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Reis et al

Viewpoint

Practical Applications of Large Language Models for HealthCare Professionals and Scientists
Florian Reis1, MD; Christian Lenz1, MSc, MD, PhD; Manfred Gossen2,3, PhD; Hans-Dieter Volk4,5, MD, PhD;
Norman Michael Drzeniek4,5, MD, PhD
1

Medical Affairs, Pfizer Pharma GmbH, Berlin, Germany
Institute of Active Polymers, Helmholtz-Zentrum Hereon, Teltow, Germany
3
Berlin-Brandenburg Center for Regenerative Therapies (BCRT), Berlin, Germany
4
Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu
Berlin, Berlin, Germany
5
BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité, Universitätsmedizin Berlin, Berlin, Germany
2

Corresponding Author:
Norman Michael Drzeniek, MD, PhD
BIH Center for Regenerative Therapies (BCRT), Berlin Institute of Health at Charité
Universitätsmedizin Berlin
Augustenburger Platz 1
Berlin, 13353
Germany
Phone: 49 30450524334
Email: norman.drzeniek@charite.de

Abstract
With the popularization of large language models (LLMs), strategies for their effective and safe usage in health care andresearch have become increasingly pertinent. Despite the growing interest and eagerness among health care professionals andscientists to exploit the potential of LLMs, initial attempts may yield suboptimal results due to a lack of user experience,thus complicating the integration of artificial intelligence (AI) tools into workplace routine. Focusing on scientists and healthcare professionals with limited LLM experience, this viewpoint article highlights and discusses 6 easy-to-implement use
cases of practical relevance. These encompass customizing translations, refining text and extracting information, generating
comprehensive overviews and specialized insights, compiling ideas into cohesive narratives, crafting personalized educational
materials, and facilitating intellectual sparring. Additionally, we discuss general prompting strategies and precautions for the
implementation of AI tools in biomedicine. Despite various hurdles and challenges, the integration of LLMs into daily routinesof physicians and researchers promises heightened workplace productivity and efficiency.
JMIR Med Inform 2024;12:e58478; doi: 10.2196/58478
Keywords: artificial intelligence; healthcare; chatGPT; large language model; prompting; LLM; applications; AI; scientists;
physicians; health care

Introduction
Large language models (LLMs), exemplified by OpenAI’s
ChatGPT, have garnered significant attention as text-based
artificial intelligence (AI) tools in record time [1]. While they
excel in mimicking natural language patterns, LLMs lackinherent factual accuracy and struggle with tasks requiring,for instance, mathematical reasoning at the graduate level[2]. Current research explores the association between LLMs
and external databases [3], as well as the combination of
LLMs with preexisting data and software tools as seenin ChatGPT’s incorporation within Microsoft Copilot. This
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facilitates the step toward retrieving and articulating coherent
responses using factual information from web-based sources,
which is particularly relevant in fields like health care andthe natural sciences where supplementation with externaland up-to-date data is an essential prerequisite for meeting professional standards. ChatGPT, for instance, alreadydemonstrates versatile applications in the medical domain,
spanning from the identification of research topics, aiding
in medical education, and supporting clinical and laboratory diagnosis to enhancing knowledge dissemination among
health care professionals, extracting medical knowledge, andengaging in medical consultation [4,5]. However, despite
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LLMs’ seemingly ideal suitability for enhancing personal
productivity and keeping up to date with latest research, many
professionals in our personal circle, including one of thecoauthors, report discouragement after a few initial experiences with widely available LLMs. From our experience,
criticism that dampens the enthusiasm of inexperienced users
for adopting LLMs in work routine includes (1) superficial
responses from chatbots, which read well but fail to capture the level of factual detail often expected in professional context; (2) confabulated information that is factuallywrong or has no factual basis (also called “hallucination”);(3) lack of citation and source disclosure; (4) limited control
over output format; and (5) failure to implement adaptation requests or criticism. Fortunately, for many use cases,
these limitations can be effectively overcome by precisely
instructing the LLM. These input instructions, known as“prompts,” can be formulated in natural language rather
than code, thus enabling intuitive usage. Nevertheless, there
are several recommendations on how to effectively interact
with an LLM to obtain the desired output. This viewpoint
article provides practical guidance along with an overview ofpotential LLM applications in the everyday work of healthcare professionals and scientists who have limited experience
with LLMs so far. Selected examples will illustrate why
LLMs offer added value for these applications and how they
can be used effectively. Finally, we will discuss overarchingaspects to consider when using LLMs.

