CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909519092645888 |
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| author | Wu, Siyi Cao, Weidan Fu, Shihan Yao, Bingsheng Yang, Ziqi Yin, Changchang Mishra, Varun Addison, Daniel Zhang, Ping Wang, Dakuo |
| author_facet | Wu, Siyi Cao, Weidan Fu, Shihan Yao, Bingsheng Yang, Ziqi Yin, Changchang Mishra, Varun Addison, Daniel Zhang, Ping Wang, Dakuo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_04592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity Wu, Siyi Cao, Weidan Fu, Shihan Yao, Bingsheng Yang, Ziqi Yin, Changchang Mishra, Varun Addison, Daniel Zhang, Ping Wang, Dakuo Human-Computer Interaction 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. |
| title | CardioAI: A Multimodal AI-based System to Support Symptom Monitoring and Risk Detection of Cancer Treatment-Induced Cardiotoxicity |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2410.04592 |