Holtercare-Bench - Diagnosis (Text): leaderboard
Holtercare-Bench (2026) asks multimodal models closed multiple-choice questions about long-term dynamic ECG (Holter) recordings of 13 to 24 hours from real hospital cases. The diagnosis task asks for the overall diagnosis of the long-term strip among similar arrhythmia options; the ECG lead is given as text values in millivolts. Score is accuracy in percent; higher is better.
Metric: Accuracy (%; multiple-choice closed QA on the overall diagnosis of the long-term strip among similar arrhythmia options; the ECG lead given as text, values in millivolts to three decimals, truncated where it exceeds the input limit; zero-shot on the held-out test cases of Holtercare-23K, 20 percent of 788 clinical Holter recordings of 13-24 hours). Source: arxiv.org. Saturation forecast: Around 2029. 13 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5 Mini | 48.03 | #348 |
| 2 | Gemini 3 Flash | 41.73 | #164 |
| 3 | MedGemma 1.5 4B | 33.46 | #2562 |
| 4 | Claude Haiku 4.5 | 28.35 | #568 |
| 5 | Phi-4 Mini Instruct | 24.8 | #6419 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=holtercare-bench-diagnosis-text · How It Works · Data refreshed daily, snapshot 2026-11-13.