Artificial intelligence (AI) is rapidly transforming medical research and scholarly publishing, reshaping how scientific knowledge is produced, evaluated, and disseminated. Initially developed as a decision-support tool, AI has evolved into a complex ecosystem encompassing machine learning, deep learning, and large language models, with applications spanning data analysis, diagnostic support, evidence synthesis, manuscript preparation, peer review, and post-publication analytics. These technologies offer substantial benefits, including accelerated research workflows, improved analytical precision, enhanced reproducibility, and expanded access to scientific communication, particularly for early-career investigators and non-native English authors.

However, the integration of generative AI introduces significant challenges. Persistent risks include algorithmic bias, hallucinated or misattributed citations, erosion of authorship accountability, confidentiality concerns, and the potential degradation of peer review integrity. As AI-generated outputs increasingly resemble human scholarly work, longstanding norms surrounding authorship, transparency, and responsibility are being reexamined. In response, editorial organizations, journals, and global health authorities have begun to establish governance frameworks emphasizing disclosure, human verification, and ethical boundaries for AI use.

This narrative review synthesizes current evidence on the evolution and applications of AI in medical research and publishing, critically examines associated risks and ethical dilemmas, and reviews emerging regulatory and editorial guidance. Finally, it outlines future directions centered on explainable and auditable AI, standardized AI literacy, and hybrid human–AI workflows. Ensuring that AI remains a tool for augmentation rather than replacement will be essential to preserving trust, rigor, and integrity in medical scholarship as these technologies become increasingly embedded in the scientific enterprise.