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CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity by Wu, Siyi; Cao, Weidan; Fu, Shihan; Yao, Bingsheng; Yang, Ziqi; Yin, Changchang; Mishra, Varun; Addison, Daniel; Zhang, Ping; Wang, Dakuo is a scholarly article available to read on EtoBox.

What is CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity about?

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.

Author

Wu, Siyi; Cao, Weidan; Fu, Shihan; Yao, Bingsheng; Yang, Ziqi; Yin, Changchang; Mishra, Varun; Addison, Daniel; Zhang, Ping; Wang, Dakuo

Published

2024

Language

EN