TY - GEN
T1 - CardioAI
T2 - 2025 CHI Conference on Human Factors in Computing Systems, CHI 2025
AU - Wu, Siyi
AU - Cao, Weidan
AU - Fu, Shihan
AU - Yao, Bingsheng
AU - Yang, Ziqi
AU - Yin, Changchang
AU - Mishra, Varun
AU - Addison, Daniel
AU - Zhang, Ping
AU - Wang, Dakuo
N1 - Publisher Copyright:
© 2025 Copyright held by the owner/author(s).
PY - 2025/4/26
Y1 - 2025/4/26
N2 - Despite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity (i.e., the various heart damages caused by cancer treatments) emerges as one major side effect. The clinical decision-making process of cardiotoxicity is challenging, as early symptoms may happen in non-clinical settings and are too subtle to be noticed until life-threatening events occur at a later stage; clinicians already have a high workload focusing on the cancer treatment, no additional effort to spare on the cardiotoxicity side effect. Our project starts with a participatory design study with 11 clinicians to understand their decision-making practices and their feedback on an initial design of an AI-based decision-support system. Based on their feedback, we then propose a multimodal AI system, CardioAI, that can integrate wearables data and voice assistant data to model a patient's cardiotoxicity risk to support clinicians' decision-making. We conclude our paper with a small-scale heuristic evaluation with four experts and the discussion of future design considerations.
AB - Despite recent advances in cancer treatments that prolong patients' lives, treatment-induced cardiotoxicity (i.e., the various heart damages caused by cancer treatments) emerges as one major side effect. The clinical decision-making process of cardiotoxicity is challenging, as early symptoms may happen in non-clinical settings and are too subtle to be noticed until life-threatening events occur at a later stage; clinicians already have a high workload focusing on the cancer treatment, no additional effort to spare on the cardiotoxicity side effect. Our project starts with a participatory design study with 11 clinicians to understand their decision-making practices and their feedback on an initial design of an AI-based decision-support system. Based on their feedback, we then propose a multimodal AI system, CardioAI, that can integrate wearables data and voice assistant data to model a patient's cardiotoxicity risk to support clinicians' decision-making. We conclude our paper with a small-scale heuristic evaluation with four experts and the discussion of future design considerations.
KW - Cancer treatment-induced cardiotoxicity
KW - Human-AI collaboration
KW - Large Language Models
KW - Multimodal AI system
UR - https://www.scopus.com/pages/publications/105005756928
U2 - 10.1145/3706598.3714272
DO - 10.1145/3706598.3714272
M3 - Conference contribution
AN - SCOPUS:105005756928
T3 - Conference on Human Factors in Computing Systems - Proceedings
BT - CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems
PB - Association for Computing Machinery
Y2 - 26 April 2025 through 1 May 2025
ER -