The paper investigates how to incorporate biomedical knowledge graphs (KGs) into large language models (LLMs) for clinical diagnosis. It evaluates five KG task formulations, three training paradigms, two KGs, and three base LLMs, finding that all paradigms outperform a non‑finetuned baseline but differ in knowledge transfer behavior. Introducing Gradient Intervention Density (GID) and Gradient Distortion (GD) metrics, the study identifies a ‘surgical alignment’ regime—sparse, localized updates achieved by KG‑judgment training with KL regularization—that improves reasoning quality even when in‑domain accuracy is lower than task‑specific supervised fine‑tuning.
arXiv:2608.24121v1 Announce Type: new Abstract: Radiology report generation (RRG) has recently benefited from large language models, which substantially improve report fluency. However, clinically fa...
arXiv:2510. 17532v2 Announce Type: replace-cross Abstract: Predicting cancer treatment outcomes requires models that are both accurate and interpretable, particularly in the presence of heterogeneous clinical data.
arXiv:2606.22419v3 Announce Type: replace Abstract: A recent Nature Medicine study reports that general-purpose frontier LLMs outperform specialized retrieval-augmented clinical tools on medical benc...
arXiv:2609.38543v1 Announce Type: new Abstract: Constantly evolving real-world knowledge necessitates models to be updated continuously. Especially in medicine, as clinical evidence changes over time...
arXiv:2606. 04922v1 Announce Type: cross Abstract: Current prompt-based and adapter-based tuning of vision-language models (VLMs) is attractive for medical imaging, where clinical data sensitivity favors frozen backbones and annotations are limited.
arXiv:2606. 12169v1 Announce Type: cross Abstract: High-stakes clinical use of large vision-language models (LVLMs) requires reasoning that is grounded in visual evidence and clinical knowledge, not just correct final answers.
arXiv:2606. 11675v1 Announce Type: new Abstract: Diagnosing pulmonary diseases requires integrating heterogeneous evidence amid phenotypic variability and cross-disease overlap.
arXiv:2608.22622v1 Announce Type: cross Abstract: Clinical decision-making relies on identifying relevant patient information to guide diagnosis and treatment, a challenge that is especially difficul...
arXiv:2505. 14107v5 Announce Type: replace-cross Abstract: The emergence of groundbreaking large language models capable of performing complex reasoning tasks holds significant promise for addressing various scientific challenges, including those arising in complex clinical scenarios.
The paper introduces Efficient Multi-Granularity Knowledge Transfer (EMGKT) for radiology report generation, aiming to improve medical knowledge extraction from X‑ray images. EMGKT encodes global knowledge via a medical vision‑language model and employs Fine‑Grained Knowledge Distillation (FGKD) to learn fine‑level embeddings without extra priors during inference. Additionally, a mixture of disease‑diagnosis expert classifiers is used to further enhance knowledge extraction, and the method is shown to be effective and transferable across datasets and baselines.
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:2511.21075v4 Announce Type: replace-cross Abstract: Engineering LLMs to accelerate life sciences research requires a robust alignment with biomedical knowledge. We observe that biomedical text...