[2603.19517] ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.19517 (cs) [Submitted on 19 Mar 2026] Title:ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding Authors:Oishi Banerjee, Sung Eun Kim, Alexandra N. Willauer, Julius M. Kernbach, Abeer Rihan Alomaish, Reema Abdulwahab S. Alghamdi, Hassan Rayhan Alomaish, Mohammed Baharoon, Xiaoman Zhang, Julian Nicolas Acosta, Christine Zhou, Pranav Rajpurkar View a PDF of the paper titled ReXInTheWild: A Unified Benchmark for Medical Photograph Understanding, by Oishi Banerjee and 11 other authors View PDF HTML (experimental) Abstract:Everyday photographs taken with ordinary cameras are already widely used in telemedicine and other online health conversations, yet no comprehensive benchmark evaluates whether vision-language models can interpret their medical content. Analyzing these images requires both fine-grained natural image understanding and domain-specific medical reasoning, a combination that challenges both general-purpose and specialized models. We introduce ReXInTheWild, a benchmark of 955 clinician-verified multiple-choice questions spanning seven clinical topics across 484 photographs sourced from the biomedical literature. When evaluated on ReXInTheWild, leading multimodal large language models show substantial performance variation: Gemini-3 achieves 78% accuracy, followed by Claude Opus 4.5 (72%) and GPT-5 (68%), while the medical specialist model MedGemma reaches only 37%. A systematic ...