Overview of Deliverables and Expected Outputs:
Comprehensive Literature Review:

Length: A 5000+ word literature review, exploring the nuances of Human-Centered Explainable AI (HCXAI) in relation to Human-Centered AI (HCAI).
Depth: The review must be extremely detailed, presenting a thorough analysis of the evolution from HCAI to HCXAI. Each section should explore its topic deeply, avoiding shallow explanations.
Triangulated Sources: Use a broad range of academic sources to ensure the literature review is well-supported. Cross-reference multiple studies and ensure that insights are drawn from diverse perspectives.
Integration of Provided Documents:

"<lit-review>": Update and adapt this document's HCAI focus to fit the evolution towards HCXAI.
"<proposal>": Integrate the latest insights and updated literature from this proposal into the review, ensuring that the most recent research is reflected.
"<concepts>": Incorporate the key ideas from the concepts article, showing how these concepts inform your HCXAI discussion.
"<high-level-feedback>": Apply all the supervisor’s feedback, making improvements based on the critique provided.
High Academic Standards:

Scholarly Tone: Maintain a confident, formal academic tone throughout the literature review. All sections must be precise, well-structured, and logically organized.
Accurate Referencing: Use correctly formatted citations and references throughout. All sources must be from reputable academic databases such as Google Scholar, ensuring the literature is up-to-date and accurate.
Comprehensive Reference List: Provide a complete reference list at the end, properly formatted according to academic citation standards (e.g., APA, MLA, Harvard).
It is important to note that the lit-review, proposal and concepts documents each contain "<reference>" tags at the end which contain a full version anotation of each of the inline references used in the article 
Detailed Analysis and Synthesis:

Conceptual Depth: Every section should explore topics with conceptual depth, linking the key ideas of HCAI and HCXAI.
Contemporary Examples: Where possible, use contemporary events or studies to validate points. Phrases like "For example" or "For instance" should introduce concrete examples to support theoretical arguments.
Feedback Application: Ensure the review reflects the high-level feedback provided by the supervisor, which is crucial for improving the structure and depth of the review.
Focus on Length and Quality:

The review must be lengthy but also focused on quality. Avoid filler content—each word should contribute meaningfully to the overall narrative.
Balanced Sections: Ensure each section is well-balanced in length and depth, offering critical evaluations of the literature rather than descriptive summaries.


Recap of Deliverables:
5000+ word literature review.
In-depth integration of the documents ("<lit-review>", "<proposal>", "<concepts>", "<review>").
Use of supervisor feedback from "<high-level-feedback>".
Dense academic references and accurate citations.
Confident academic tone and adherence to formal writing standards.


<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.

<references>
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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>
Olechowski, A. L., Eppinger, S. D., Joglekar, N., & Tomaschek, K. (2020). Technology readiness levels: Shortcomings and improvement opportunities. Systems Engineering, 23(4), 395-408.
Rallis, D., Baltogianni, M., Kapetaniou, K., & Giapros, V. (2024). Current Applications of Artificial Intelligence in the Neonatal Intensive Care Unit. BioMedInformatics, 4(2), 1225-1248.
Vermeire, T., Laugel, T., Renard, X., Martens, D., & Detyniecki, M. (2021, September). How to choose an explainability method? Towards a methodical implementation of XAI in practice. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 521-533). Cham: Springer International Publishing.
Dwivedi, R., Dave, D., Naik, H., Singhal, S., Omer, R., Patel, P., ... & Ranjan, R. (2023). Explainable AI (XAI): Core ideas, techniques, and solutions. ACM Computing Surveys, 55(9), 1-33
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., ... & Zhou, B. (2023). Trustworthy AI: From principles to practices. ACM Computing Surveys, 55(9), 1-46..
Tezpal, M., Ghosh, S., Lalwani, R., Yadav, J. and Yadav, A.K., 2024. Artificial Intelligence in Health Care–A Study on Perceptions of and Readiness for Artificial Intelligence in Health-care Professionals. Journal of Marine Medical Society, pp.10-4103.
Arnouts, S., Brown, S., De Arriba, M.L., Donabedian, M. and Charlier, J., 2022. Technology Readiness Levels for vaccine and drug development in animal health: From discovery to life cycle management. Frontiers in Veterinary Science, 9, p.1016959.
AlZaabi, A., AlMaskari, S., & AalAbdulsalam, A. (2023). Are physicians and medical students ready for artificial intelligence applications in healthcare?. Digital Health, 9, 20552076231152167.
Larsson, E., & Thesing, M. (2024). Change Management Strategies for Seamless Adoption of Digital Healthcare Solutions in the Healthcare Industry.
Gomes, J., & Romão, M. (2018). Information system maturity models in healthcare. Journal of medical systems, 42(12), 235.
Akbar, M. A., Leiva, V., Rafi, S., Qadri, S. F., Mahmood, S., & Alsanad, A. (2022). Towards roadmap to implement blockchain in healthcare systems based on a maturity model. Journal of Software: Evolution and Process, 34(12), e2500.
Reddy, S., Rogers, W., Makinen, V. P., Coiera, E., Brown, P., Wenzel, M., ... & Kelly, B. (2021). Evaluation framework to guide implementation of AI systems into healthcare settings. BMJ health & care informatics, 28(1).
Casey, A., Ansari, S., Nakisa, B., Kelly, B., Brown, P., Cooper, P., ... & Makinen, V. P. (2023). Application of comprehensive evaluation framework to Coronavirus Disease 19 studies: A systematic review of translational aspects of artificial intelligence in health care. medRxiv, 2023-02.
Adegboro, C., Choudhury, A., Asan, O., & Kelly, M. (2021). Artificial Intelligence to Improve Health Outcomes in the NICU and PICU: A Systematic Review.. Hospital pediatrics. https://doi.org/10.1542/hpeds.2021-006094.

Kerasidou, A. (2021). Ethics of artificial intelligence in global health: Explainability, algorithmic bias and trust. Journal of Oral Biology and Craniofacial Research, 11(4), 612-614.
Moons KGM, Wolf RF, Riley RD, Whiting PF, Westwood M, Collins GS, Reitsma JB, Kleijnen J, Mallett S (2019) PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med 170:W1–W33
Hurkmans, C., Bibault, J. E., Clementel, E., Dhont, J., van Elmpt, W., Kantidakis, G., & Andratschke, N. (2024). Assessment of bias in scoring of AI-based radiotherapy segmentation and planning studies using modified TRIPOD and PROBAST guidelines as an example. Radiotherapy and Oncology, 194, 110196.
</references>
</concepts>