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ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction

Research Medical/Healthcare AI

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TL;DR - This paper identifies “ECG Mirage,” where vision-language models appear effective at clinical prediction but fail to meaningfully use the correct patient’s ECG. Visual prompt tuning improves both predictive performance and reliance on patient-specific ECG information without modifying the VLM backbone.

  • Tests compare matched ECGs, outcome-discordant mismatched ECGs, and text-only inputs while keeping clinical context and targets fixed.
  • Across four VLMs on MDS-ED, matched ECGs provide no consistent advantage for predicting ICU admission or clinical deterioration.
  • Supervised learning followed by conditional direct preference optimization of restricted visual prompts yields 70.6% balanced accuracy for ICU admission and 67.5% for deterioration.
  • The tuned models increase matched-versus-mismatched performance gaps to roughly 16.5 and 5.5 percentage points for the two tasks, respectively.

Sources (1)

ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction

arXiv cs.AI Jinning Liang, Mingcheng Zhu, Tingting Zhu 2026-09-18 arXiv:2609.21755
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-26 14:14:54.050656 UTC

TL;DR - This paper identifies “ECG Mirage,” where vision-language models appear effective at clinical prediction but fail to meaningfully use the correct patient’s ECG. Visual prompt tuning improves both predictive performance and reliance on patient-specific ECG information without modifying the VLM backbone.

  • Tests compare matched ECGs, outcome-discordant mismatched ECGs, and text-only inputs while keeping clinical context and targets fixed.
  • Across four VLMs on MDS-ED, matched ECGs provide no consistent advantage for predicting ICU admission or clinical deterioration.
  • Supervised learning followed by conditional direct preference optimization of restricted visual prompts yields 70.6% balanced accuracy for ICU admission and 67.5% for deterioration.
  • The tuned models increase matched-versus-mismatched performance gaps to roughly 16.5 and 5.5 percentage points for the two tasks, respectively.
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