Governing the Algorithm: Navigating AI Regulations in Medical Writing

Artificial Intelligence (AI) has, without a doubt, transformed the operations of many industries [1]. So, it was only natural that it would be applied in medical writing. AI can prove to be an effective aid in medical writing, expediting the process, but also poses several challenges. Thus, keeping in mind how rapidly the adoption of AI is increasing among the professional medical writers, it is important to be well-equipped for the challenges [2]. This article talks about the history of use of AI assistance in writing, impact of AI on medical writing, the ethical concerns it raises, and the regulatory and ethical safeguards required for ensuring responsible use of AI in medical writing.

Historical Context of AI in Writing

AI-assisted writing has evolved through key phases, driven by advancements in natural language processing (NLP) and machine learning. In the 1980s-1990s, word processors like Microsoft Word focused on basic editing. The 1990s-2000s introduced grammar checkers using rule-based algorithms. The 2000s-2010s saw predictive text, enhancing efficiency through statistical models. Deep learning revolutionized AI writing in the 2010s-2020s, enabling tools like GPT-4 to generate coherent text. Today, AI-driven platforms assist with style, grammar, and even creative writing. This shift from word-based editing to idea synthesis raises questions about AI’s role in authorship and intellectual contribution [3].

The Role of AI in Medical Writing

ChatGPT and other deep learning-based language models have useful applications in medical writing. They can aid in the preparation of research abstracts, clinical trial reports, patient education materials, discharge summaries, and medical textbooks and guidelines. AI models can also help in administrative work, for example, preparation of pre-authorization letters, insurance requests, and product labels for medical devices and pharmaceuticals [3].

The use of AI is definitely helpful but AI-generated text needs to be thoroughly screened by experts to ascertain authenticity and trustworthiness. The use of AI models for generating medical writing text can raise a number of issues such as bias, false information in text, and plagiarism. So, despite the fact that AI models can assist in writing, they need expert human oversight to prevent the aforementioned issues. Additionally, because medical knowledge advances very quickly, AI models need to be updated regularly to maintain the new developments. Therefore, AI integration in medical writing can improve efficiency, but human involvement is still necessary to assure credibility and maintain ethical standards [3].

Ethical Issues in AI-Based Medical Writing

AI in medical writing poses several ethical issues that need to be resolved to preserve trust and credibility in healthcare communications.

• Bias and Inclusivity. AI language models like ChatGPT are trained on huge datasets, which makes their responses prone to the inherent biases present in the training data [3, 4]. This may lead to racial, gender, or cultural bias that lowers the level of inclusivity in medical writing [3].
• Misinformation and Hallucination. Misinformation in the form of factual errors or made-up references may appear in AI-generated content. This kind of event is called ‘hallucination’ [3,4]. The unchecked usage of such data in medical journals may result in erroneous clinical decision-making and potential legal issues [4].
• Transparency and Accountability. AI-generated medical writing often lacks transparency, as the sources and decision-making processes behind the text are not always evident [3]. This opacity makes it difficult to assess content credibility and detect potential errors [4].
• Impact on Human Expertise and Creativity. Over-reliance on AI in medical writing may diminish critical thinking and writing skills among researchers [3]. AI’s ability to generate polished but formulaic text could reduce originality and lead to a homogenization of scientific literature [4].
• Plagiarism and Research Integrity. AI-generated text may unintentionally replicate existing content, raising concerns about plagiarism and research misconduct [3].
• Access Disparities and Job Displacement. The excessive cost and technical expertise required for AI-based writing tools may create a digital divide, limiting access for researchers from resource-limited settings [3]. Additionally, the automation of writing tasks raises concerns about job displacement in medical editing and publishing [4].

Recent Developments in AI Regulations in Medical Writing

As large language models (LLMs) become increasingly integrated into medical writing, recent studies have highlighted the pressing need for standardized regulations. Two significant investigations—one examining Korean medical journals and another focusing on radiological journals—shed light on the current landscape of AI governance in scientific publishing.

A 2024 survey of one hundred leading Korean medical journals found that only 18% had established AI-related policies, a stark contrast to the 87% adoption rate among global journals. High-impact journals were more likely to implement AI policies, with 28% of top-quartile journals incorporating guidelines compared to just 8% in the lowest quartile. Key provisions included prohibiting AI authorship (94.4%), mandating human responsibility for content accuracy (72.2%), and requiring full disclosure of AI use (100%). However, only 16.7% of journals permitted AI use solely for language improvement, and 44.4% actively discouraged AI-generated content beyond basic editing [5].

