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arXiv:2609.35549v2 Announce Type: replace Abstract: Rare-disease diagnosis is a long-tail reasoning problem: phenotypes are incomplete, individual disorders are sparsely documented, and relevant evid...
arXiv:2608. 12138v1 Announce Type: cross Abstract: General-purpose large language models (LLMs) have recently been reported to match or exceed specialized clinical AI tools on medical benchmarks, but such comparisons draw on a narrow set of systems and on benchmarks developed largely in high-income settings.
arXiv:2607. 24838v1 Announce Type: cross Abstract: In medical multiple-choice question answering (MCQA), Retrieval-Augmented Generation (RAG) can supplement the domain knowledge of language models (LMs).
arXiv:2606. 15931v1 Announce Type: cross Abstract: Historical medical archives and traditional medicines hold immense potential for drug discovery and remain a primary source for current drug development.
The paper benchmarks five large language models—GPT‑5.1, Gemini 2.0 Pro, Llama 3, Qwen, and BioGPT—across five heterogeneous knowledge graphs in biomedical and cognitive neuroscience. Using a multitier query dataset, it evaluates multi‑hop reasoning and factual accuracy under graph‑grounded and graph‑agnostic conditions, finding that general‑purpose models outperform domain‑specific ones on complex reasoning tasks and that graph grounding consistently reduces hallucinations. The study offers practical guidance for selecting LLM‑KG integration strategies and discusses hybrid neurosymbolic architectures for applications such as personalized medicine and explainable AI.
arXiv:2608. 15382v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly proposed for healthcare decision support, but their evaluations still reward single-answer accuracy rather than reasoning about interventions, mechanisms, harms, evidence, and uncertainty.