2023
DOI: 10.1109/rbme.2022.3185953
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Explainable Artificial Intelligence Methods in Combating Pandemics: A Systematic Review

Abstract: Despite the myriad peer-reviewed papers demonstrating novel Artificial Intelligence (AI)-based solutions to COVID-19 challenges during the pandemic, few have made a significant clinical impact, especially in diagnosis and disease precision staging. One major cause for such low impact is the lack of model transparency, significantly limiting the AI adoption in real clinical practice. To solve this problem, AI models need to be explained to users. Thus, we have conducted a comprehensive study of Explainable Arti… Show more

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Cited by 40 publications
(4 citation statements)
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References 122 publications
(198 reference statements)
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“…Conversely, an explainable AI may increase the trust and acceptance of physicians and patients towards AI, reduce risks in healthcare, and is regulatory compliance of healthcare providers 14,15 . To achieve AI explainability in the medical field, many previous studies used a novel technique named LIME (Linear Interpretable Model-Agnostic Explanations) 16,17 .…”
Section: Introductionmentioning
confidence: 99%
“…Conversely, an explainable AI may increase the trust and acceptance of physicians and patients towards AI, reduce risks in healthcare, and is regulatory compliance of healthcare providers 14,15 . To achieve AI explainability in the medical field, many previous studies used a novel technique named LIME (Linear Interpretable Model-Agnostic Explanations) 16,17 .…”
Section: Introductionmentioning
confidence: 99%
“…Explainable AI (xAI) solutions for biomedical domain are attracting increasing scientific interest 36 . Emerging applications include drug discoveries 37 , cancer diagnosis 38 , microbiome studies 39 and clinical decision support systems in pandemics 40 . We believe that research community working on automatic pollen classification would highly benefit as well from xAI solutions providing insights into how classifiers make decisions.…”
Section: Related Workmentioning
confidence: 99%
“…Explainable AI (xAI) solutions for biomedical domain is attracting increasing scientific interest 28 . Emerging applications include drug discoveries 29 , cancer diagnosis 30 , microbiome studies 31 and clinical decision support systems in pandemics 32 . We believe that research community working on automatic pollen classification would highly benefit as well from xAI solutions providing insights into how classifiers make decisions.…”
Section: Introductionmentioning
confidence: 99%