In recent years, artificial intelligence (AI) has started to help pharmacovigilance teams, medical writers, and regulatory writers by identifying specific types of errors that humans are consistently incapable of identifying. Large language models (LLMs) like ChatGPT and the quick growth of artificial intelligence (AI) have made it more difficult to distinguish between human-written text and text produced by AI. This has made it extremely difficult to determine who wrote a piece of writing, particularly since chatbots are now widely accessible. As a result, serious issues have emerged in the academic and educational fields, where written work's authenticity and dependability are crucial (Doru et al., 2025).
It is not because AI is "smarter," nor is it because AI has any human-like comprehension of clinical science. Instead, some kinds of errors call for a level of cross-document consistency tracking, fatigue-free repetition, and pattern recognition that the human brain did not evolve to execute effectively or consistently.
Why AI Is Better in Some Error Detection
The basic thinking abilities of humans are amazing. We comprehend intention, danger, context, and nuance. Performing extremely repetitious audits over hundreds or thousands of structured pages is something that people are awful at. Consider a 600-page clinical trial report, a safety submission package with 300 distinct narratives, or a global labeling package that must be consistent across languages and nations. It is unrealistic to expect a human to maintain perfect internal consistency over such a large volume. In addition to saving reviewers' and journal editors' time, artificial intelligence (AI) has the potential to speed up the review process and guarantee the completion of more reviews in less time. The introduction of AI technologies as assistants will speed up the process, potentially to weeks after submission, in an era where the review process of submitted papers can take months. AI is still missing the thorough, high-level subject expertise required to assess articles, though. Moreover, if AI is taught on data sets containing established biases, it may mirror such biases, which could decrease the accuracy of AI in such fields. As a result, the Journal of the American Medical Association (JAMA) supports using AI as an assistant and published detailed guidelines for its use in peer review (Alnaimat et al., 2025).
Figure 1. Steps for using artificial intelligence in the writing process, with examples of two tools (Mondal et al., 2025)
AI's impact on terminology theory and practice
Translation research has traditionally focused on human communication and cognition when studying terminology. But there has been a paradigm shift in artificial intelligence recently, especially in generative AI using large language models (LLMs). Terminology plays a completely different role because these artificial intelligence systems have their own ways of producing and comprehending language. Research has not yet examined the intricacies of communication interaction between the several human and artificial actors involved in terminology and translation/interpreting activities, despite the fact that human participation is still vital. In the age of generative AI, closing this gap is important for grasping how human-AI collaboration may maximize translation processes and knowledge transmission (Massion, 2024).
The Issue of Statistical Differences
Numerical inconsistency is another type of inaccuracy that AI detects far more accurately than humans. Human beings are notably bad at verifying textual numbers. A 2025 Nature article explored the application of AI in examining mistakes, calculations, methodology, and anomalies in publication citations. Another 2025 article in Frontiers examined the application of AI technologies in data analysis and paper manuscript drafting, as well as any potential hazards. Large language models (LLMs) for reference error detection, the Black Spatula Project, and YesNoError tools for mathematical and experimental logic error detection were also examined. AI does a great job catching statistical errors and flagging research that needs to be fixed or even pulled. Even though AI helps a lot, people still need to step in and make the big decisions to keep things on track (Alnaimat et al., 2025).
Consistency Across Documents in Submission Packages
AI is able to verify that various medical study documents are consistent with one another. For instance, scientists write about what they intend to investigate first, then how they will evaluate the findings, and lastly the report that details the findings. AI is able to compare all three and ensure that no significant changes have occurred.
Additionally, it can guarantee that:
The study's patient population remained the same. They actually applied the arithmetic and statistics they had promised to use, and the outcomes they display in a particular country are identical to those they wish to employ in another.
Leaders in APAC (Asian and Pacific countries) should take note of this since many of the region's businesses are transitioning from producing basic generic medications to more complicated ones that require approval in numerous nations (such as the United States and Europe). Companies must ensure that their documentation complies with national regulations in order to sell in many locations. At the most difficult point, when there is the most work to be done, AI assists with this.
Validation of References and Bibliographies
In medical writing, references can be unexpectedly troublesome. Citations could be out-of-date, irregular in format, incomplete, or misaligned. AI can now verify journal data, validate DOIs, and compare quoted numerical figures with source publications. Insightful information regarding the references used in bachelor's theses at Aalto University is provided by recent research. Their results show that the integrity of academic references has not been jeopardized by the use of LLMs. Organizations and institutions must keep up with the rapid changes in the AI landscape in order to ensure that policies and procedures adapt appropriately (Hyyryěinen, 2024).
