analysis of vast amounts of patient data. Intensive Care Units (ICUs) present an optimal opportunity for generating high-quality, rich, multidimensional datasets through continuous monitoring and frequent interventions, which are invaluable strengths for AI applications in supporting diagnostics and treatment decisions (Beam et al., 2024; Adegboro et al., 2021; Suresh et al., 2024). However, despite the development of numerous AI models for neonatal and paediatric ICU settings, as evidenced by over 260 studies in a recent systematic review, few progress beyond the prototyping phase, limiting their translation into real-world clinical practice and potential to improve outcomes (Schouten et al., 2024).
To address these implementation challenges, researchers have developed Ethical AI frameworks and Fair, Accountable, and Transparent (FAT) systems. While these approaches show promise, studies indicate their direct impact remains limited (Rességuier & Rodrigues, 2020; Article 19, 2019). In parallel, Explanatory AI (XAI) has gained prominence in recent years (Saeed & Omlin, 2023).
Facilitating FAT and ethical AI imperatives, XAI systems play a key role in critical healthcare fields such as neonatal care, where Explainable Interfaces (EIs) communicate transparency about outputs, promoting trust between providers and AI systems and compliance 
with evolving regulations, like the EU AI Act (Dixit, 2023; Panigutti et al., 2023).

Despite major breakthroughs in technically-driven XAI solutions, they often lack end-user interpretability (Nguyen et al., 2024). Recognizing the inherent humanity in the concept of explainability, Human-Centred Explainable AI (HCXAI) has gained increasing attention. HCXAI combines XAI's interpretability with Human-Centred AI (HCAI) user-focused design principles, demonstrating that it can effectively lead to the integration of production interfaces in clinical settings (Ehsan et al., 2022; Thieme et al., 2024).
.
However, thus far, HCXAI in neonatal care research is underapplied. This forms a significant gap in current research; there’s a scarcity of framework development that incorporates AI effectively with human-centred design principles in neonatology. The success of automotive and financial sectors use of XAI systems (Benamira, 2024), the healthcare sector is slow to transfer these advancements effectively (Goldfarb & Teodoridis, 2022), leading to challenges in transparency and interpretability, as a result of complex deep learning networks that offer greater prediction accuracy at the cost of interpretability (Zahoor et al., 2023).
In that light and as AI continues to lead to improved-mortality rates, a crucial pivot in securing sustainability within this sector may not be possible without addressing pertinent concerns of potential harm within system-design through a human-centred approach. Hence, developing a solid framework to facilitate effective implementation of the principles of Human centred design in explainable AI implementations. That is HCXAI, exemplifies a proactive strategy to maximise the uptake and implementation of future XAI applications, while promoting compliance and trustworthiness to secure future opportunities for improved neonatal clinical outcomes. 


Methodology 
To understand the nuanced perspectives of end-users, the study will undertake semi-structured interviews with neonatologists, paediatricians and neonatal nurses to investigate the opinions of healthcare professionals surrounding the use of AI, including barriers, and facilitators.
Employing Interpretive Phenomenological Analysis (IPA) allows for deep exploration of experience-based insight from stakeholder groups and will play a crucial role in ensuring the  HCAI framework design is authentically human-centric; where human-user needs centre in system-design. 
Inline with HCI methods, data collected from a User Study will facilitate a data-driven evaluation and contribute to a comprehensive analysis of the efficacy of designed artefacts as perceived by potential users, as well as facilitating informed design of future iterations.  
 
HCXAI Framework Explainable Interface Design 
A critical aspect of our HCXAI framework will be the development of EIs that will allow AI in neonatal care to practically be operational. Core requirements of this interface will be to maximise transparency and build trust with healthcare professionals by making the interaction with AI models as intuitive as possible. Tools used will include:
Interactive Visualisations: Users will be able to understand how different
inputs influence AI predictions interactively, granting greater insight to reliability and responsible context-driven application of predictions. Customizability facilitated through this interaction will support multiple use-cases with flexibility for prioritising specific information. 
Explanation Features: The user interface will use methods like SHAP and LIME to explain, in plain language, the reason for every prediction. These methods prove advantageous in implementing interpretability and meaningfully contribute to responsible application in specific use-cases, where novel measures like QII and Trust Score will facilitate evaluation of design (Sharma et al., 2024).
These features will be designed and informed by stakeholder groups and contribute toward transparency in AI operations, to foster confidence in clinicians for making responsible AI-supported decisions, thereby supplementing their decision-making and effective care-potential amongst neonates.

Anticipated Outcomes and Significance
In building a comprehensive neonatal HCAI roadmap, this work provides guidelines on how to build glass-box AI models. 
The main expected outcomes from our study are:
Improved accuracy, transparency, and explainability of AI models with development of validation through innovative metrics.
Better understanding of healthcare providers' perceptions surrounding AI should lead to the ameliorated development and adoption of AI in neonatal care. Accordingly, our HCAI framework will be in alignment with the EU AI Act for legal and ethical compliance regarding the latest regulatory standards. 
In contributing to reduced impairment and negative birth experiences, these contributions are anticipated to significantly improve maternal and neonatal health-outcomes.
The HCXAI framework could serve as a model for the ethical and responsible development of AI in other healthcare applications.  


Conclusion
The current proposal seeks to address the international requirement for research in AI healthcare innovations, addressing implementation challenges in AI-integrated neonatal care through amolorated user engagement and transparency. By setting solid ground for AI adoption, with proven benefits to clinical settings, this research contributes to informed strategy around the current and future challenges of AI in healthcare. 
This builds on HCXAI presence within healthcare by aligning technological advancements designed in such a way to support trustworthy AI improved neonatal outcomes and care.




