Systemic Barriers and Ecological Construction in the Clinical Translation of Medical Artificial Intelligence: A Multidimensional Path Beyond Technological Refinement

In recent years, the field of medical artificial intelligence (AI) has experienced sharp tension between "technological spectacle" and "clinical reality."On the one hand, AI continues to evolve, with its potential for technological innovation gradually being unleashed, as models repeatedly set new records in laboratory benchmark tests.On the other hand, products that are truly integrated into clinical workflows and consistently generate patient value remain exceedingly rare.How to harness the potential of "technology for good" and how to respond to people's aspirations for a better life have become defining questions of our time.Through a systematic diagnosis across six dimensions-technology, data, evaluation, payment, collaboration, and governance-this study reveals structural mismatches such as the disconnection between accuracy and clinical utility, the coexistence of data silos and privacy barriers, the value gap in traditional evaluation metrics, the vacuum in payment models, the fragmentation of medical-engineering collaboration, and the lag in regulatory adaptation.Beyond technology centralism.This study proposes an analytical framework of "multidimensional ecosystem construction."It outlines pathways to address existing challenges through several dimensions: developing clinically friendly systems, building trustworthy data networks such as federated learning (FL), establishing clinical value assessment systems that incorporate patient-reported outcomes (PROs), innovating performance-based payment and subscription business models, fostering deeply embedded team science, and developing agile governance mechanisms such as regulatory sandboxes.Through case analyses of IDx-DR, IBM Watson, and Google Health, as well as comparative studies of regulatory systems in China, the United States, and Europe, the paper delineates a feasible pathway from "technological marvels" to "clinical realities."The findings indicate that the key to successful clinical translation lies in shifting the focus of evaluation from "models" to "people," with patient empowerment, value validation, and a sustainable ecosystem serving as the three strategic pillars.