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.

The rapid development of large language models (LLMs) has accelerated research into applying artificial intelligence (AI) to domains such as medical question answering and clinical decision support. However, LLMs face substantial limitations in medical contexts due to challenges in understanding specialized terminology, complex contextual information, hallucination issues (i.e., generating incorrect responses), and the black-box nature of their reasoning processes. To address these issues, methods like retrieval-augmented generation (RAG) and its graph-based variant, GraphRAG, have been proposed to incorporate external knowledge into LLMs. Nonetheless, these approaches often rely heavily on external resources and increase system complexity. In this study, we introduce MedSumGraph, a medical question-answering system that enhances GraphRAG by integrating structured medical knowledge summaries and optimized prompt designs. Our method enables LLMs to better interpret domain-specific knowledge without requiring additional training, and it enhances the reliability and interpretability of responses by directly embedding factual evidence and graph-based reasoning into the generation process. MedSumGraph achieves competitive performance on two out of eight multiple-choice medical QA benchmarks, including MedQA (USMLE), outperforming closed-source LLMs and domain-specific foundation models. Moreover, it generalizes effectively to open-domain QA tasks, yielding significant gains in reasoning over common knowledge and evaluating the truthfulness of answers. These findings demonstrate the potential of structured summarization and graph-based reasoning in enhancing the trustworthiness and versatility of LLM-driven medical AI systems.

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.

In intensive care units, large-scale clinical time-series data are continuously accumulated through electronic medical records and bedside monitoring systems. However, direct utilization of such data for clinical decision-making remains challenging due to irregular sampling, pervasive missingness, unstructured diagnostic information, and incomplete ICD labeling. Automated ICD coding constitutes an extreme multi-class classification problem with thousands of long-tailed categories, while intervention prediction tasks, such as mechanical ventilation management, involve rare transition events and severe class imbalance. To address these challenges, we propose CAGE, a hierarchical Clinical Decision Support System framework that integrates diagnosis, time-series signals, and intervention prediction. The framework first infers admission-level diagnostic context using a partial-label Automated ICD Coding module that combines DCNv2 with an Adaptive CLPL loss, producing probability-weighted diagnostic embeddings. These embeddings are subsequently fused with ICU time-series tensors and processed by a multi-branch Temporal Convolutional Network equipped with an ICD-conditioned gating mechanism to predict future ventilation state transitions. The experimental results demonstrate that DCNv2 achieves consistent superiority across all hit@k and probability concentration metrics for ICD coding. For intervention prediction, the proposed method substantially outperforms existing baselines, achieving a Macro-AUC of 98.2, Macro-AUPRC of 77.4, and F1-score of 79.4. These findings indicate that reinjecting diagnostic context as a conditioning variable, together with imbalance-aware loss design, effectively enhances rare-event detection and improves the practical applicability of clinical decision support systems.

Continual learning (CL) aims to learn new tasks sequentially while retaining prior knowledge. Although recent mixture-of-experts (MoE) architectures mitigate catastrophic forgetting by reducing global parameter sharing, they introduce a new bottleneck: expert-level interference. Because multiple tasks inevitably share a subset of experts, these shared parameters are repeatedly exposed to heterogeneous task updates, which may contribute to the degradation of previously encoded knowledge. To tackle this, we propose CARE-MoE (Conflict-Aware Routing-Guided Expert Mixture-of-Experts), a unified framework designed to limit repeated expert sharing, characterize expert utilization, and regulate expert-level updates. First, CARE-MoE aims to limit excessive expert sharing via dual-granularity routing, which leverages both task-level structural and input-level semantic information to distribute representations effectively. Second, rather than using routing merely for expert selection, it diagnoses highly exposed experts via multi-dimensional routing signal aggregation, quantifying expert importance through selection frequency, cross-task overlap, and routing consistency. Finally, conflict-aware expert stabilization translates this diagnosis into targeted optimization, selectively directing preservation updates to the routing parameters associated with heavily shared experts and modulating their strength according to the directional agreement between adaptation and preservation, while allowing task-specific experts to flexibly adapt. Extensive experiments on standard and long-sequence CL benchmarks show that CARE-MoE achieves higher overall performance than the results reported for existing methods. In-depth analyses further indicate that our framework bridges routing behavior with parameter optimization, achieving a favorable balance between stability and plasticity in long-term continual learning.

Background/Objectives: Postoperative pulmonary complications (PPCs) following abdominal surgery are associated with prolonged hospitalization, delayed recovery, and increased mortality. Because nursing surveillance is essential for early detection and timely intervention, this study aimed to develop a holistic nursing surveillance decision support system integrating PPC risk prediction with structured nursing action recommendations. Methods: In this retrospective cohort study, electronic medical record (EMR) data from approximately 6900 adult patients who underwent abdominal surgery at a single institution between January 2015 and September 2023 were analyzed. The study protocol was approved by the Institutional Review Board, and the requirement for informed consent was waived because of the retrospective study design. PPC risk was predicted using a tabular multilayer perceptron (MLP) encoder with SHapley Additive exPlanations (SHAP)-based feature weighting and a random forest classification head optimized via Optuna. Class imbalance was addressed using weighted sampling, class weighting in BCE(Binary Cross Entropy) With Logits Loss, and decision-threshold optimization. For clinical decision support, a large language model generated structured nursing surveillance recommendations in an action–evidence–rationale JSON format and was aligned through supervised fine-tuning (SFT) using human-evaluated cases. Results: The prediction model achieved an AUROC of 0.810, with an accuracy of 0.811, precision of 0.547, and recall of 0.545. In expert evaluation, the SFT-aligned model improved recommendation quality, reducing incorrect nursing actions from 19.3% to 8.0%. Conclusions: The proposed system demonstrates the feasibility of an end-to-end nursing surveillance decision support framework linking PPC risk prediction with structured clinical recommendations. The findings suggest its potential to support more accurate risk prediction and more actionable nursing surveillance for patients undergoing abdominal surgery.