arXiv:2608. 09080v1 Announce Type: cross Abstract: Large Language Models (LLMs) have achieved strong performance in medical question answering and clinical reasoning tasks.
The paper introduces the first benchmark for evaluating confidence estimation in large language models during multi‑turn medical consultations, combining three types of medical data and an information sufficiency gradient to capture how confidence and correctness evolve as evidence accumulates. Experiments with 27 methods reveal that token‑level and consistency‑level confidence approaches are limited by medical data, and that medical reasoning must be judged on both diagnostic accuracy and information completeness. Building on these findings, the authors propose MedConf, a retrieval‑augmented, linguistically grounded self‑assessment framework that aligns patient information with supporting, missing, and contradictory relations, producing interpretable confidence estimates that outperform existing methods across multiple datasets and LLMs.
arXiv:2607. 13036v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for decision support in healthcare, but clinical evidence is often incomplete or evolving.
arXiv:2608. 03854v1 Announce Type: new Abstract: When decoder language models are used as classifiers, predicted class probabilities depend on implementation choices, including the prompt template, verbalizer (label-to-token mapping), and scoring rule, that are rarely treated as experimental variables.
arXiv:2607. 28788v1 Announce Type: new Abstract: Clinical diagnosis at hospital admission must be made rapidly from limited, incomplete evidence.
arXiv:2606. 19509v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored.
arXiv:2608.29582v1 Announce Type: cross Abstract: Current evaluations of large language models (LLMs) primarily focus on factual knowledge retrieval, overlooking the fundamental challenge of navigati...
The paper introduces ACTMED, a diagnostic framework that combines Bayesian Experimental Design with large language models to emulate real‑world clinical reasoning. ACTMED actively selects the most informative test at each step, using LLMs to simulate patient states and update beliefs without needing task‑specific training data. The authors evaluate the system on real datasets, demonstrating improvements in diagnostic accuracy, interpretability, and efficient resource use while keeping clinicians involved in the decision loop.
arXiv:2607. 17508v1 Announce Type: cross Abstract: We introduce Retrieval-Augmented Interpretable Learning (RAIL), a probabilistic meta-learning framework for zero-shot generation of task-specific interpretable models that synthesizes coefficient-space structure from natural-language task descriptions and a memory of previously learned task-specific predictors.
arXiv:2608. 16507v1 Announce Type: new Abstract: Due to the limited amount of information, modeling longitudinal rare-disease data can benefit from integrating clinical knowledge.
arXiv:2605.28740v2 Announce Type: replace-cross Abstract: As large language models are increasingly deployed for clinical text, ensuring they can reliably signal their own uncertainty becomes critica...
arXiv:2608. 12477v1 Announce Type: new Abstract: Clinical prediction models are often developed as if the outcome of interest were cleanly observed for every patient.