arXiv:2602.04279v3 Announce Type: replace Abstract: Electrocardiography (ECG) serves as an indispensable diagnostic tool in clinical practice, yet existing multimodal large language models (MLLMs) remain unreliable for ECG interpretation, often producing plausible but clinically incorrect analyses. To address this, we propose ECG-R1, the first reasoning ECG MLLM designed for reliable ECG interpretation via three innovations. First, we construct the interpretation corpus using \textit{Protocol-Guided Instruction Data Generation}, grounding interpretation in measurable ECG features and monograph
The proliferation of more capable MLLMs highlights their current unreliability in critical medical applications, pushing researchers to develop more robust, application-specific models like ECG-R1.
This development addresses a key limitation of existing MLLMs in high-stakes fields like medicine, potentially enabling more accurate and trustworthy AI diagnostics, which impacts healthcare efficiency and patient outcomes.
The focus shifts from general-purpose MLLMs to specialized, protocol-guided systems capable of reliable interpretation in specific medical domains, moving AI diagnostic tools closer to clinical utility.
- · Healthcare sector
- · AI diagnostic developers
- · Patients
- · General-purpose MLLMs in clinical settings (without specialization)
Improved accuracy and reliability of AI-powered ECG interpretation in clinical settings.
Increased adoption of AI tools in medical diagnostics as reliability concerns are mitigated, potentially reducing physician workload.
The methodology of 'Protocol-Guided Instruction Data Generation' could become a standard for developing reliable AI across other specialized, high-stakes domains.
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Read at arXiv cs.CL