From bedside to bench: towards clinical predictive AI research that achieves real-world impact

Clinical predictive artificial intelligence (AI) tools, including equations or models developed using statistical, machine learning or AI methods, have proliferated, yet relatively few effectively translate into routine care. A major contributor to this bench-to-bedside gap is the lack of explicit translational planning at the outset. In this narrative review, we synthesise emerging principles for designing, developing and evaluating predictive AI tools with real-world impact in mind. We propose a pre-modelling discipline that defines the clinical question, intended users and position in the care pathway, highlights the importance of early and sustained stakeholder engagement and explicitly links programme theory to model outputs to inform decisions and change outcomes. It covers regulatory pathways and routes to self-sustainability, ethical and equity considerations and software implementation requirements that shape whether tools can be deployed and sustained. Finally, we summarise core methodological issues in model development, validation and monitoring that are particularly relevant to translation. Taken together, this bedside-to-bench approach reconceptualises clinical predictive AI tools as complex interventions, beginning with a clear expectation of their real-world deployment and benefit and planning backwards to inform model development. This shift in thinking is required to increase the chance that clinical predictive AI tools deliver real-world benefit rather than remaining confined to print.