Jul 2026· Journal of Fatima Jinnah Medical University· Vol 20, pp. 1-2· 0 citations
TL;DR
Artificial intelligence is reshaping medical publishing through three complementary technologies: generative artificial intelligence, AI-powered evidence-synthesis platforms, and increasingly autonomous AI assistants.
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
BACKGROUND
Artificial intelligence (AI) is rapidly transforming surgical research and medical publishing by changing how clinicians discover, evaluate, synthesize, and communicate scientific evidence. Despite widespread adoption, practical guidance on the responsible integration of AI into academic writing remains limited, particularly as large language models (LLMs) and emerging AI systems become increasingly sophisticated.
METHODS
This narrative review examines the contemporary AI ecosystem relevant to medical writing, including LLMs, retrieval-augmented systems, structured evidence extraction platforms, citation analytics tools, AI-enhanced academic databases, and emerging agentic AI systems. Current evidence relating to AI-assisted manuscript preparation, ethical considerations, governance, confidentiality, reproducibility, and scientific integrity was reviewed. A practical workflow integrating literature discovery, evidence extraction, synthesis, citation validation, and editorial refinement is proposed.
RESULTS
AI can substantially improve the efficiency, organization, clarity, and consistency of manuscript preparation while supporting evidence retrieval, synthesis, and editorial refinement. However, responsible use requires awareness of important limitations, including hallucinated references, publication bias amplification, confidentiality risks, language weighting, and the inability of current LLMs to distinguish reliably between truth and plausibility. The review also highlights the increasing integration of academic databases with AI-assisted retrieval and synthesis, together with the emergence of agentic research systems capable of performing increasingly complex scientific workflows under human supervision.
CONCLUSIONS
AI should be regarded as an adjunct to, not a substitute for, scientific reasoning and scholarly judgment. When used transparently and under expert supervision, AI can enhance the quality and efficiency of medical writing while preserving the central intellectual responsibilities of authorship, interpretation, verification, and accountability. As AI systems become increasingly autonomous, maintaining rigorous human oversight will become progressively more important.
S. Thomson, B. Bassett, J. Krige· World Journal of Surgery· 0 citations
This editorial highlights the evolving principles of responsible GAI use in publishing, including the prohibition of GAI authorship, disclosure of GAI assistance, and continued human oversight.
Norhafiza Razali, Siti Norsyafika Kamaruddin, A. Shuid· Journal of Clinical and Heal...· 0 citations
Generative artificial intelligence (AI) is rapidly transforming how scientific knowledge is produced, reviewed, and disseminated. In response, journals and publishing organizations have begun issuing policies to govern AI use in scholarly publishing. However, it remains unclear whether existing governance frameworks meaningfully address the risks AI introduces across the full publication pipeline. We conducted a narrative review of journal policies, publisher guidance, and recent analyses of AI governance in scientific publishing, complemented by direct examination of submission guidelines from high-impact, open-access, and regional medical journals. Our findings show that while most journals have converged on a narrow legal consensus (prohibiting AI authorship and requiring disclosure), yet governance remains fragmented and incomplete. Policies disproportionately target text generation by authors, while leaving critical domains under-regulated, including AI-assisted data analysis, peer review practices, enforcement mechanisms, and equity implications for researchers and reviewers globally. To synthesize these findings, we introduce the AI governance readiness levels, a five-level framework for assessing how well-equipped journals are to govern AI across the research and publication process. We further describe PRAIDE (Preparation, Representation, Attribution, Integrity checks, Dissemination, and Evaluation) as an illustrative architecture that integrates existing policies, integrity safeguards, and post-publication oversight into a coherent governance model. We argue that effective AI governance in scientific publishing cannot be achieved through static rules or journal-centric control alone. Instead, it requires a shift toward shared, adaptive oversight of the scientific publishing system, recognizing that responsibility for governing AI in science is collective, continuous, and inseparable from the public trust in research.
R. Abulibdeh, J. Arslan, S. Ordóñez et al.· MIT Science Policy Review· 1 citation
A framework for sustainable, human-centered integration of AI is proposed in which AI is restricted to technical verification and efficiency, while judgments on scientific merit, ethics, and paradigm-shifting research are reserved for appropriately valued human experts.
INTRODUCTION
Artificial intelligence (AI) is being widely used by authors, reviewers and editors in the academic publishing process. However, there remains ambiguity about the boundaries of AI use. Amid this growing uncertainty, editors and publishers face an ongoing tension between a desire to advance the academic publishing process and a need to uphold scientific integrity. This study examined advisory and guideline statements about AI use by health professions education (HPE) journals.
METHODS
We collected a corpus of texts relating to the use of AI in HPE journals from 22 HPE journals, 6 respective publishers and the Committee on Publication Ethics (COPE) policies in January 2026 relating to the use of AI in academic publishing. The corpus was examined using content analysis.
RESULTS
Five main themes were identified from the analysis: 1) Accountable governance, 2) Protection of ethical standards, 3) Safeguarding of research integrity, 4) Maintenance of human oversight, and 5) Balancing of opportunities and risk.
DISCUSSION
Overall, HPE journals predominantly frame the use of AI in academic publishing as a risk rather than an opportunity, with the overarching concern being a potential compromise of scientific integrity. Editors need to manage various tensions related to AI use and disclosure, including ensuring that policies keep up with the speed of technological advancement, and potentially broader impacts on the scholarly HPE field. As guidance on AI use continues to evolve, authors, reviewers and editors must ensure that they actively maintain and optimise their policies and practices in response to emerging evidence, including of unintended consequences.
Sruthi Saravanan, S. C. Chan, M. A. Rashid· Medical Teacher· 0 citations
Artificial Intelligence (AI) is increasingly being integrated into medical research and scholarly publishing, supporting activities such as literature searching, data analysis, medical imaging, manuscript preparation, and peer review. Despite these opportunities, AI use introduces concerns related to hallucinations, bias, privacy, confidentiality, copyright, reproducibility, and scientific accountability. Existing guidance provides important principles for responsible AI use, but reporting practices remain variable. This article reviews the TITAN guidelines as a practical framework for improving transparency and responsible reporting of AI use in medical research and scholarly publishing. The TITAN guidelines provide a proportionate and technology neutral approach to AI reporting. It distinguishes minor uses, such as language assistance, from substantive applications involving research design, analysis, interpretation, or scientific content. The framework emphasizes that human researchers retain responsibility for evaluating AI outputs and the integrity of published work. It also provides a basis for authors, reviewers, editors, and publishers to incorporate standardized AI reporting into scholarly workflows. TITAN offers a practical framework for documenting meaningful AI involvement while supporting transparency, reproducibility, and human accountability. Its flexible structure can accommodate emerging AI technologies, including multimodal and agentic systems. Periodic revision and coordinated adoption by medical journals and research communities will be important to maintain its relevance as AI capabilities continue to evolve.
Arzoo Nazir, Shah Zeb· Electronic Journal of Medica...· 0 citations
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