Preventing Protocol deviations in Clinical Development
Good Clinical Practice, or GCP for short, is a term used by the International Council for Harmonization of Technical Requirements for Registration of Medications for Human Use, or ICH. GCP, or good clinical practice, is a kind of manual that helps people conduct their trials properly. It merely states that trial administrators are responsible for ensuring the safety of trial participants. This means that they must always keep an eye so that if things go wrong due to the mistakes that were being made along the way, they will be aware of that. Therefore, those people doing the trials are called investigators. All these parties must observe PDs so that they can always take care of risks or stay on track. All this taking into consideration is a big headache. This is because all this is done manually, which is hard to do, and the unorganized data does not help with anything. With the mistakes going unnoticed. This makes it more difficult to identify systematic problems in study conduct. LLMs make it a lot easier to turn unstructured data into something you can actually use for decision-making. They are flexible and easy to expand, handling raw text without much prep work—especially when old-school methods just cannot keep up with complex problems or demand too much from users (Zou et al., 2025).
Figure 2. An overview of AI's advantages, difficulties, and potential applications in clinical documentation (Lee et al., 2024)
Harmonization in Global Labelling
When safety labels send different signals, doctors and patients end up scratching their heads, not sure what to believe. This kind of confusion doesn't just stay local, either. In today's connected world, people and healthcare workers pull information from all over the globe. When the details clash, it can mess with public health everywhere. AI can help find irregularities in safety labeling between different products or locations more quickly, enabling the identification of errors and possibly suggesting modifications where they occur. Moreover, the insights provided by AI can also uncover the reasons for the inconsistencies in certain safety updates, and this can help businesses and the authorities understand the reasons and work towards harmonization.
Where Humans Are Still Better Than AI
Even though LLMs have demonstrated impressive ability in natural language processing and creation, when used without prompt-engineering techniques, they continue to be unreliable and significantly less competent than human specialists in writing scientific systematic reviews. For activities like assessing bias risk and arriving at therapeutically relevant conclusions, language expertise is not the primary prerequisite. However, LLMs can be useful tools to assist researchers in some elements of the review process when properly supervised Accountability is beyond AI's comprehension; it only does correlation. Regulators want ethical clarity, methodological justification, and scientific reasoning, all of which are essentially human duties. Pharma companies in the United States and Asia are adopting a hybrid process (Sollini et al., 2025).
Figure 3: Performance assessment between Humans and ChatGPT across various writing assignments (Haq et al, 2023)
Conclusion
AI is emerging as a potent companion in medical writing, particularly for technical assignments requiring great precision and widespread uniformity. In lengthy manuscripts that would take many hours for people to evaluate, machines can swiftly identify grammatical errors, formatting errors, missing references, wrong numbers, and contradictions. AI cannot, however, take the role of human writers. Humans provide clinical knowledge, moral judgment, and the capacity to communicate scientific findings to regulators, patients, and doctors. Agencies all across the world still require and demand human knowledge in the areas of comprehension, responsibility, and risk associated with medical writing.
References
Alnaimat F.AlSamhori A. R. F. El Sharu H. Othman, L., Oralbek, A. & Zimba O. (2025). Artificial intelligence in detecting statistical errors Implications for authors reviewers and editors.The Journal of Korean Medical Science 40(49) e342. https://doi.org/10.3346/jkms.2025.40.e342
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Haq Z. U., Naeem, H., Naeem, A., Iqbal, F. & Zaeem, D. (2023). Comparing human and artificial intelligence in writing for health journal. An exploratory study.
https://doi.org/10.1101/2023.02.22.23286322
Hyyryläinen, E. (2024). Recognising erroneous ai generated references. https://aaltodoc.aalto.fi/handle/123456789/130287
Lee, C., Britto, S. &Diwan, K. (2024). Evaluating the impact of artificial intelligence (Ai) on clinical documentation efficiency and accuracy across clinical settings: A scoping review. Cureus
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Massion, F. (2024). Terminology in the age of ai.The transformation of terminology theory and practice. Journal of Translation Studies 4(1) 67–94. https://doi.org/10.3726/JTS012024.04
Mondal, H., Mondal, S. & Jana, S. (2025). The artificial intelligence dilemma in academic writing Balancing efficiency and integrity. Indian Journal of Cardiovascular Disease in Women 10(3) 225–230. https://doi.org/10.25259/IJCDW_86_2024
Sollini, M., Pini, C., Lazar, A., Gelardi, F., Ninatti, G., Bauckneht, M., Chiti, A. & Kirienko, M. (2025). Human researchers are superior to large language models in writing a medical systematic review in a comparative multitask assessment. Scientific Reports 16(1) 173.
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Zou, M., Popko, L. & Gaudio, M. (2025). Using large language models for advanced and flexible labelling of protocol deviations in clinical development. Therapeutic Innovation & Regulatory Science 59(4) 833–847. https://doi.org/10.1007/s43441-025-00785-z
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