CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity
<|reference_start|>CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity: 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.<|reference_end|>
@article{wu2024cardioai:,
    title={CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and
  Risk Detection of Cancer Treatment-Induced Cardiotoxicity},
    author={Siyi Wu, Weidan Cao, Shihan Fu, Bingsheng Yao, Ziqi Yang, Changchang
  Yin, Varun Mishra, Daniel Addison, Ping Zhang, Dakuo Wang},
    journal={arXiv preprint arXiv:2410.04592},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04592},
    primaryClass={cs.HC}
}