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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.