A worthy call to action by CHAI founding members, John Brownstein, Christopher Longhurst, Karandeep Singh, Ashish Atreja, MD, MPH and Aneesh Chopra. This deeply resonates and aligns with the Coalition for Health AI (CHAI)'s vision to establish a federated network of Quality Assurance Labs for AI. Imaging a network of labs that can readily, and transparently generate prospective, real world evidence on AI model performance is desperately needed for post-market surveillance purposes if we are to ensure AI models are safe and effective for all of us.
AI promises to revolutionize healthcare by enhancing diagnosis, treatment, and patient safety. Yet, a critical gap exists between AI research and its real-world impact. We urgently need comprehensive clinical effectiveness evaluations to bridge this divide. My colleagues at UC San Diego Health, UC Davis Health, and I published this article in the NEJM AI to establish boundaries for using AI in the healthcare setting, bringing together research and real-world evidence for comprehensive clinical effectiveness. AI is bound to revolutionize our industry that was traditionally adverse to change, yet it is essential to collaborate on the shared goal of safer, more effective, and equitable care for all patients. Christopher Longhurst Karandeep Singh Aneesh Chopra Ashish Atreja, MD, MPH Our call to action: (1) Move Beyond Model Validation: It's essential to validate AI models not just through simulations but in real-world clinical settings, akin to clinical trials for new drugs. (2) Focus on Local Context: The importance of local health care contexts in AI model validation cannot be overstated. Different settings require tailored approaches for optimal outcomes. (3) Implement Science Principles: Adopting implementation science principles will ensure that AI tools are effectively integrated into health care workflows, resulting in tangible improvements in patient outcomes. A robust network of healthcare delivery organizations is crucial to focus on the clinical effectiveness of AI models in real-world settings. This collaborative approach will help us achieve safer, more effective, and equitable care for all patients. #HealthcareInnovation #AIinHealthcare #ClinicalEffectiveness #PatientSafety #ImplementationScience #AIValidation #EquitableCare #HealthTech
Moving forward!
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2moThis is essential for safe use of AI as we move forward in healthcare. Great work here.