# XAI-Based Clinical Decision Support Systems: A Systematic Review
###### 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 processes. Non-knowledge-based CDSSs utilize vast amounts of data and algorithms to effectively make decisions; however, the deep learning black-box problem causes unreliable results. EXplainable Artificial Intelligence (XAI)-based CDSSs provide valid rationales and explainable results. These systems ensure trustworthiness and transparency by showing the recommendation and prediction result process using 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; introduces 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 CDSSs in the future. explainable AI; deep learning; clinical decision support system +
Footnote †: journal:Conversely, non-knowledge-based CDSSs provide decision-making by learning patterns found in past clinical information using machine learning or AI without rules. Non-knowledge-based CDSSs have been extensively studied in various medical areas [3; 4], dealing with issues such as hypertension, heart failure, and lung disease. These CDSSs are expected to be a breakthrough methodology that can reduce the cost of knowledge construction and provide personalized treatment. However, the black-box problem [5], in which explaining the process behind the results derived by AI models is not possible, makes it difficult to apply them in healthcare, where transparency is essential. Therefore, to utilize non-knowledge-based CDSSs, it is necessary to introduce robust clinical validation and evaluations or provide convincing evidence to support the results. In other words, rules in knowledge-based CDSSs are generated by structuring literature-based, practice-based, or patient-driven evidence, which is too costly for experts, and non-knowledge-based CDSSs suffer from the inability to explain model results, making them unusable in practice in the healthcare domain. EXplainable AI (XAI) [6] was proposed to explain the process of the results of AI-based systems. XAI is a set of processes and methods that allows human users to comprehend and trust the results and output created by machine learning algorithms. The main features of XAI techniques involve creating a suite of ML techniques that (1) produce more explainable models while maintaining a high level of learning performance (e.g., prediction accuracy) and (2) enable humans to understand, appropriately trust, and effectively manage the emerging generation of artificially intelligent partners. The differences in knowledge-, non-knowledge-, and XAI-based CDSSs are shown in Figure 1[7]. XAI can explain the process and rationale behind an AI model's decision in a manner that can be interpreted by the user. XAI technologies can be broadly categorized into feature-, model-, complexity-, and methodology-based technologies. XAI-based CDSSs provide high performance of AI models in the healthcare domain, where transparency must be ensured, along with explainability and interpretability, enabling the development of real-world healthcare services. In particular, XAI-based CDSSs can explain the rationale and reasoning behind AI-based model results in real-world clinical processes.