The Explainable Analytics for Exploring Misdiagnoses
Authors: 
Hao-Ting Pai, Wen-Cheng Chung, Xin-Hong Fang, Yu-Hsin Hsu, Shu-Ting HuangAuthors Info & Claims
Hao-Ting Pai, Wen-Cheng Chung, Xin-Hong Fang, Yu-Hsin Hsu, Shu-Ting HuangAuthors Info & ClaimsPages 238 - 243
Abstract
Current AI-driven methods in healthcare show significant limitations in addressing misdiagnoses, often leading to serious consequences. This work highlights the inadequacy of state-of-the-art AI in managing diagnostic errors through experiments with public real-world datasets, namely “Bone Marrow Transplant: Children” and “Breast Cancer Wisconsin (Original)”. We propose two novel scoring measures—Local-score and Global-score—to enhance the accuracy of Transparent Classification (TC). Experimental results indicate that Global-score improves recall but may reduce precision, whereas Local-score slightly lowers recall for better precision. These findings suggest that both Local-score and Global-score can effectively identify misdiagnoses, marking a substantial improvement over traditional AI methods. This research offers a new perspective in employing explainable AI to address the challenges of misdiagnoses, thereby improving patient care and medical diagnostic accuracy.
References
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Ibrahim, S. A., & Pronovost, P. J. (2021, September). Diagnostic errors, health disparities, and artificial intelligence: A combination for health or harm?. In JAMA Health Forum (Vol. 2, No. 9, pp. e212430-e212430). American Medical Association.
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- The Explainable Analytics for Exploring Misdiagnoses
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Published In
May 2024
349 pages
ISBN:9798400716874
DOI:10.1145/3673971
Copyright © 2024 ACM.
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Association for Computing Machinery
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Published: 09 September 2024
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ICMHI 2024
ICMHI 2024: 2024 8th International Conference on Medical and Health Informatics
May 17 - 19, 2024
Yokohama, Japan
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