ReviewThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology2024
The transformative impact of large language models on medical writing and publishing: current applications, challenges and future directions.
Review in The Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Concerns of Using Large Language Models in Health Care Research and Practice: Umbrella Review.Journal of medical Internet research · 2026Pooled it
- Blinded by the Bot: Benchmarking GPT and Gemini Against Human Authors in Otolaryngology Reviews.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Arkangel AI, OpenEvidence, ChatGPT, Medisearch: Are They Objectively up to Medical Standards? A Real-Life Assessment of LLM Chatbots in Health Care.Mayo Clinic proceedings. Digital health · 2026Article
- Harnessing big data and artificial intelligence in transfusion medicine: Opportunities for precision, safety and efficiency.Vox sanguinis · 2026Review
- Applications of Large Language Models in Medical Research: From Systematic Reviews to Clinical Studies.Bioengineering (Basel, Switzerland) · 2026Review
- A structured framework for effective and responsible generative artificial intelligence chatbot prompt engineering throughout the scientific process: a comprehensive guide for the health and medical researcher.Frontiers in artificial intelligence · 2026Review
- Prompt engineering for generative artificial intelligence chatbots in health research: A practical guide for traditional, complementary, and integrative medicine researchers.Integrative medicine research · 2025Article
- Navigating the AI frontier: Balancing efficiency and integrity in orthopaedic peer review.Journal of clinical orthopaedics and trauma · 2025Article
- Use of Artificial Intelligence in Scientific Writing. The Danger of Trying Too Hard to Please.Hemodialysis international. International Symposium on Home Hemodialysis · 2025Article
- Regulating the unseen hand: AI, authorship, and trust in medical science.Annals of medicine and surgery (2012) · 2025Article
- Effects ofThe Korean journal of physiology & pharmacology : official journal of the Korean Physiological Society and the Korean Society of Pharmacology · 2025Article
- A guide to evade hallucinations and maintain reliability when using large language models for medical research: a narrative review.Annals of pediatric endocrinology & metabolism · 2025Article
- Demographic and Physical Determinants of Unhealthy Food Consumption in Polish Long-Term Care Facilities.Nutrients · 2025Article
- The Ability of Large Language Models to Generate Patient Information Materials for Retinopathy of Prematurity: Evaluation of Readability, Accuracy, and Comprehensiveness.Turkish journal of ophthalmology · 2024Article
- Mind the gap: A cross-sectional analysis of large language model guidance in emergency medicine journals.Digital healthArticle
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.