OpenClinicalAI: enabling AI to diagnose diseases in real-world clinical settings
This addresses the challenge of deploying AI for disease diagnosis in practical healthcare environments, though it is incremental as it builds on existing methods for a specific domain.
The paper tackles the problem of AI systems failing to diagnose diseases in real-world clinical settings where not all subject categories are known, by proposing OpenClinicalAI, which significantly outperforms state-of-the-art AI in diagnosing Alzheimer's disease in such settings.
This paper quantitatively reveals the state-of-the-art and state-of-the-practice AI systems only achieve acceptable performance on the stringent conditions that all categories of subjects are known, which we call closed clinical settings, but fail to work in real-world clinical settings. Compared to the diagnosis task in the closed setting, real-world clinical settings pose severe challenges, and we must treat them differently. We build a clinical AI benchmark named Clinical AIBench to set up real-world clinical settings to facilitate researches. We propose an open, dynamic machine learning framework and develop an AI system named OpenClinicalAI to diagnose diseases in real-world clinical settings. The first versions of Clinical AIBench and OpenClinicalAI target Alzheimer's disease. In the real-world clinical setting, OpenClinicalAI significantly outperforms the state-of-the-art AI system. In addition, OpenClinicalAI develops personalized diagnosis strategies to avoid unnecessary testing and seamlessly collaborates with clinicians. It is promising to be embedded in the current medical systems to improve medical services.