Artificial Intelligence in medical research and publishing: Progress, risks, and future perspectives.

Area of Science:

• Medical research and scholarly publishing
• Artificial intelligence applications in science

Background:

• Artificial intelligence (AI) has evolved from decision support to a complex ecosystem including machine learning and large language models.
• AI applications in medical research and publishing span data analysis, diagnostics, evidence synthesis, manuscript preparation, peer review, and analytics.

Purpose of the Study:

• To review the evolution and applications of AI in medical research and publishing.
• To critically examine the risks, ethical dilemmas, and emerging governance frameworks for AI in science.
• To outline future directions for responsible AI integration in the scientific enterprise.

Main Methods:

• This narrative review synthesizes current evidence on AI in medical research and publishing.
• It critically examines associated risks and ethical dilemmas.
• It reviews emerging regulatory and editorial guidance for AI use.

Main Results:

• AI offers benefits like accelerated research, improved precision, enhanced reproducibility, and broader access to scientific communication.
• Significant challenges include algorithmic bias, citation errors, authorship accountability issues, confidentiality concerns, and potential peer review degradation.
• Editorial organizations and health authorities are developing governance frameworks for AI use, emphasizing disclosure and human verification.

Conclusions:

• AI is transforming medical scholarship, presenting both opportunities and substantial ethical challenges.
• Revising norms around authorship, transparency, and responsibility is crucial as AI outputs increasingly mimic human work.
• Future directions involve explainable AI, AI literacy, and hybrid human-AI workflows to maintain trust and rigor in scientific publishing.