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
Creatures that display ‘hedonic place preference behaviour’ are thought by many scientists to experience feelings, on the assumption that their attraction to pleasure-producing substances which lack nutritional value (e.g. cocaine, morphine) cannot easily be attributed to
unconscious instinctual behaviour. In this paper, we discuss how a simple artificial agent that instantiates attributes of an affective system engaging in felt uncertainty about its intrinsic needs in relation to environmental resources can similarly display hedonic place preference behaviour
— through an apparently subjective form of information processing — while simultaneously being entirely deterministic. We outline some implications of this artificially engineered behaviour for our understanding of the physical basis of consciousness and the experience of free
will.
M. Solms, St John Grimbly, B. Bassett et al.· Journal of Consciousness Stu...· 0 citations
Confirmed oncogenic microbes contribute significantly to cancer burden. Identifying and confirming novel microbial oncogenicity could yield strategies and tools that will reduce disease burdens. However, relevant evidence may be dispersed across a vast biomedical literature that is infeasible for humans to comprehensively synthesize. Large Language Models (LLMs) may enable scalable, expert-level systematic evidence synthesis to identify high priority microbe-cancer pairs; however, such capabilities have not yet been demonstrated.
Domain experts were recruited to create a human-validated test dataset to benchmark the performance of LLMs (Gemini 2.5 Pro, Gemini 2.5 Flash, GPT-5, and GPT-5 Nano) on 24 original research papers using Mouse Mammary Tumor Virus-Like Virus and breast cancer as a case study. We devised a structured template for evidence extraction and appraisal of papers, consisting of multiple choice, Likert-scale, multi-select, and free-text question types (77 question items across 24 papers). Agreement between (1) experts, and (2) experts and each LLM, was determined per question instance using novel scoring metrics. LLMs were assessed by comparing inter-expert and expert-LLM agreement score distributions to determine whether LLMs behaved as additional experts by either increasing or maintaining inter-expert agreement. Free-text responses were further evaluated qualitatively.
Across all question types, LLM responses aligned closely with expert assessments, with two models (GPT-5, GPT-5 Nano) achieving score distributions statistically indistinguishable from those of experts. Gemini models behaved similarly for most tasks but were significantly more lenient in applying microbial oncogenesis criteria, often over-attributing criteria fulfillment. Hallucinations were rare, although more frequent in smaller models (Gemini 2.5 Flash, GPT-5 Nano). Methodological appraisal and identification of contradictions within full-text papers were the most persistent areas of LLM vulnerability, however, the error rate could not be directly compared with experts.
Two LLMs (GPT-5, GPT-5 Nano) were indistinguishable from domain experts on structured domain research paper evaluation tasks. This evidence supports use of LLMs for automated systematic evidence synthesis. However, methodological appraisal tasks and contradiction identification in full-text papers remain weaknesses requiring further investigation, strengthening, and possibly multi-model strategies.
Kaela Kokkas, Hairong Wang, Richard Klein et al.· Frontiers in Cellular and In...· 0 citations
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