arXiv AI October 31, 2026 By Jungwoo Oh, Hyunseung Chung, Junhee Lee, Min-Gyu Kim, Hangyul Yoon, Ki Seong Lee, Youngchae Lee, Muhan Yeo, Edward Choi

ECG-Reasoning-Benchmark: A Benchmark for Evaluating Clinical Reasoning Capabilities in ECG Interpretation

Read the original on arXiv AI →

The ECG-Reasoning-Benchmark is a new multi‑turn evaluation framework with over 6,400 samples that tests step‑by‑step reasoning for 17 core ECG diagnoses. It shows that state‑of‑the‑art multimodal large language models can retrieve medical knowledge but struggle to maintain a complete reasoning chain and to link ECG findings to visual evidence. The benchmark highlights a gap in visual grounding and motivates the development of reasoning‑centric medical AI.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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Diagnosing as Cardiologists Do: ECG Agents with Doctor-Grounded Priors for Clinical Reasoning Across Diseases and Populations

Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence. Whether this expert reading process can serve as an effective prior for ECG agents remains unclear.

agents computer-vision fine-tuning multimodal benchmarks safety

arXiv Machine Learning

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By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert

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Jul 28

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arXiv Computation and Language

Sep 1

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By Mohammadsina Hassannia, Matthew A. Reyna, Reza Sameni

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