arXiv:2602.17531v2 Announce Type: replace Abstract: This position paper argues that current benchmarking practice in 12-lead ECG representation learning must be fixed to ensure progress is reliable and aligned with clinically meaningful objectives. The field has largely converged on three public multi-label benchmarks (PTB-XL, CPSC2018, CSN) dominated by arrhythmia and waveform-morphology labels, even though the ECG is known to encode substantially broader clinical information. We argue that downstream evaluation should expand to include an assessment of structural heart disease and patient-le
The proliferation of AI in medical diagnostics necessitates a robust and reliable evaluation framework to ensure models are clinically relevant and not just technically proficient.
This paper highlights a critical need to standardize AI benchmarking in healthcare, which directly impacts patient safety, trust in AI, and the effective integration of AI into clinical practice.
The focus on broader clinical information beyond arrhythmia and waveform-morphology suggests a recalibration of AI development and evaluation in cardiology, pushing for more comprehensive diagnostic capabilities.
- · AI ethicists
- · Medical AI researchers focused on broader diagnostics
- · Healthcare providers
- · Patients
- · AI developers using narrow benchmarks
- · Clinical AI products lacking broad utility
Increased pressure on AI developers to expand and diversify their evaluation datasets and methods for medical AI.
A potential shift in funding and research focus towards more comprehensive, clinically meaningful AI applications in healthcare.
Improved regulatory frameworks and greater public trust in the diagnostic capabilities of AI systems, leading to accelerated adoption.
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Read at arXiv cs.LG