The IRMA Community
Newsletters
Research IRM
Click a keyword to search titles using our InfoSci-OnDemand powered search:
|
Systematic Literature Review: XAI and Clinical Decision Support
Abstract
Machine learning (ML) applications hold significant promise for innovation within healthcare; however, their full potential has not yet been realised, with limited reports of their clinical and cost benefits in clinical practice. This is due to complex clinical, ethical, and legal questions arising from the lack of understanding about how some ML models operate and come to make decisions. eXplainable AI (XAI) is an approach to help address this problem and make ML models understandable. This chapter reports on a systematic literature review investigating the use of XAI in healthcare within the last six years. Three research questions identified as issues in the literature were examined around how bias was dealt with, which XAI techniques were used, and how the applications were evaluated. Findings show that other than class imbalance and missing values, no other types of bias were accounted for in the shortlisted papers. There were no evaluations of the explainability outputs with clinicians and none of the shortlisted papers used an interventional study or RCT.
Related Content
Yu Bin, Xiao Zeyu, Dai Yinglong.
© 2024.
34 pages.
|
Liyin Wang, Yuting Cheng, Xueqing Fan, Anna Wang, Hansen Zhao.
© 2024.
21 pages.
|
Tao Zhang, Zaifa Xue, Zesheng Huo.
© 2024.
32 pages.
|
Dharmesh Dhabliya, Vivek Veeraiah, Sukhvinder Singh Dari, Jambi Ratna Raja Kumar, Ritika Dhabliya, Sabyasachi Pramanik, Ankur Gupta.
© 2024.
22 pages.
|
Yi Xu.
© 2024.
37 pages.
|
Chunmao Jiang.
© 2024.
22 pages.
|
Hatice Kübra Özensel, Burak Efe.
© 2024.
23 pages.
|
|
|