Toward a Unified Technology Readiness Ladder for Clinical Artificial Intelligence: A Systematic Review and Delphi Synthesis

Artificial intelligence (AI) and machine learning (ML) technologies are rapidly advancing, yet their translation into clinical practice remains slow and uneven. This systematic review and Delphi synthesis aimed to integrate and reconcile diverse Technology Readiness Level (TRL) frameworks for clinical AI, creating a unified, evidence-based ladder for assessing AI maturity in healthcare. We analyzed 10 eligible studies, identifying eight distinct TRL frameworks, with only two—CARE and ML-TRL—covering the full nine-level spectrum. Key findings revealed moderate convergence on critical milestones (e.g., dataset provenance by TRL 3, prospective validation by TRL 6, and real-world monitoring by TRL 9), but significant gaps persisted in adaptive-algorithm governance, cybersecurity, and patient-reported outcomes. Empirical validation remains limited, with only four studies linking readiness levels to measurable outcomes. Through Delphi synthesis, we harmonized 105 input and 14 output indicators into a consolidated TRL ladder, providing a structured pathway for AI development from concept to deployment. This work highlights the need for standardized, domain-specific criteria to accelerate safe and effective AI adoption in healthcare while addressing regulatory, ethical, and practical challenges.