General Recommendations for Using
LLMs
Before delving into specific use cases within the medical
and scientific context, let us first introduce some universally
applicable suggestions for optimizing prompts. Drawing fromour experience, prevalent recommendations can be broadly
categorized into specifying the precise task for the LLM,

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elucidating relevant contextual factors, and delineating thedesired output (Multimedia Appendix 1).
To align the outcome closely with personal expectations,
it can be advantageous to construct one or more exemplary
outcomes and append them as explicitly classified examples
to the prompt, separated by quotation marks or parentheses.
Based on our usage experience, the choice of language itself
also influences output quality; hence, formulating the prompt
in a widely used language, ideally English, is recommended,as it enables the LLM to draw from a wider database. To
refine the desired output, unambiguous language should be
used, preferably using positive rather than negative formulations. Particularly with complex tasks, the “Chain of Thought
Prompting” technique can yield improved results [6]. This
technique enhances the reasoning capabilities of LLMs bybreaking down multistep problems into a series of intermediate reasoning steps. For generating suitable prompts, it isalso feasible to integrate the LLM by providing the programwith relevant instructions and subsequently request feedback
to iteratively refine the desired prompt. Further guidanceon optimizing prompts can be found within the realm of“Prompt Engineering” and on websites of various popular
LLM providers.

Use Cases
There are numerous opportunities for the use of LLMs
in the professional environment of scientists and healthcare professionals [eg, 4,5]. The use cases presented inthis viewpoint, therefore, only provide a spotlight on thisbroad field and by no means an all-encompassing overview.
Based on personal experience, our reasons for selecting theapplications presented in the following sections are that they
are easy to implement also for individuals with limited prior
LLM experience, quickly yield satisfactory results, and aretransferable to similar application scenarios (Table 1).

Table 1. Overview of presented use cases in this article and the complexity of their corresponding prompts. For each category of prompt complexity,
at least 1 illustrative example with the original prompt and output is given in the course of this article.
1
2

Use case
Translation
Text editing

Description
Executing customizable translations
Refining text and extracting information

Prompt complexity
Low
Low

4

Idea elaboration

Integrating ideas into cohesive narratives

Medium3

5
6

Information compilation
Training and education
Personalized sparring partner

Constructing comprehensive overviews and specialized insights
Developing personalized learning schedules and crafting educational materials
Facilitating intellectual sparring and creative brainstorming

Customizing Translations
For widely spoken languages, the milestone of ensuring atranslation quality comparable to that of translation software
has been achieved, for instance, by ChatGPT [7]. In contrast
with dedicated translation tools, leveraging an LLM provides
diverse customization opportunities for vocabulary, tone, andstyle, combining general benefits of a proficient translation
software with specific strengths inherent in an LLM. A
translation prompt should encompass details on the target
language, desired degree of alignment with the original text
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MediumMediumHigh(literal vs freely translated), intended target audience (eg, PhD
vs elementary school students), and writing style (formal vsinformal). A noteworthy application involves the analysis ofscientific information, wherein an LLM not only translates
but also succinctly condenses and linguistically adjusts thecontent to the recipient while retaining its meaning. Thisfacilitates communication and understanding not only acrosslanguage barriers but also across diverse backgrounds andlevels of education, which is particularly crucial in medical
and scientific communication with laypeople.
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An example for a translation prompt could look like thefollowing: “Translate the following text given in quotation
marks into Spanish. Stick closely to the original text. Adopt aformal writing style. Make sure the text is understandable foran adult layperson without medical training.”

Editing Text and Extracting Information
One of the core strengths of LLMs lies in handling text,
such as summarizing and efficiently organizing written

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information. For this use case, the text to be summarizedor edited can be either typed or uploaded to the LLM as a
document, with the latter feature, for instance, available from
ChatGPT version 4.0 onwards. The output length, format, andstyle can be tailored by a precise prompt, as shown in Figure
1. By specifying the audience or assigning a writer persona,
the output style and depth of factual detail can be adjusted.

Figure 1. Tailored summaries of an uploaded document depending on the intended target audience. Two different prompts asking for a summaryof the same uploaded document (blue). A full-length PDF of one of the authors’ open access scientific articles was uploaded as the input [8]. The
prompt on the left specifies word count, output format, and audience for a summary to be used in a professional context. The prompt on the rightexemplifies how large language models can be used to extract key information and make the content widely accessible for any audience. Answers
given by OpenAI’s ChatGPT-4 are shown in gray.