Similarly, a meta-research study on radiological journals revealed a significant gap in AI policy adoption. Less than half (43.9%) of radiology journals had explicit LLM guidelines, with policies primarily targeting authors (43.4%) rather than reviewers (29.6%) or editors (25.9%). Journals under publishers like Elsevier demonstrated higher AI policy adoption rates compared to Springer and BMC. The study also underscored inconsistencies in disclosure requirements, with many journals referencing publisher-wide policies rather than developing independent frameworks. Experts call for standardized AI regulations to enhance transparency and ensure the ethical integration of LLMs in scientific publishing [7].

Both studies highlight the urgent need for clear, enforceable AI policies in medical writing. Standardized disclosure requirements, verification mechanisms, and expanded guidelines—especially concerning AI-generated images and videos—are essential to maintaining research integrity and credibility in an AI-driven era [5, 7].

Regulatory and Ethical Safeguards for AI in Medical Writing

To address the ethical concerns associated with AI in medical writing, regulatory and ethical safeguards must be implemented. The importance of adopting AI policies in medical journals is increasing. The key points of such policies should include AI disclosure, prohibition of AI as an author without human oversight, and guidelines for ethical AI use [6]. Regulatory bodies must refine and enforce these standards [4].

• The bias and misinformation in AI-generated medical content can be managed with balanced training datasets, mechanisms for detecting and correcting biases, and stringent human monitoring [3].
• AI-generated content often lacks clear attribution of sources and explainability in decision-making [6]. Therefore, implementing transparency policies for disclosure of AI-generated content, source tracking, and AI auditing systems can enhance trust and reliability [3]. Developing AI tools with explainable outputs and mandatory citation of sources can improve accountability and trust in AI-driven medical documentation [4].
• Enforcing strict peer-review processes, plagiarism detection tools, and clear guidelines for AI use in research are necessary [5]. It will help tackle plagiarized or hallucinated content and maintain scientific integrity [3, 6]. Additionally, AI should be seen as an aid rather than an autonomous content creator [4].
• AI-generated content in medical writing also needs strict regulatory compliance (e.g., GDPR, HIPAA) and anonymization techniques to protect patient confidentiality [6]. AI in medical writing may pose risks to patient privacy if trained on sensitive data [1, 3].
• Journals should require authors to disclose AI use and confirm human verification of outputs [5, 6]. AI cannot be held accountable for errors; thus, human authors must retain responsibility for AI-assisted content [4].
• Training researchers in AI ethics and responsible AI use is also essential [3, 5]. These programs should emphasize AI as an assistive tool rather than a replacement for human intellect to maintain scientific integrity and creativity [4].

Therefore, rather than outright restricting and rejecting the use of AI in medical writing, it should be regulated through international ethical frameworks and technical advancements (e.g., watermarking). Interdisciplinary collaboration is also essential to ensure integrity and innovation in medical writing [3, 5].

Conclusion

The rapid integration of AI into medical writing brings both promise and responsibility. While AI can streamline workflows, enhance efficiency, and provide valuable support, it is not without its challenges. Ethical concerns such as bias, misinformation, and lack of transparency must be addressed through robust regulations and human oversight [3, 4]. Medical writing, at its core, is about accuracy, credibility, and the responsible dissemination of information—principles that AI alone cannot uphold [4, 6].

Rather than resisting AI, the focus should be on harnessing its potential while ensuring its ethical use. Through international collaboration, transparent guidelines, and continuous human involvement, AI can complement, rather than replace, the expertise of medical writers [3, 5]. The future of medical writing will not be about choosing between AI and human intellect, but rather about striking a balance where both coexist to enhance the quality, integrity, and accessibility of medical knowledge [4, 6].

References

1. Rashid AB, Kausik MAK. AI revolutionizing industries worldwide: A comprehensive overview of its diverse applications. Hybrid Adv. 2024;7:100277.
2. 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.
3. Doyal AS, Sender D, Nanda M, et al. ChatGPT and artificial intelligence in medical writing: concerns and ethical considerations. Cureus. 2023;15(8):e43292.
4. Wohlfarth B, Streit SR, Guttormsen S. Artificial Intelligence in Scientific Writing: A Deuteragonistic Role? Cureus. 2023 Sep 18;15(9):e45513. .
5. Ahn S. Large language model usage guidelines in Korean medical journals: a survey using human-artificial intelligence collaboration. J Yeungnam Med Sci. 2025;42:14.
6. Blanco-González A, Cabezón A, Seco-González A, et al. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals. 2023; 16(6):891.
7. Zhong J, Xing Y, Hu Y, et al. The policies on the use of large language models in radiological journals are lacking: a meta-research study. Insights Imaging. 2024 Aug 1;15(1):186.

Authors:

Sweaksha Langoo (MSc. Molecular Biology and Biochemistry)
Scientific Writer – Enago Life Sciences
Connect with Sweaksha on LinkedIn

Raghuraj Puthige, PhD.
Function Head, Medical Communications – Enago Life Sciences
Connect with Raghuraj on LinkedIn