Word count: 1,102  (Excluding headings)

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</references>
</proposal>

<high-level-feedback>
High level feedback on the proposal: 
Distinguish between research objectives, and research questions. Pick a one liner objective, and unpack that then with three research questions. I would suggest that barriers to adoption, and indeed facilitators have been looked at thoroughly in similar contexts so I am unsure what level of unique barriers that might emerge from a study such as this. 
Methodology pretty light - no sense of how you are looking to conduct the data analysis, or thematic analysis. If you are looking to develop a prototype, have you considered the design science approach? It might be a nice way to build iterative prototypes while also gathering empirical evidence as to how it can be evaluated. In which case, then perhaps focus groups might be a good idea rather than interviews? 
Anticipated outcomes looks pretty weak. This is the justification of what you setting out to do. If you develop this tool for practitioners, what are the impacts? What is its importance? What will it offer? You can layer that then with the academic insights this study will deliver also, and what body of knowledge you are adding to a fledgling area. 
Research gap needs work - The foundation for the study comes from the research gap. How do you know this study is needed? Obviously this comes from reading as many papers as possible, and it won't jump out after just a page of literature, but you really need to emphasize that this research direction is being demanded by as many sources as possible to showcase it's importance. 
After an initial literature exploration, a nice exercise might be to think about structure for the literature. What sections are needed to tell the story you are looking to tell? 
You are treating AI as a pretty high level artefact. You need to provide examples of the specific types of technologies you are looking at, how they are used, case examples, their role in healthcare, etc... Get deeper with an appreciation to their worth.  
</high-level-feedback>

<concepts>
From Bytes to bedside (Schouten et al., 2024)

This paper comes from the Intensive Care Medicine Journal and examines the readiness of AI in PICU and NICU settings. It starts with detailing the transformative potential of AI in medicine, particularly intensive care noting the rising volume of research on the topic, particularly in Intensive care units. This concept is not something new, health information systems is an established field and Intensive Care Units generate high-quality data which is ideal for leveraging AI models. 

It then introduces the problem with these models, that they do not progress beyond prototyping and have high bias. This concept is discussed in an earlier study where an evaluation of AI’s impact in NICU and PICU settings, showed that most models had limited clinical-impact (Adegboro et al., 2021). 
A research gap is then identified as the lack of research on neonatal and paediatric patient focused AI, and the research focus on readiness and risk of bias. Bias has arisen as a significant challenge with AI in healthcare and contributes to ethical issues also (Kerasidou, 2021). 
The concept of readiness is evaluated through the Technology Readiness Level, providing a framework for evaluating new technology readiness broken into nine levels and widely used in research like the EU Horizon project (Olechowski et al., 2020; Arnouts et al., 2022). The paper uses the Prediction model Risk Of Bias ASsessment Tool (PROBAST) framework to assess risk of bias. This framework was developed specifically for this purpose and used in systematic reviews for prediction models in healthcares (Moons et al., 2019). Authors did note the existence but unavailability of the PROBAST-AI framework, specifically designed for AI models in research. This framework along with Transparent Reporting of a multivariable prediction model for Individual Prognosis AI (TRIPOD-AI) have been recently studied and refined for their application in other healthcare domains (Hurkmans et al., 2024).
The results of the systematic review highlight risk of Bias and Technology Readiness as key barriers. Despite over 260 studies being evidenced in the review, the progression of the readiness of these technologies was not clearly evident. The prevalence of low-readiness and high-bias in studies evaluated work in opposition to the implementation and is evidenced by no studies reaching clinical readiness. Research also explores the concept of AI readiness from an organisational perspective, highlighting the lack of related training among healthcare professionals and the need for readiness in adoption (AlZaabi et al., 2023; Tezpal et al., 2024). From a change management perspective, awareness, cross-functional teams and resistance management, are also considered essential adaptations to new technology in clinical healthcare (Larsson & Thesing, 2024). Similarly, maturity models are widely used frameworks for evaluating specific organisational Information Systems capability (Gomes & Romão, 2018), and . Moreover external validation, real-time data, novel infrastructure and user-friendliness were seen as essentials in practice but were not well represented in literature. The prevalence of these models is reflected in other research focusing directly on neonatal care and shares similar conclusions regarding the need for more clinical trials, validation and addressing of ethical issues (Rallis et al., 2024).
The concept of explainability is discussed as an essential part of ethical AI, Trustworthy AI and Implementation. Explainability has been growing in importance especially in the field of AI for compliance with regulation and is recognised as a gold standard for trust and adoption in critical domains (Dwivedi, 2023). Trustworthy AI enables the adoption of these concepts as well as other ethical and technical requirements, like privacy, validation and transparency (Li et al., 2023). The EU AI act upholds Trustworth AI with While the importance of explainability is widely accepted in promoting transparency and trust, the usability of these systems has also emerged as a weak point, yet an essential concept in facilitating clinical adoption of these systems (Vermeire, 2021). 
The Translational Evaluation of Healthcare AI was developed and includes three components of capability, utility and adoption to evaluate translational value of AI in healthcare and has been applied to identify gap in the real-world translational value of AI technologies, like for evaluating COVID-19-AI (Reddy et al., 2021; Casey et al., 2023). 

<references>
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</references>
</concepts>