The Paradigm Shift in Health Sciences Literature: Charting the Future of Large Language Models in Scientific Publishing

Abstract

The exponential expansion of biomedical literature, coupled with the growing demand for rapid clinical knowledge dissemination, has created an unsustainably high workload for researchers, peer reviewers, and journal editors. Large Language Models (LLMs) represent a transformative architectural pivot capable of automating, optimizing, and reshaping workflows across the health sciences publishing lifecycle. This paper comprehensively analyzes the current and future applications of LLMs in manuscript preparation, peer review workflows, and editorial curation. Furthermore, we examine the profound technical and ethical vulnerabilities inherent to generative artificial intelligence in medicine—including factual confabulations, demographic biases, and data privacy conflicts with regulatory frameworks like HIPAA and GDPR. Finally, we evaluate the emerging consensus frameworks, such as the FUTURE-AI guidelines and updated International Committee of Medical Journal Editors (ICMJE) criteria, advocating for a hybrid human-AI symbiotic model that preserves scientific integrity while maximizing technological efficacy.

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