Evidence Requirements for Clinical AI Adoption

AI tools in healthcare should be framed as support for shared decision-making rather than as autonomous decision-makers, which can improve clinician-patient communication. The ML-CDSS model must be trained and validated on patient data from diverse demographic backgrounds to be applicable to the UK's varied patient population. Generating real-world evidence through clinical case studies of ML-CDSS use was identified as critical to broader adoption. Building clinician trust requires that clinicians can observe a tool's reliability and validity before integrating its outputs into clinical decisions. Transparent reporting of AI development methods, appropriate use cases, and performance metrics is essential for credible clinical adoption. Developing implementation blueprints describing how the ML-CDSS could be embedded across different settings was proposed as a way to encourage wider adoption.