Artificial Intelligence in Medical Publishing: At the Crossroads of Innovation, Scientific Integrity, and Editorial Governance

Abstract

Medical publishing has undergone one of the most profound transformations in its history. From traditional manuscript handling to electronic submission systems, digital peer review, plagiarism detection software, and open-access publishing, each technological advance has reshaped scholarly communication by improving the efficiency, accessibility, and dissemination of scientific knowledge. The emergence of generative artificial intelligence (AI), AI-powered evidence-synthesis platforms, and increasingly autonomous AI assistants marks the next stage in the evolution of scholarly publishing.¹ Unlike earlier innovations that primarily improved publishing processes, these technologies increasingly assist with and, in some cases, undertake tasks that have traditionally depended on human intellectual judgement, including literature discovery, scientific writing, data analysis, peer review, editorial decision support, and knowledge synthesis. Consequently, the challenge confronting medical journals has shifted from adopting new technologies to governing their responsible use in ways that foster innovation while safeguarding scientific integrity, editorial accountability, and public trust. Artificial intelligence is reshaping medical publishing through three complementary technologies. Generative AI models, including OpenAI's ChatGPT (GPT-4o and GPT-5), Google's Gemini, Anthropic's Claude, and Meta's Llama, generate original text, code, tables, figures, and multimedia content. AI-powered search and evidence-synthesis platforms, such as Perplexity AI, Elicit, Consensus, Semantic Scholar AI, and Google AI Overviews, assist with literature retrieval, evidence synthesis, and knowledge discovery. Increasingly autonomous AI assistants, including Microsoft Copilot, ChatGPT Agent, and Gemini Workspace, have evolved from conversational tools into workflow assistants capable of performing multi-step tasks such as literature review, manuscript drafting, statistical coding, journal selection, and preparation of responses to peer reviewers. Although frequently grouped together under the umbrella of AI, these technologies present fundamentally different editorial challenges.²