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Evaluates agentic LLM systems for generating breast cancer treatment recommendations using 72 clinical cases, finding that the best system (Claude Opus 4.8 with D&C+SA pipeline) achieved a global score of 0.594 but remains insufficient for unsupervised clinical use due to persistent errors.
This paper introduces iLENS, an interpretable LLM-guided mixture-of-experts framework for survival prediction and patient subtyping in Alzheimer's disease using neuroimaging data. The approach provides transparent, biologically grounded rationales for its routing decisions, bridging high-performance survival analysis with interpretable clinical decision support.
This paper introduces the Large Cancer Assistant (LCA), a model-agnostic orchestration framework for scalable clinical decision support in oncology that decouples multimodal data ingestion from AI inference using a 7-tuple architecture and Algorithmic Impermeability.
This paper introduces MMIR-TCM, a novel framework that integrates multimodal large language models with memory-augmented segmentation and retrieval-augmented generation to support Traditional Chinese Medicine clinical decision making, along with a new dataset MedTCM and evaluation metric TDEU.
This paper presents ClaMPAPP, a hybrid architecture that uses an LLM as an interface to extract features from clinical narratives, which are then passed to an XGBoost classifier for pediatric appendicitis diagnosis, demonstrating improved robustness and safety over end-to-end LLM baselines.
This paper presents an online adaptive clinical decision support AI system that integrates treatment effect estimation, digital twin simulation, and reinforcement learning to recommend treatments in a safe, clinician-supervised manner, validated on a synthetic simulator and the TCGA ovarian cancer dataset.
This paper presents VIBEMed, a multi-agent framework with a self-evolution mechanism and safety sandbox for robust clinical decision support, integrating specialized agents for diagnosis, treatment planning, and evolving clinical knowledge over time.
This survey reviews the role of knowledge graphs in medicine across five key domains—clinical decision support, disease prediction, health recommender systems, precision medicine, and medical question answering—discussing applications, challenges, and future directions.
This paper proposes a knowledge-enhanced visual diagnostic system for traditional Chinese medicine that uses a Neo4j knowledge graph, a four-stage symptom matching pipeline, and an information gain-driven proactive questioning strategy to improve transparency and interpretability. Results demonstrate significant improvements in diagnostic trust and reduced cognitive load.
This systematic scoping review examines three categories of large AI models in dental healthcare: language-generative models, discriminative vision foundation models, and dental-specific foundation models, analyzing 97 studies to show that general-purpose and domain-specific models play complementary roles, with integrated pipelines outperforming single-model approaches.
Proposes RAG4Outcome, a retrieval-augmented generation framework integrating multimodal clinical data (PET-CT reports, surgical records, follow-up notes) to improve prognostic prediction in chronic osteomyelitis, enhancing interpretability and clinical reliability.
This study evaluates how interactive dialogue with an LLM (via the MedSyn system) improves diagnostic accuracy for physicians in emergency care settings, showing significant gains for residents on difficult cases.
The article introduces OncoAgent, a dual-tier multi-agent framework designed for privacy-preserving clinical decision support in oncology. It details a system architecture that combines corrective RAG, a reflexion safety loop, and dual-tier QLoRA fine-tuning optimized for AMD hardware.
This paper introduces a stochastic causal representation learning framework to resolve the bias-precision paradox in personalized medicine, demonstrating improved accuracy and interpretability in ICU clinical decision support.
The author uses an AI agent to analyze 8 years of his mother's hypertension records, identifying morning surges and drug interactions that were missed during brief hospital visits, highlighting AI's role in bridging gaps in chronic care continuity.
Google DeepMind announces an AI co-clinician research initiative aimed at improving healthcare delivery through 'triadic care,' where AI agents assist patients under physician supervision. The system demonstrated high accuracy and zero critical errors in a study of primary care queries, outperforming existing evidence synthesis tools.