Information Compilation: Creating
Comprehensive Overviews andSpecialized Insights
As the body of available knowledge grows both in depth andbreadth, LLMs are a helpful tool to gain specialized insights
or general overviews over topics of interest. However, when
LLMs are confronted with factual questions, hallucination canoccur, meaning that the returned information is syntacticallyand grammatically accurate but factually incorrect. Special
attention should be paid to this matter by meticulously
verifying all facts, references, sources, and links provided by
the LLM. Furthermore, while LLMs without internet accessoften explicitly mention that their “knowledge” is limited
to a specific time period, from our practical usage experience, there has rarely been an indication that the prompt’s
answer might be beyond the capabilities of the program. For
the current version (4.0) of OpenAI’s ChatGPT, which has
internet access, allowing it to execute a web search and mine
information, it has been demonstrated that the incidence of
hallucinations is significantly lower than that of the previous
version (ChatGPT-3.5) [9]. By summarizing and comparing
findings from various sources, LLMs are especially useful for
exploring a topic in which the user may not be an expert
yet. They can also aid in highlighting trends across different
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sources or extracting divergent positions on a debated topic.
However, performing a thorough web search with the help of
LLMs often requires specific instructions.
For instance, while the prompt “What are the latest
developments in quantum computing?” triggered a web
search and quoted 3 sources, the more detailed prompt,“Search the web for research on quantum computing from
2023. What are the latest developments in the field?” yielded
results based on 6 sources and a link leading to the web
search. Adding “Include your level of confidence, sources anddate of answer, and whether your answer is speculative. Make
it a markdown table at the end of your answer” returned along answer based on 5 references and included a statement
on research confidence and speculation, as well as a tabularsummary of the results and sources. Asking the AI chatbot
to “Consider at least 10 sources” returned 8 brief paragraphs
based on 12 different sources (Multimedia Appendix 2).

Elaborating Ideas: Integrating Bullet
Points Into Flowing Text
Standardized documentation tasks are a frequent requirement
in medical-scientific fields. A substantial amount of timeis dedicated to formulating individual pieces of information
into full-text narratives. By acquainting an LLM with one’s
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preferred or desired writing style, it becomes possible totransform bullet-pointed information into full text. Initially,
this involves providing the LLM with examples of target
frameworks in terms of structure, content, and style. Oncean example is established, additional information can beincorporated and processed with reference to this example. This approach thus represents the opposing process tocondensing an extensive text to fewer key points. A specificapplication in the medical field might be the generationof discharge summaries and medical reports [10]. Initially,
structural layout and linguistic style need to be defined.
Subsequently, information needed for generating a medicalsummary report can be listed in bullet points as a prompt andtransformed into full text by the LLM.
An exemplary prompt for this use case could appear as
follows:
Here are two examples (named Example 1 and 2,
each in quotation marks) demonstrating the structuraland content-based layout of an epicrisis authored byme: (“Example 1,” “Example 2”). Now, utilize thefollowing pieces of information to compose a coherent
text and align them linguistically and structurally to thetwo examples mentioned above: (pieces of informationin bullet-point style)

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For such applications, special precautions, such as
anonymizing all data that could be used to identify a person,must be strictly observed.

Training and Education: Developing
Customized Learning Schedules andTeaching Material for Students
Leveraging LLMs in the medical domain also offers
applications to training and education. Their adaptive learning
capabilities hold promise, for instance, in crafting personalized learning plans for medical professionals. LLMs can also
harness vast repositories of medical information and scientific
data to curate tailored educational materials. By analyzing
a learner’s proficiency level, preferred learning modality,
areas of interest along with requirements and demands, LLMs
can generate personalized curricula and suggest resources,
textbooks, or web-based courses. In the context of student
education, LLMs present opportunities for the development
and structuring of seminars that optimize learning outcomes.
By aggregating and analyzing diverse sources of medical andscientific information, LLMs can assist educators in designingseminars that align with specific learning objectives andcurriculum requirements [11]. As shown in Figure 2, LLMs
can aid in crafting and structuring seminar topics, designinginteractive presentations, and suggesting clinical scenarios forcase studies, following a single prompt.

Figure 2. Designing customized teaching material to facilitate medical education. An example prompt asking for a course schedule on a given topic
for a specific audience (blue). The response by ChatGPT-4 is shown in gray. The output is presented in tabular format.

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Personalized Sparring Partner:
Challenging Ideas, Supporting
Brainstorming, and Being QueriedThis chapter delineates the transformative potential of
LLMs as cognitive partners, elucidating their ability tochallenge preconceived notions, fuel imaginative thinking,foster structured brainstorming, and provide feedback on theuser’s theories or hypotheses. Thus, using LLMs as personalized assistants might help researchers overcome writer’s
block and break out of their creative rut by providing diverse

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perspectives. The iterative dialogue with an LLM facilitatesa reflective process for individuals, compelling them toarticulate their ideas cogently. This fosters a deeper understanding of the proposed concepts and enables refinement
through robust debate and structured discourse. This feature
can be used, for instance, by prompting the LLM to bequeried on a specific topic, as shown in Figure 3. It is helpful
to first introduce the setting and specify the desired output. To
help the LLM process the task, the respective prompt can be
subdivided into several shorter inputs.

