MedGemma: Our most capable open models for health AI development

Saving lives with AI health coaching

Healthify collaborates with OpenAI to improve millions of lives with sustainable weight loss.

Introducing HealthBench

HealthBench is a new evaluation benchmark for AI in healthcare which evaluates models in realistic scenarios. Built with input from 250+ physicians, it aims to provide a shared standard for model performance and safety in health.

Towards Next-Generation Healthcare: A Survey of Medical Embodied AI for Perception, Decision-Making, and Action

arXiv:2606. 15647v1 Announce Type: new Abstract: Foundation models have demonstrated impressive performance in enhancing healthcare efficiency across a wide range of medical applications.

Pioneering an AI clinical copilot with Penda Health

OpenAI and Penda Health debut an AI clinical copilot that cuts diagnostic errors by 16% in real-world use—offering a new path for safe, effective AI in healthcare.

Patient-centered data science: an integrative framework for evaluating and predicting clinical outcomes in the digital health era

arXiv:2408. 02677v2 Announce Type: replace-cross Abstract: This study proposes a novel, integrative framework for patient-centered data science in the digital health era.

Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies

arXiv:2606. 16721v1 Announce Type: new Abstract: Medical diagnosis and treatment are dynamic processes in which patient states evolve over time and clinical interventions alter future outcomes.

Introducing HUGS - Scale your AI with Open Models

Enabling a new model for healthcare with AI co-clinician

Researching the path to AI-augmented care and development of an AI co-clinician.

MedUAG: Unified Understanding and Generation for Medical Multimodal Models

The paper introduces MedUAG, a unified medical multimodal model that supports both understanding and generation tasks. It presents MedUAGCorpus, the largest dataset of over 6 million instances across 14 imaging modalities, and MedUAGBench, a benchmark covering 12 diverse generation tasks with standardized protocols. Experiments show that MedUAG performs strongly across many medical understanding and generation tasks, setting a competitive baseline for future medical multimodal systems.

Using AI to help physicians diagnose rare genetic diseases affecting children

Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.

ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages

arXiv:2606. 13572v1 Announce Type: cross Abstract: Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios.

ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages

Multimodal Large Language Models (MLLMs) have shown promising reasoning capabilities in general domains, yet their performance remains limited in specialized settings such as healthcare, especially in multilingual and low-resource scenarios. This gap is critical in regions like rural India, where patients often express complex medical queries in native Indic languages and rely on multimodal inputs such as medical images.