We were honored to host Dr. Nadia Brancati(NRCI), for a guest seminar on May 28th! 🇮🇹 She shared incredible insights from her cutting-edge work in Computer Vision, Machine Learning, and Medical Imaging. Thank you for an inspiring talk! #ComputerVision# #MedicalAI# #MachineLearning#

TL;DR: Clinical AI benchmarking has a core problem — real EHRs can't be shared and their labels aren't verifiable. This paper solves it with a fully synthetic hospital, and frontier models still fall short of top physicians. Title: Synthetic Hospital: An Open, Verifiable, Physician-Validated Longitudinal EHR Benchmark URL: Points 🏥 Built from medical education materials into 1,268 patients and 5,602 encounters, fully synthetic and PHI-free so it can be shared openly 🔗 Diagnoses, findings, and temporal relations are deterministically grounded in ICD-10-CM/SNOMED CT/LOINC, with labels derived mechanically from a knowledge graph 👨‍⚕️ Physicians distinguished synthetic from real charts at just 53% accuracy — essentially chance 📊 Across 10 models on 5 tasks, the best patient-diagnosis score was Kimi 2.5-thinking at 0.732 severity-weighted F1, matching average physician performance ⚠️ Still well below top physicians (0.89); every model missed roughly half the findings in summarization tasks 🔁 Swapping the generator model changed scores by ≤0.05, confirming it measures real clinical state, not generation artifacts Feels significant to finally have a benchmark that can measure clinical LLMs honestly, without a privacy tax. #MedicalAI# #LLMBenchmark#

medical ai keeps advancing, now you can take the bones out of a chest X-ray 🩻 @qure_ai released a model that splits a radiograph into soft tissue, bone and lungs, so the ribs stop hiding what's behind them ▶️ on Spaces