Figure 3. Using ChatGPT as a personal sparring partner to simulate interactions and discussions. Exemplary prompts asking for a study plan onrheumatology (blue) and then to be tested on the contents of the proposed plan. The task is broken down into 2 prompts, resulting in a conversational
interaction. First, ChatGPT-4 replies with a detailed schedule and specific advice for 21 study sessions over the course of 1 week (gray). The firstanswer is truncated and shown in smaller font due to limited space. Next, ChatGPT-4 is asked to test the user on the proposed topics. Upon receiving
an answer, the large language model first corrects the user’s answer and then proceeds to pose the next question, thus correctly referring to thepreviously defined task. EULAR: European Alliance of Associations for Rheumatology.

Technical Hurdles and Challenges
Regarding LLMs, the generation of factually incorrect
responses or an output that may be well worded but disconnected from the input is known as a hallucinationand may discourage further use of LLMs, particularly
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among professional users who rely on a high level of output
accuracy. Consequently, using LLMs as mere fact-finding
tools not only carries substantial risks of misinformation but
also fails to fully exploit the actual strength of LLMs inhandling extensive volumes of text. Therefore, we do notrecommend using LLMs for looking up hard facts or for
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conducting rigorous literature searches. While completely
preventing hallucinations is likely unattainable, various
prompting strategies, such as the previously mentioned
chain-of-thought prompting, specifying concrete example
outputs (few-shot prompting), or uploading reference
documents (retrieval-augmented generation) offer empiricalevidence for reducing hallucinations [12] and can also be
applied by novice users. On a broader level, another technicalhurdle to achieving a meaningful impact in health careis the seamless integration of LLMs into existing systems
and workflows [13]. Resource constraints, a lack of clinical validation for these tools, and opaqueness in decisionmaking are exemplary barriers in this regard, which could
be overcome in the future when more practical application
experience has been gained and clear regulatory guidelines
and frameworks have been established.

Random Versus Deterministic
Responses
A limitation of the ChatGPT web interface is the randomnessof the responses, meaning that the model may provide aslightly different reply for each new prompt. This is particularly evident with long and complex outputs, which can

Reis et alvary greatly in length, tone, and overall quality, even when
the same prompt has been used. The use of an application
programming interface (API) offers the same GPT models
and functionalities as regular web chats but comes withexpanded features for users who wish to purchase a customized model and integrate it into their business software. The
API not only enables users to integrate the various available GPT models into their own applications and products,
but also allows for more precise customization of the LLM,
including a temperature setting. This parameter controls therandomness of the LLM’s responses, with a low temperature setting resulting in a more consistent and deterministic
output, while a higher temperature generates more varied andcreative responses. To demonstrate this, we asked GPT-4 totell a joke, as shown in Figure 4. At a temperature of 0,
the model repeated the same joke on scientists and atoms
3 times, varying its response by only 1 word. With increasing temperature, the program responded less often with its
usual atom joke. A low temperature setting may be usefulto produce replicable outputs of similar length, style, or
structure. A high temperature setting would potentially be
useful, when similar prompts are used for repetitive tasks,
but a varying output is required, such as when composing
invitation emails for recurring professional events without
reusing identical content every time.

Figure 4. Setting the output temperature via the application programming interface (API) to generate deterministic results. An identical prompt wasentered into the OpenAI API 3 times using the GPT-4 model (chat was cleared between prompts). When the temperature (Temp) was set to 0,
the output showed only minimal variation as the model behaved deterministically. At higher values of Temp=1 or Temp=2, the output variety also
increased.

