CASE medical agents beat GPT-5.4 by 16% on longitudinal reasoning over UK Biobank MRIs
CASE-Bench · hf · 2026-10-12
Durham University and Alibaba DAMO Academy present CASE: role-specific clinical agents that seek evidence across longitudinal records and imaging, plus an agentic post-training framework for compact VLMs. A UK Biobank benchmark of 50,401 questions from 4,739 patients links each to patient-specific environments with baseline/follow-up MRIs. SFT on 14,734 frontier-model trajectories is followed by agentic RL. Their Qwen3-VL-8B agent beats GPT-5.4 and Claude Opus 4.8 by over 16% and 10% relative accuracy under matched conditions.
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