How AI is changing the way we approach Medical Scientific Writing

Dr R Klingman, MD 

Key Takeaways

• Governance Era: Recent guidance from regulatory and professional bodies such as the Federal Drug Agency (FDA), European Medicines Agency (EMA) and ISMPP highlights the responsible integration of AI in medical communication and healthcare 

• Writing by design: Utilizing AI as a supportive tool rather than a source—where human oversight and verification are key—reflects a “writing by design” approach 

• Human-in-the-loop: Effective collaboration with AI in medical writing requires continuous quality evaluation, error correction to ensure messaging is clear and equitable, maintaining a human-in-the-loop throughout the process is essential 

Introduction

Are medical writers going to become defunct and fall away in the face of generative AI? This article explores the many reasons why human oversight and insight are irreplaceable by machine learning. However, by harnessing AI medical writing will reach its defining moment.

Playing a vital role in healthcare, by communicating scientific data to healthcare professionals, regulators, and patients, medical writing supports informed decision-making and effective scientific exchange.¹ The integration of artificial intelligence (AI) and large language models (LLMs) is transforming this scientific communication landscape, enriching content creation, and reshaping healthcare communication.^1,2 As medical writers work to make complex information accessible and understandable, they encounter both exciting opportunities and significant challenges, particularly concerning quality and accountability in AI-generated content.² To leverage these tools responsibly, medical writers must verify accuracy, assess relevance, and understand both the capabilities and limitations of AI.² 

What are the types of AI tools that medical writers can use ?

Medical writers are now exposed to an incredibly wide range of AI tools to support their research and writing.³ In particular, large language models (LLMs) have the potential to transform medical writing, with advanced systems such as Chat Generative Pre-trained Transformer (ChatGPT; including GPT-4 and GPT-4o), Claude, Microsoft Copilot, and Gemini AI, offering distinct capabilities across different stages of the writing process. Everyday new companies are being formed to create bespoke AI tools to suit medical audiences including more specific scientific searches that produce summaries of scientific topics and supporting publications ¹ 

Applications of AI in Medical Writing: How do medical writers use AI tools? 

Medical writers and researchers are increasingly adopting AI tools for tasks including:⁴ 

• Research and Literature Reviews: AI assists in streamlining data analysis and referencing, significantly enhancing the clarity and conciseness of manuscripts.

• Drafting Manuscripts: AI tools assist in creating structured drafts while complying with journal requirements.

• Statistical Analysis: AI aids in statistical tasks, addressing the shortage of expertise in this area.

• Language Translation and Localization: AI enhances accessibility by translating and localizing content for diverse audiences.

The role of AI in research and scholarly publishing is rapidly evolving, with growing integration across the publication process, and much of the literature on ChatGPT in medical writing focuses specifically on academic manuscript preparation.^{4, 5} 

AI can support time-intensive tasks that are essential for clarity and conciseness by generating structured drafts and streamlining activities such as data analysis, reference management, and compliance with journal requirements, thereby transforming how scientific knowledge is created, reviewed, and shared. This fills the gap in efficiency and reaching deadlines in shorter time spans.^{4, 6} 

In addition, the rapidly expanding volume of scientific literature makes it increasingly difficult to stay current and informed, and AI may legitimately assist in improving the efficiency of literature review, particularly given its strength in synthesizing existing text.³ AI can rapidly extract relevant information, identify key findings, and produce coherent summaries or draft text, thereby improving time efficiency in the writing process.^{1, 3} 

Support for statistical analysis is another widely cited application of AI tools, particularly within a recognised shortage of statistical expertise, and limited availability of methodologists and biostatisticians, in medicine and biomedical research.³ 

In addition, AI can enhance medical writing through language translation and localisation, while also assisting with summarising articles, generating drafts, and identifying relevant academic papers.¹ 

Overall, while AI technology demonstrates proficiency in generating manuscript segments and synthesizing material, existing evidence reveals ongoing difficulties related to correctness, citation dependability, and content originality. Therefore, while AI shows promise in generating content, human oversight remains essential to address concerns of accuracy, citation reliability, and originality.¹ 

What are some of the shortcomings of AI use in medical and scientific writing? 

Challenges of AI in Medical Writing

Despite its potential, AI faces notable challenges:

• Accuracy and Bias: AI systems can produce incorrect information, increasing the risk of medical errors.

• Inconsistency: Responses may vary across similar tasks, requiring careful validation.

