Radiology's Last Exam (RadLE): Benchmarking Frontier Multimodal AI Against Human Experts and a Taxonomy of Visual Reasoning Errors in Radiology
Authors
Suvrankar Datta
Divya Buchireddygari
Lakshmi Vennela Chowdary Kaza
Mrudula Bhalke
Kautik Singh
Ayush Pandey
Sonit Sai Vasipalli
Upasana Karnwal
Hakikat Bir Singh Bhatti
Bhavya Ratan Maroo
Sanjana Hebbar
Rahul Joseph
Gurkawal Kaur
Devyani Singh
Akhil V
Dheeksha Devasya Shama Prasad
Nishtha Mahajan
Ayinaparthi Arisha
Rajesh Vanagundi
Reet Nandy
Kartik Vuthoo
Snigdhaa Rajvanshi
Nikhileswar Kondaveeti
Suyash Gunjal
Rishabh Jain
Rajat Jain
Anurag Agrawal
Abstract
Generalist multimodal AI systems such as large language models (LLMs) and vision language models (VLMs) are increasingly accessed by clinicians and patients alike for medical image interpretation through widely available consumer-facing chatbots. Most evaluations claiming expert level performance are on public datasets containing common pathologies. Rigorous evaluation of frontier models on difficult diagnostic cases remains limited. We developed a pilot benchmark of 50 expert-level "spot diagnosis" cases across multiple imaging modalities to evaluate the performance of frontier AI models against board-certified radiologists and radiology trainees. To mirror real-world usage, the reasoning modes of five popular frontier AI models were tested through their native web interfaces, viz. OpenAI o3, OpenAI GPT-5, Gemini 2.5 Pro, Grok-4, and Claude Opus 4.1. Accuracy was scored by blinded experts, and reproducibility was assessed across three independent runs. GPT-5 was additionally evaluated across various reasoning modes. Reasoning quality errors were assessed and a taxonomy of visual reasoning errors was defined. Board-certified radiologists achieved the highest diagnostic accuracy (83%), outperforming trainees (45%) and all AI models (best performance shown by GPT-5: 30%). Reliability was substantial for GPT-5 and o3, moderate for Gemini 2.5 Pro and Grok-4, and poor for Claude Opus 4.1. These findings demonstrate that advanced frontier models fall far short of radiologists in challenging diagnostic cases. Our benchmark highlights the present limitations of generalist AI in medical imaging and cautions against unsupervised clinical use. We also provide a qualitative analysis of reasoning traces and propose a practical taxonomy of visual reasoning errors by AI models for better understanding their failure modes, informing evaluation standards and guiding more robust model development.
