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TRV-2026-0235Certified recordPeer-reviewed

Medical student reliance on artificial intelligence in nephrology education

Background Large language models such as ChatGPT are increasingly used in medical education and may influence student learning and decision-making. Despite strong performance on factual recall, limitations in clinical reasoning raise concerns about how learners engage with artificial intelligence (AI)-generated recommendations, particularly in challenging domains such as renal physiology, where foundational understanding underpins clinical application. Methods Fifty-seven first-year medical students completed 24…

Health · The Trace — both readings · certified 2026-07-17 · v1 · article view · machine-readable

Current reading — gain

First-year medical students showed a modest net improvement in accuracy after reviewing ChatGPT-generated answers, because incorrect-to-correct changes exceeded correct-to-incorrect changes.

Current reading — problem

First-year medical students changed answers to match ChatGPT in 22.3% of cases, with greater reliance on foundational than clinical questions, indicating context-dependent overreliance risk.

What this doesn’t fix

Findings are limited to a single case-based learning session with first-year students answering a small set of nephrology questions, constraining generalizability to other learners and settings.

Evidence

Reader signal

How should this claim be treated?

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Truvace Impact Record TRV-2026-0235, v1: “Medical student reliance on artificial intelligence in nephrology education.” Truvace, 2026-07-17. /record/TRV-2026-0235 (accessed at citation time). sha256 642ff36003bf0ebc…

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