• Ghostwriting Concerns: The indistinguishable nature of AI-generated and human-authored texts raises multiple ethical questions.

• AI Hallucination: Instances of fabricated data and references highlight the need for stringent verification.

Accuracy, bias, and inconsistency remain significant challenges in the use of AI for medical writing. AI systems may generate erroneous responses, increasing the risk of medical errors and necessitating thorough validation of all AI-generated content. In addition, training datasets may contain inherent biases that lead to skewed outputs, and AI tools can produce inconsistent or inadequate responses across similar tasks.¹ 

The ghostwriting potential of ChatGPT and related AI tools is an issue that has been widely debated, particularly given the difficulty in distinguishing AI-generated from human-authored work, including in academic abstracts.^1,7 Although detection tools such as AI Detector, GPT Detector, and GPTZero are available, and quality assurance platforms such as iThenticate and Turnitin can support content verification, they do not replace careful human editorial scrutiny.¹ 

Paper mills also pose an increasing threat to the integrity of the medical literature, with synthetic or fraudulent scientific papers estimated to account for up to 24% of publications in medicine.³  Encouragingly, AI may also assist in identifying such fraudulent outputs; for example, the GPT-2 Output Detector has demonstrated strong discriminative performance for detecting fake text (AUC 0.94), outperforming some plagiarism detection tools and blinded human reviewers. ³ 

Another important limitation is AI hallucination, where  fabricated data or references are generated even by advanced models.² Citation hallucination has been reported in approximately 20–50% of AI-generated scientific content, depending on the task complexity, which means that the medical writer needs to be rigorous in identifying scientifically validated sources.² Citation-checking tools can help flag incorrect or fabricated references, and strategies such as retrieval-augmented generation, which limits outputs to only trusted and up-to-date databases, can further reduce the risk of hallucinations. 

The Human Element: What Can AI NOT Replace? 

Human beings have nuances of creativity, critical thinking and originality that are almost impossible to replicate. Although AI tools can generate content efficiently, they are content generators, not medical writers and therefore they lack originality, advanced analytical ability, and the capacity to infer complex medical meaning—qualities that are fundamental to professional medical writing.⁴ 

Despite their impressive capabilities, AI systems are not infallible and do not possess cognitive reasoning, contextual judgement, or emotional intelligence.² As a result, medical writers act as essential safeguards, there to act as a barrier by validating the accuracy, consistency, and clinical relevance of AI-generated material, identifying discrepancies, and correcting biased or misleading content.² The absence of human judgement also increases the likelihood of contextual errors, as AI may misinterpret complex medical concepts or overlook nuanced clinical insights, and it cannot replicate the critical reasoning and ethical considerations required in medical research.⁶ 

Beyond scientific publications, medical writers produce a wide range of materials, including plain language summaries, books, continuing medical education slide decks, e-learning modules, patient brochures, promotional content, blogs, advisory board reports, and regulatory documents.⁴

Understanding the unique needs of the targeted audience is essential to effective health communication, however, AI may not be able to implement audience-specific writing in the same way that human writers do.² Medical writers carefully consider who will read their content, how their word choice may affect interpretation, and consequently tailor their writing tone, structure and technical depth to suit the target audience.² AI also has limitations in medical marketing, where messaging is shaped by market insights, competitor intelligence, and key opinion leader perspectives. It cannot fully replicate the nuanced writing needed for advisory board reports, slide decks, and other strategic promotional materials.⁴ 

What are the recent guidelines for use of AI in medicine and health communications? 

Recent guidelines from regulatory and professional bodies highlight the responsible use of AI in medical communication and healthcare.^8,9 The FDA and EMA’s January 2026 Guiding Principles of Good AI Practice in Drug Development outline ten joint principles, including that the development and use of AI technologies be ‘Human-centric by design’ aligning with ethical and human-centric values, and employing ‘Risk-based performance assessment’ to evaluate the complete system including human-AI interactions.⁸ In September 2025, the International Society for Medical Publication Professionals (ISMPP) issued enhanced guidance on use of AI for medical publication professionals as well as the wider medical communications community.⁹ The ISMPP update recognizes the importance of complying with authorship guidance including properly disclosing the use of AI models and tools, the need for awareness of the systemic problem of bias with LLM, and why all outputs from AI models should be reviewed and verified for accuracy by humans.⁹ Likewise, the International Committee of Medical Journal Editors (ICMJE) revised its guidance in January 2024, mandating that authors attribute and cite AI tools used in manuscript preparation while retaining full accountability for content accuracy and integrity.¹ 

What does the future hold for the medical writer with AI – friend or foe?

