AI-assisted vs. textbook-based vs. blended learning for acute abdomen diagnosis: a retrospective cohort study of emergency interns.

چکیده اصلی

BACKGROUND: Large language models (LLMs) are reshaping medical education. OBJECTIVE: To examine whether learning approach-AI-assisted, textbook-based, or blended-was associated with diagnostic accuracy for acute abdominal conditions among emergency interns. METHODS: We reviewed 720 clinical decisions by 72 emergency interns at a tertiary center over 12 months. Interns were classified into three groups: textbook-based (n = 27), AI-assisted (n = 21), and blended (n = 24). Propensity score matching with pair-stratified GEE addressed selection bias and clustering. RESULTS: Diagnostic accuracy was 73.0% (textbook), 82.4% (AI-assisted), and 87.1% (blended) (p < 0.001). Blended learning showed the largest advantage over textbook (adjusted OR = 3.21, 95% CI: 1.87-5.51, p < 0.001), followed by AI-assisted (adjusted OR = 1.68, 95% CI: 0.99-2.85, p = 0.053). Matched analyses confirmed the benefit for blended learning (p = 0.011). Subgroup analyses suggested larger effects for severe cases (OR = 4.92) and atypical presentations (OR = 3.85), with an E-value of 5.88 supporting robustness against unmeasured confounding. CONCLUSION: Blended learning combining AI tools with traditional resources showed the strongest association with diagnostic accuracy, suggesting that integrated rather than exclusive approaches may better support clinical reasoning in emergency training.

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