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

• Record Type: Journal Article
• Authors: Loh, Hui Wen; Ooi, Chui Ping; Seoni, Silvia; Barua, Prabal Datta; Molinari, Filippo; Acharya, U Rajendra
• Is Part Of: Computer methods and programs in biomedicine. Volume 226(2022)
• Publication Date: 2022-11
• Subjects: Explainable artificial intelligence (XAI) -- Deep learning -- Machine learning -- PRISMA -- SHAP -- LIME -- GradCAM -- LRP -- EBM -- CBR -- Rule-based -- Expert system -- Saliency map -- Attention mechanism -- Healthcare; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.107161
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Examined studies that used the explainable artificial intelligence (XAI) technique for AI model in healthcare applications. As a result, three major healthcare datasets were identified: clinical features, text, and high-dimensional data. This review also included optimal model performance of Machine Learning (ML) and Deep Learning (DL) models for XAI. Areas that require more attention, like XAI for biosignal abnormalities and clinical note interpretation, were identified. A reliable AI model for healthcare applications should ideally be both high performing and produce interpretable results. 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.