ECG Mirage: Revealing and Mitigating the Underutilisation of ECGs in Vision-Language Models for Clinical Prediction· 揭示和缓解VLM在临床预测中ECG的不当使用
Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and electrocardiograms (ECGs). Vision--language models (VLMs) can jointly process these modalities, but strong predictive performance does not necessarily imply meaningful use of the correct patient's ECG. We term this failure mode ECG Mirage: apparent multimodal capability without useful dependence on patient-specific ECG information. We distinguish two forms: ECG neglect, where ECGs provide little predictive benefit, and ECG confusion, where matched ECGs outperform no-image inputs but not mismatched ECGs. To evaluate these behaviours, we compare predictions obtained with matched ECGs, outcome-discordant mismatched ECGs, and no-image inputs wh
揭示VLM在临床预测中对ECG信息的不当使用及其缓解方法。
- 核心方法
- 通过比较匹配ECG、结果不一致的非匹配ECG和无图像输入的预测效果来评估VLM的ECG利用情况，提出缓解措施。
- 适合谁读
- 研究者、工程师、临床医生
- 要解决的问题
- 现有VLM在临床预测中可能不对ECG信息进行有效利用，导致预测结果依赖性低或混淆。
- 关键实验
- 实验包括使用匹配ECG、结果不一致的非匹配ECG和无图像输入的数据集进行预测性能比较。
- 主要贡献
- 定义了ECG Mirage现象，提出了评估和缓解VLM在临床预测中不当使用ECG信息的方法。
- 意义与局限
- 有助于提高VLM在临床应用中的可靠性和解释性，但需进一步验证不同数据集上的效果。