Aug 2026· Schizophrenia bulletin· Vol 52 5· 0 citations
Medicine
TL;DR
Addressing confidence in deploying LLM-derived ratings as a scalable, multilingual tool for schizophrenia research and clinical practice would strengthen confidence in deploying LLM-derived ratings as a scalable, multilingual tool for schizophrenia research and clinical practice.
It is found that aggregate pairwise comparisons transfer strongly across evaluation settings, including from randomly generated tasks to clinically meaningful tasks and across datasets, and at the same time, clinically meaningful tasks exhibit greater task-method interaction.
Florent Pollet, Matthew B. A. McDermott· 1 citation· ⚡1
An empirical benchmark for evaluating clinical triage systems that assesses explanation quality alongside decision outcomes, and provides a reproducible, checkpoint-based evaluation pipeline and outline a roadmap for bias stress-testing, hallucination mitigation, and open benchmark release.
S. Marimuthu, Patricia L. Mabry, HealthPartners.Com· 0 citations
Blinded physician evaluation has been considered by many to be the gold standard for assessing clinical reasoning in large language models (LLMs). This is difficult to scale; thus, prior studies typically rely on small physician panels, often from a single institution or specialty, which both limits the scientific ques...
Thomas A. Buckley, Zahir Kanjee, Peter G. Brodeur et al.· 0 citations
Summary Artificial intelligence (AI) systems have quietly crossed an uncomfortable threshold: on a growing range of diagnostic tasks, they often match, and in some well-defined tasks even exceed, human performance. Evidence from large language models such as Med-PaLM and GPT-4, and deep learning systems in pathology, r...
C. Pohlkamp, T. Haferlach· EClinicalMedicine· 0 citations
Large language models (LLMs) are increasingly supporting complex mental-health decisions, which depend not only on factual evidence but also value-laden interpretations. We introduce a mixed-methods human-LLM auditing framework examining decision consistency, susceptibility to cognitive heuristics, declarative intellec...
Maciej Wodziński, Joanna Wodzińska, Kacper Dudzic et al.· 0 citations
Conversational AI increasingly answers patients' rehabilitation questions, but a fluent, well-liked answer can still be clinically unsafe, and preference-only evaluation hides this by collapsing many judgments into one winner. We ran a human-centered evaluation of the mRehab Advisory Agent with two cohorts in distinct...
Kritarth Dandapat, Tanmay Anishkumar Master, Anarghya Das et al.· Proceedings of the 4th Inter...· 0 citations
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