Despite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity remains one severe side effect. The clinical decision-making of cardiotoxicity is challenging, as non-clinical symptoms can be missed until life-threatening events occur at a later stage, and clinicians already have a high workload centered on the treatment, not the side effects. Our project starts with a participatory design study with 11 clinicians to understand their practices and needs; then we build a multimodal AI system, CardioAI, that integrates wearables and LLM-powered voice assistants to monitor multimodal non-clinical symptoms. Also, the system includes an explainable risk prediction module that can generate cardiotoxicity risk scores and summaries as explanations to support clinicians' decision-making. We conducted a heuristic evaluation with four clinical experts and found that they all believe CardioAI integrates well into their workflow, reduces their information overload, and enables them to make more informed decisions.

翻译：尽管近期癌症治疗手段的进步延长了患者生存期，但治疗诱发的心脏毒性仍是严重的副作用之一。心脏毒性的临床决策具有挑战性：非临床症状可能被忽视，直至晚期发生危及生命的事件；而临床医生的工作重心已高度集中于治疗本身，无暇顾及副作用监测。本项目首先通过参与式设计研究访谈了11位临床医生，以理解其工作实践与需求；随后构建了多模态人工智能系统CardioAI，该系统整合可穿戴设备与基于大语言模型的语音助手，用于监测多模态非临床症状。此外，系统包含可解释的风险预测模块，能够生成心脏毒性风险评分及解释性摘要，以支持临床医生的决策。我们邀请四位临床专家进行启发式评估，结果显示所有专家均认为CardioAI能良好融入其工作流程，减轻信息过载，并帮助其做出更明智的临床决策。