Review
Version 1
Preserved in Portico This version is not peer-reviewed
XAI-based Clinical Decision Support System: A Systematic Review
Version 1
: Received: 11 June 2024 / Approved: 11 June 2024 / Online: 11 June 2024 (15:58:51 CEST)
How to cite: Kim, S.; Kim, D.; Kim, M.; Ko, H.; Jeong, O. XAI-based Clinical Decision Support System: A Systematic Review. Preprints 2024, 2024060721. https://doi.org/10.20944/preprints202406.0721.v1 Kim, S.; Kim, D.; Kim, M.; Ko, H.; Jeong, O. XAI-based Clinical Decision Support System: A Systematic Review. Preprints 2024, 2024060721. https://doi.org/10.20944/preprints202406.0721.v1
Abstract
With increasing electronic medical data and the development of artificial intelligence, Clinical Decision Support Systems (CDSSs) assist clinicians in diagnosis and prescription. Traditional knowledge-based CDSSs follow an accumulated medical knowledgebase and a predefined rule system, which clarifies the decision-making process; however, maintenance cost issues exist in the medical data quality control and standardization process. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively decide; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSS provides a valid rationale and explainable results. It ensures trustworthiness and transparency by showing the recommendation and prediction results process through explainable techniques. However, existing systems have limitations, such as the scope of data utilization and the lack of explanatory power of AI models. This study proposes a new XAI-based CDSS framework to address these issues; introduce resources, datasets, and models that can be utilized; and provides a foundation model to support decision-making in various disease domains. Finally, we propose future directions for CDSS technology and highlight societal issues that need to be addressed to emphasize the potential of CDSS in the future.
Keywords
Explainable AI; Deep learning; Clinical Decision Support System
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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