AI is undoubtedly going to become the medical writer's right hand assistant. Proficiency in using generative AI becoming an essential skill for medical writers, much like mastering word processors and internet browsers.¹⁰ Most likely medical writing will become increasing collaborative , with AI supporting initial drafting and data analysis, while human medical writers contributing expertise and ethical oversight. This symbiotic relationship has the potential to improve both the quality and efficiency of medical writing whilst retaining human creativity, originality and critical thinking.⁷  

Conclusions

As AI continues to influence health communication, the role of medical writers must adapt. While AI tools can improve efficiency and generate diverse content, they are no substitute for human medical expertise. Medical writers must apply critical thinking to navigate the significant risks of misinformation, bias and hallucination and bias to ensure all content is accurate and responsibly created.²   Adopting a “writing by design” approach, whereby AI is leveraged as a tool rather than a source—where final content is guided and verified by human input—is essential. Maintaining a human-in-the-loop throughout the writing process allows medical writers  to effectively harness AI capability, while safeguarding the integrity of healthcare communication. It is our foremost responsibility as medical professionals to “ First, do no harm”. This now expands beyond the traditional role of diagnosis and prescribing to ensuring that healthcare information is safeguarded in the context of AI.

References

1. Fakharifar A, Beizavi Z, Pouramini A, Haseli S. Application of artificial intelligence and ChatGPT in medical writing: a narrative review. J Med Artif Intell 2025;8:52  https://dx.doi.org/10.21037/jmai-24-342

   
   

2. Miguel RT, El Joumaa M, Ali R. Artificial Intelligence Bias in Health Communication: Risks and Strategies for Medical Writers. AMWA. 2025;40(3). https://doi.org/10.55752/amwa.2025.479

   
   

3. Ramoni D, Sgura C, Liberale L, Montecucco F, Ioannidis JPA, Carbone F. Artificial intelligence in scientific medical writing: legitimate and deceptive uses and ethical concerns. Eur J Intern Med 2024;127:31–35. doi: 10.1016/j.ejim.2024.07.012. https://pubmed.ncbi.nlm.nih.gov/39048335/ 

   
   

4. Ahaley SS, Pandey A, Juneja SK, Gupta TS, Vijayakumar S. ChatGPT in medical writing: A game-changer or a gimmick? Perspect Clin Res 2024;15(4):165–171. doi: 10.4103/picr.picr_167_23. https://pmc.ncbi.nlm.nih.gov/articles/PMC11584153/ 

   
   

5. Mondal H, Mondal S, Behera JK. Artificial intelligence in academic writing: Insights from journal publishers' guidelines. Perspect Clin Res 2025;16(1):56–57. doi: 10.4103/picr.picr_67_24. https://pmc.ncbi.nlm.nih.gov/articles/PMC11759234/ 

   
   

6. Alokaily F. Artificial intelligence (AI) in medical publications pros and cons. Saudi Med J 2025; 46(1). doi: 10.15537/smj.2025.46.1. https://pmc.ncbi.nlm.nih.gov/articles/PMC11717111/ 

   
   

7. Kapoor MC. Navigating the impact of artificial intelligence on medical writing. Ann Card Anaesth 2025;28(2):105–106.  doi: 10.4103/aca.aca_14_25. https://pubmed.ncbi.nlm.nih.gov/40237655/ 

   
   

8. US Food and Drug Administration (FDA)/European Medicines Agency (EMA). Guiding Principles of Good AI Practice in Drug Development. January 2026. Available at: https://www.fda.gov/media/189581/download  (Accessed: 19 February 2026).

   
   

9. Goldman K, Moss V, Griffiths S, Patel CJ, Dorrell G, Foreman-Wykert A, et al. Enhanced guidance on artificial intelligence for medical publication and communication professionals. Curr Med Res Opin 2025;41(8):1395–1400. doi: 10.1080/03007995.2025.2556012. https://pubmed.ncbi.nlm.nih.gov/40938311/ 

   
   

10. Armitage R. Generative AI in medical writing: co-author or tool? Br J Gen Pract 2024;74(740):126–127.  doi: 10.3399/bjgp24X736605. https://pubmed.ncbi.nlm.nih.gov/39222432/