ArticleInternational urology and nephrology2026
Effect of large language model on diagnostic accuracy and clinical completeness among nephrology fellows managing transplant infection.
Article in International urology and nephrology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundInfections are the predominant etiologies of post-transplant mortality in India, yet a structured infectious disease curriculum for nephrology fellows is limited. We evaluated whether large language model (LLM)-augmented clinical reasoning could improve diagnostic accuracy, clinical completeness, and clinical efficiency in managing complex transplant infection scenarios.
methodsThis was a prospective, single-center, pre (LLM-unaided)-post (LLM-aided)-study (washout period of 7 days). We investigated 1200 responses generated on 30 expert-validated kidney transplant infectious disease vignettes (50 sub-questions). 12 final-year nephrology fellows participated voluntarily in the study. Intervention was Open AI ChatGPT-4o generated exemplar responses. Responses were scored for accuracy (0-100%), completeness (0-3 scale), and time-to-answer by blinded assessors. Inter-rater reliability and paired differences were also analyzed.
findingsMedian accuracy increased from 72% (65·5-78·5) in the unaided arm to 85·5% (79·0-91·0) in the aided arm with Δ = + 13·0%, p-value < 0·001. Completeness improved from 1·9 (1·6-2·1) to 2·5 (2·3-2·8) (p-value < 0·001). Median time-to-answer decreased from 7.1 (5.9-8.4) to 5.9 (4.8-7.1) minutes (p = 0.002). The most significant gains occurred in fungal (+ 19·2%) and viral (+ 17·5%) infections, especially in immunosuppressant modulation and antifungal treatment protocols. Minimal or no benefit was observed in dual infection scenarios and niche dose-duration knowledge. Only a single clinically significant hallucination (3·3%) occurred.
conclusionOur findings primarily support the role of LLM-generated outputs as structured educational tools to enhance clinical reasoning training among nephrology fellows. Real-world clinical deployment requires additional validation, safety safeguards, and prospective outcome studies.
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