Responsible Use of LLMs
The transformative potential of using and integrating LLMs
into clinical workflows and health care systems must be
approached with caution and guided by rigorous ethicalprinciples and regulatory oversight. One of the foremost
challenges lies in patient privacy and data security. As
LLMs rely on vast amounts of patient data for training,
there are major concerns regarding privacy breaches andunauthorized access [13]. Robust data anonymization, securestorage, and adherence to privacy regulations are essential.
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an ethical perspective, LLMs may inadvertently perpetuatebiases present in their training data, leading to unequal
treatment recommendations [14]. Rigorous bias detection
and model fine-tuning are necessary, while legal frameworks should ensure algorithmic fairness and accountability. Lastly, transparency of decision-making processes is
crucial, as clinicians need insights into how LLMs arriveat their recommendations [15]. All these limitations should
be considered when LLMs are used by health care professionals or scientists. Striking a balance between technological advancement and human safety, multiple governments,
companies, and institutions have established guidelines forresponsible LLM use. The World Health Organization,
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for instance, emphasizes the significance of adhering to
ethical principles when using LLMs in health by outlining
6 core principles: protecting autonomy, promoting humanwell-being, ensuring transparency, fostering responsibility,
ensuring inclusiveness, and promoting sustainable AI [16].

Conclusion
LLMs are potent tools, providing immediate and user-friendly
access to substantial computational capabilities for a broad
range of individuals, while bypassing the necessity for
programming skills. Despite certain technical, legal, andethical challenges, the integration of LLMs into the daily
practices of medical professionals and researchers holds
potential for significant enhancements in productivity andefficiency. However, as for any tool, the impact of LLMs
will be shaped by their intended application. In summary,to address the various challenges arising from the use of

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LLMs, we believe that a multistage evaluation process isindicated: first, it should be determined which tasks canbe appropriately outsourced to such a tool and weighed
up against potential implementation hurdles [17,18]. Issues
with regulatory, ethical, and legal requirements also need
to be examined. Subsequently, the generated material must
be thoroughly checked for content accuracy and appropriate
references to mitigate the risk of hallucinations or plagiarism[18,19]. Finally, any use of these tools must be transparently disclosed, with accountability always resting with thehuman user [20]. Overall, the orientation toward a bioethical framework composed of the principles of beneficence,
nonmaleficence, autonomy, and justice provides valuableguidance for the deployment of LLMs in a medical context[21]. We advocate for responsible LLM usage, necessitating a comprehensive understanding of their limitations, anawareness of potential data biases, and a consistent adherenceto ethical standards.

Acknowledgments
No external funding in the preparation of this paper. When preparing this manuscript, the authors used OpenAI’s ChatGPT-4
to translate expressions and suggest vocabulary for improved readability (search prompts shown in Multimedia Appendix 2).
After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the
publication.
Authors’ Contributions
FR conceptualized the study; designed the methodology; was in charge of the project’s administration, software, validation,and visualization; and drafted the manuscript. CL conceptualized the study, designed the methodology, supervised the study,
and reviewed and edited the manuscript. MG designed the methodology, was responsible for validation and supervision, andreviewed and edited the manuscript. HDV acquired the funding, designed the methodology, was responsible for validationand supervision, and reviewed and edited the manuscript. NMD conceptualized the study; acquired the funding; designed themethodology; was responsible for the software, validation, visualization; drafted the manuscript; and reviewed and edited themanuscript.
Conflicts of Interest
FR and CL are current employees of Pfizer Pharma GmbH, Berlin, Germany. The opinions expressed in this article are those of
the authors and not necessarily those of Pfizer. Pfizer was neither financially involved in the creation nor in the publication ofthis article. The other authors declare no conflicts of interest.
Multimedia Appendix 1
Different input styles for prompt optimization using a text-based or structured tabular approach. This self-constructed example
illustrates 2 possible approaches to formulating prompts: input as continuous text (column 1), akin to directives provided toa human assistant, or, alternatively, structured as bullet points (column 2 and 3). The latter option also allows for saving theprompt structure, facilitating easier modification, and reuse for similar tasks.
[DOCX File (Microsoft Word File), 15 KB-Multimedia Appendix 1]
Multimedia Appendix 2
Web search prompts using ChatGPT-4.
[DOCX File (Microsoft Word File), 31 KB-Multimedia Appendix 2]
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Abbreviations
AI: artificial intelligence
API: application programming interface
LLM: large language model

Edited by Alexandre Castonguay; peer-reviewed by Lingxuan Zhu, Qiao Jin; submitted 16.03.2024; final revised version
received 09.07.2024; accepted 11.07.2024; published 05.09.2024
Please cite as:
Reis F, Lenz C, Gossen M, Volk HD, Drzeniek NM
Practical Applications of Large Language Models for Health Care Professionals and Scientists
JMIR Med Inform 2024;12:e58478
URL: https://medinform.jmir.org/2024/1/e58478
doi: 10.2196/58478

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Reis et al© Florian Reis, Christian Lenz, Manfred Gossen, Hans-Dieter Volk, Norman Michael Drzeniek. Originally published in
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JMIR Med Inform 2024 | vol. 12 | e58478 | p. 9
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