A review in The American Surgeon (2026) maps artificial intelligence (AI), especially large language models (LLMs), across four stages of surgical research: literature review, data analysis, manuscript preparation and research synthesis. The authors propose matching each stage with AI’s role, its benefits, the ways it can fail and the oversight needed. AI query systems are designed to speed synthesis across thousands of publications. In data analysis, LLMs paired with statistical environments can run regression models and generate code that can be audited; specialized architectures can automate image segmentation and histopathology analysis.

The review identifies risks at each stage: LLM errors and hallucinations; reduced reproducibility from changes in model versions and variability between runs; fabricated citations and paper-mill submissions that threaten research integrity; and over-reliance that may weaken trainees’ critical appraisal and biostatistical reasoning. The authors argue for oversight tailored to each stage and pair risks with corresponding mechanisms, translating them into implementation recommendations for surgical departments, residency programs, research groups and journals.

The framework treats AI as an aid to surgical scholarship rather than a replacement for researchers. Its recommendations include standardized disclosure frameworks, AI literacy in graduate medical education and institutional governance structures intended to preserve the integrity of surgical research.

A review in The American Surgeon (2026) maps artificial intelligence (AI), especially large language models (LLMs), across four stages of surgical research: literature review, data analysis, manuscript preparation and research synthesis. The authors propose matching each stage with AI’s role, its benefits, the ways it can fail and the oversight needed. AI query systems are designed to speed synthesis across thousands of publications. In data analysis, LLMs paired with statistical environments can run regression models and generate code that can be audited; specialized architectures can automate image segmentation and histopathology analysis.

The review identifies risks at each stage: LLM errors and hallucinations; reduced reproducibility from changes in model versions and variability between runs; fabricated citations and paper-mill submissions that threaten research integrity; and over-reliance that may weaken trainees’ critical appraisal and biostatistical reasoning. The authors argue for oversight tailored to each stage and pair risks with corresponding mechanisms, translating them into implementation recommendations for surgical departments, residency programs, research groups and journals.

The framework treats AI as an aid to surgical scholarship rather than a replacement for researchers. Its recommendations include standardized disclosure frameworks, AI literacy in graduate medical education and institutional governance structures intended to preserve the integrity of surgical research.