Abstract
Medical imaging is essential for diagnosing disease, guiding treatment and monitoring patients over time. Although artificial intelligence has advanced image interpretation, existing medical models often address restricted tasks, while general-purpose proprietary models lack access to private clinical imaging required to achieve medical expertise. Developing models that can interpret complex examinations and communicate their findings in the context of clinical decisions remains a major challenge. Here, we show that Neuro-VL, a vision-language model trained on nearly one million neuroimaging studies from routine clinical care, can interpret complete imaging examinations and engage in natural language conversations about their findings. Across internal and international datasets, Neuro-VL outperformed frontier general-purpose and specialized medical models in diagnosing, localizing, and describing abnormalities, as well as generating comprehensive radiology reports. The model also compared serial examinations to assess disease progression and more reliably resisted misleading clinical assumptions than leading frontier models from OpenAI and Google. These results extend health system learning beyond individual predictions to interactive image interpretation and longitudinal disease monitoring. They demonstrate how the collective clinical experience of health systems can be used to develop medical AI with specialized expertise and broad clinical capabilities.
Abstract (translated)
URL
https://arxiv.org/abs/2611.15094
