acePerformance of large language models in complex coronary artery disease decision-making and potential improvement strategy in a real-world cohort
Objective: Previous studies have demonstrated that large language models (LLMs) can achieve performance comparable to multidisciplinary heart teams (HTs) in coronary revascularisation decision-making but limited by population bias. We aimed to assess LLMs’ performance in complex coronary artery disease (CAD) decision-making using a real-world cohort and to explore the potential improvement strategy. Methods: Patients with stable CAD and angiographically confirmed three-vessel or left main disease randomly select…
Three predefined prompting strategies raised LLM agreement with heart-team revascularisation decisions from 47.0% to 79.3% in the same complex coronary artery disease cohort.
With a typical patient-style prompt, ChatGPT-4.1 agreed with heart-team decisions in only 47.0% of complex coronary artery disease cases and never identified cases needing further testing.
Even after prompting improvements, agreement with heart teams was not complete, and the authors emphasise that potential safety risk remains.
Evidence
- Peer-reviewedBMJ Health & Care Informatics2026-10-13
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Truvace Impact Record TRV-2026-1470, v1: “Performance of large language models in complex coronary artery disease decision-making and potential improvement strategy in a real-world cohort.” Truvace, 2026-10-15. /record/TRV-2026-1470 (accessed at citation time). sha256 ce8c377833b19356…
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