Abstract
Artificial intelligence (AI) holds significant promise for revolutionizing health care by enhancing diagnosis, treatment, and patients’ safety. However, the current disparity between the abundance of AI research and the scarcity of evidence on real-world impact underscores the urgent need for comprehensive clinical effectiveness evaluations. These evaluations must go beyond model validation to explore the real-world effectiveness of AI models in clinical settings, especially because so few have gone on to show any meaningful impact. The importance of local context in AI model validation and impact assessment cannot be overstated. We call for increased recognition of implementation science principles and their adoption through development of a network of health care delivery organizations to focus on the clinical effectiveness of AI models in real-world settings to help achieve the shared goal of safer, more effective, and equitable care for all patients.
