Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).

Loh, HW; Ooi, CP; Seoni, S; Barua, PD; Molinari, F; Acharya, UR

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).

Loh, HW Ooi, CP Seoni, S Barua, PD Molinari, F Acharya, UR

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Comput Methods Programs Biomed, 2022, 226, pp. 107161
Issue Date:: 2022-11

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Field	Value	Language
dc.contributor.author	Loh, HW
dc.contributor.author	Ooi, CP
dc.contributor.author	Seoni, S
dc.contributor.author	Barua, PD
dc.contributor.author	Molinari, F
dc.contributor.author	Acharya, UR
dc.date.accessioned	2023-06-28T05:52:21Z
dc.date.available	2022-09-25
dc.date.available	2023-06-28T05:52:21Z
dc.date.issued	2022-11
dc.identifier.citation	Comput Methods Programs Biomed, 2022, 226, pp. 107161
dc.identifier.issn	0169-2607
dc.identifier.issn	1872-7565
dc.identifier.uri	http://hdl.handle.net/10453/170948
dc.description.abstract	BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
dc.format	Print-Electronic
dc.language	eng
dc.publisher	Elsevier
dc.relation.ispartof	Comput Methods Programs Biomed
dc.relation.isbasedon	10.1016/j.cmpb.2022.107161
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0801 Artificial Intelligence and Image Processing, 0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering
dc.subject.classification	Medical Informatics
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Delivery of Health Care
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Delivery of Health Care
dc.subject.mesh	Humans
dc.subject.mesh	Artificial Intelligence
dc.subject.mesh	Delivery of Health Care
dc.title	Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).
dc.type	Journal Article
utslib.citation.volume	226
utslib.location.activity	Ireland
utslib.for	0801 Artificial Intelligence and Image Processing
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2023-06-28T05:52:20Z
pubs.publication-status	Published
pubs.volume	226

Abstract:

BACKGROUND AND OBJECTIVES: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS: Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS: In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION: We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.

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http://hdl.handle.net/10453/170948