Explainable AI for Healthcare 5.0: Opportunities and Challenges
نویسندگان
چکیده
In the healthcare domain, a transformative shift is envisioned towards Healthcare 5.0. It expands operational boundaries of 4.0 and leverages patient-centric digital wellness. 5.0 focuses on real-time patient monitoring, ambient control wellness, privacy compliance through assisted technologies like artificial intelligence (AI), Internet-of-Things (IoT), big data, networking channels. However, procedures, verifiability prediction models, resilience, lack ethical regulatory frameworks are potential hindrances to realization Recently, explainable AI (EXAI) has been disruptive trend in that explainability traditional models by leveraging decision-making outputs. The factor opens new opportunities black-box brings confidence stakeholders interpret machine learning (ML) deep (DL) models. EXAI focused improving clinical health practices transparency predictive analysis, which crucial domain. Recent surveys have not significantly data analysis interpretation lowers its practical deployment opportunities. Owing gap, proposed survey explicitly details requirements 5.0, collection process. Based review method presented research questions, systematically, article unfolds architecture presents an ensemble computerized tomography (CT) image classification segmentation A solution taxonomy proposed, challenges presented. supported case study electrocardiogram (ECG) monitoring preserves local via federated (FL) for metric validation. case-study experimental proves efficacy setups envisions real-life model deployments wide range applications.
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2022
ISSN: ['2169-3536']
DOI: https://doi.org/10.1109/access.2022.3197671