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AuthorRasheed, Khansa
AuthorQayyum, Adnan
AuthorGhaly, Mohammed
AuthorAl-Fuqaha, Ala
AuthorRazi, Adeel
AuthorQadir, Junaid
Available date2023-07-13T05:40:51Z
Publication Date2022
Publication NameComputers in Biology and Medicine
ResourceScopus
ISSN104825
URIhttp://dx.doi.org/10.1016/j.compbiomed.2022.106043
URIhttp://hdl.handle.net/10576/45570
AbstractWith the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various DL models is a roadblock to clinical utilization. Therefore, to gain the trust of clinicians and patients, we need to provide explanations about the decisions of models. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provided a comprehensive review of explainable and interpretable ML techniques for various healthcare applications. Along with highlighting security, safety, and robustness challenges that hinder the trustworthiness of ML, we also discussed the ethical issues arising because of the use of ML/DL for healthcare. We also describe how explainable and trustworthy ML can resolve all these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development. 2022 The Author(s)
SponsorThis publication was made possible by NPRP grant #[ 13S-0206-200273 ] from the Qatar National Research Fund (a member of Qatar Foundation). Open Access funding is provided by the Qatar National Library. The statements made herein are solely the responsibility of the authors.
Languageen
PublisherElsevier
SubjectExplainable machine learning
Healthcare
Interpretable machine learning
Trustworthiness
TitleExplainable, trustworthy, and ethical machine learning for healthcare: A survey
TypeArticle Review
Volume Number149
dc.accessType Open Access


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