The ethics of using artificial intelligence in writing medical research papers

The rapid integration of large language models into medical publishing offers considerable potential for improving drafting efficiency but simultaneously raises substantial concerns regarding research integrity, accountability, and the reliability of the scientific record. Recent incidents in which artificial intelligence (AI) systems were listed as coauthors have prompted urgent regulatory revisions. In this review, we identify a global consensus that strictly prohibits AI authorship, as algorithms lack both moral agency and legal accountability. Transparency has emerged as an essential requirement, and undisclosed AI use is increasingly regarded as a form of ethical misconduct. Key risks include “hallucinations” (notably citation fabrication), algorithmic bias, and potential violations of privacy regulations (e.g., the Health Insurance Portability and Accountability Act) when protected health information is processed through cloud-based platforms. The analysis indicates that rigid prohibitions are operationally unenforceable, supporting instead a “human stewardship” model in which AI functions as a drafting scaffold subjected to rigorous human verification. AI represents a lasting transformation in medical writing that necessitates a shift from simple prohibition to structured governance. To preserve epistemic validity, we propose a framework built on task segmentation, mandatory cross-referencing of claims, and data sovereignty. Ultimately, AI must remain a transparent assistive tool, with full responsibility for the manuscript residing exclusively with the human investigator.

Authors

Institutions

Publication Details

Journal
Kosin Medical Journal
Published
2025-12-31
DOI
https://doi.org/10.7180/kmj.25.141
Citations
1
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.65
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The ethics of using artificial intelligence in writing medical research papers

Hyunyong Hwang, Shinae Yu
1 citations
Kosin Medical Journal
Artificial Intelligence in Healthcare and Education
0.65
article

The ethics of using artificial intelligence in writing medical research papers

Hyunyong Hwang, Shinae Yu
article en
1 citations

Abstract

The rapid integration of large language models into medical publishing offers considerable potential for improving drafting efficiency but simultaneously raises substantial concerns regarding research integrity, accountability, and the reliability of the scientific record. Recent incidents in which artificial intelligence (AI) systems were listed as coauthors have prompted urgent regulatory revisions. In this review, we identify a global consensus that strictly prohibits AI authorship, as algorithms lack both moral agency and legal accountability. Transparency has emerged as an essential requirement, and undisclosed AI use is increasingly regarded as a form of ethical misconduct. Key risks include “hallucinations” (notably citation fabrication), algorithmic bias, and potential violations of privacy regulations (e.g., the Health Insurance Portability and Accountability Act) when protected health information is processed through cloud-based platforms. The analysis indicates that rigid prohibitions are operationally unenforceable, supporting instead a “human stewardship” model in which AI functions as a drafting scaffold subjected to rigorous human verification. AI represents a lasting transformation in medical writing that necessitates a shift from simple prohibition to structured governance. To preserve epistemic validity, we propose a framework built on task segmentation, mandatory cross-referencing of claims, and data sovereignty. Ultimately, AI must remain a transparent assistive tool, with full responsibility for the manuscript residing exclusively with the human investigator.

Kosin Medical JournalVol. 40(4)
Inje University Haeundae Paik Hospital (KR)
Openalex Percentile: Top 18%
Artificial Intelligence in Healthcare and Education
0.65
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.