Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in D

arXiv:2605.08445v1 Announce Type: new Abstract: AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes

arXiv:2605.11398v1 Announce Type: new Abstract: We introduce AcuityBench, a benchmark for evaluating whether language models identify the appropriate urgency of care from user

arXiv:2604.09596v1 Announce Type: new Abstract: Dermatologic diseases impose a large and growing global burden, affecting billions and substantially reducing quality of life.

arXiv:2604.27899v1 Announce Type: new Abstract: Understanding how human health changes over time, and why responses to interventions vary between individuals, remains a central

arXiv:2605.04454v1 Announce Type: new Abstract: Alignment evaluation in machine learning has largely become evaluation of models. Influential benchmarks score model outputs under

arXiv:2603.18008v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used for mental-health support; yet prevailing evaluation methods--fluency metrics,

arXiv:2604.05348v1 Announce Type: new Abstract: Hallucinations in medical large language models (LLMs) remain a safety-critical issue, particularly when available evidence is

arXiv:2603.29366v1 Announce Type: new Abstract: Prior authorization remains one of the most burdensome administrative processes in U.S. healthcare, consuming billions of dollars