Dr. Helena Fischer – Editor, Health
Artificial intelligence tools have rapidly entered the public sphere, transforming how researchers, writers, and clinicians approach their work. From grammar checkers to advanced generative models, these technologies offer powerful assistance but also raise critical questions about authorship, accuracy, and ethical apply in medical and scientific communication.
The widespread availability of AI tools like Grammarly, Microsoft Editor, Google’s Gemini, and Microsoft Copilot has created both opportunities and challenges for health professionals seeking to improve efficiency without compromising integrity. As these tools evolve, understanding their appropriate role in research and publication has become essential.
Medical journals have begun establishing clear guidelines to address these concerns. For instance, the journal Medical Care explicitly states that AI tools do not qualify for authorship under ICMJE recommendations. Authors using such tools must disclose their use in the Methods section and remain fully responsible for all content, including AI-generated portions.
This distinction is crucial because generative AI—such as large language models like ChatGPT and Claude—differs significantly from traditional machine-learning or natural language processing tools. While the latter are often explainable and reproducible, many generative AI systems operate as “black boxes,” making it tricky to verify outputs or replicate results across different users or time points.
One major concern is the lack of static, citable content in generative AI tools. Algorithm updates and model changes can alter responses unpredictably, and corporations may discontinue services without warning. This instability undermines the scientific principle of reproducibility, which requires that others be able to replicate findings using the same methods.
generative AI tools are known to hallucinate—producing false or fabricated information, including nonexistent citations. This poses a serious risk in medical research, where accuracy is paramount. Relying on unverified AI output could lead to the dissemination of incorrect data, potentially affecting clinical decisions or public health understanding.
To mitigate these risks, several best practices have emerged for using AI tools responsibly in 2026. First, transparency is essential: any use of AI must be disclosed to co-authors, editors, and readers. Attempting to present AI-generated content as original work violates publication ethics and could result in severe consequences, including retractions or institutional sanctions.
Second, all information obtained via AI tools must be manually verified. Users should never assume accuracy, even for seemingly routine facts or references. Third, ideas derived from AI should be checked for originality to avoid unintentional plagiarism, as AI may reproduce existing content without attribution.
Fourth, under current U.S. Copyright law, AI-generated work is not eligible for copyright protection because it lacks human authorship. This means text, images, or logos produced solely by AI cannot be legally owned or licensed, limiting their utility in formal publications.
Finally, if an AI tool is used in research, it must yield consistent results across team members. If others cannot replicate the output using the same inputs, the tool should be abandoned for scholarly work, as it fails the test of reliability.
using AI to catch typos or improve code readability remains a legitimate and helpful application. However, presenting AI-generated text, data, or analysis as one’s own intellectual effort crosses an ethical line. The professional and reputational risks of misuse far outweigh any short-term gains in productivity.
As AI continues to integrate into healthcare and research workflows, ongoing dialogue among editors, researchers, and technologists will be vital. Clear policies, coupled with critical user judgment, can support harness the benefits of AI while safeguarding the credibility of medical science.