  A profound gap persists between artificial intelligence (AI) and clinical
practice in medicine, primarily due to the lack of rigorous and cost-effective
evaluation methodologies. State-of-the-art and state-of-the-practice AI model
evaluations are limited to laboratory studies on medical datasets or direct
clinical trials with no or solely patient-centered controls. Moreover, the
crucial role of clinicians in collaborating with AI, pivotal for determining
its impact on clinical practice, is often overlooked. For the first time, we
emphasize the critical necessity for rigorous and cost-effective evaluation
methodologies for AI models in clinical practice, featuring
patient/clinician-centered (dual-centered) AI randomized controlled trials
(DC-AI RCTs) and virtual clinician-based in-silico trials (VC-MedAI) as an
effective proxy for DC-AI RCTs. Leveraging 7500 diagnosis records from two-step
inaugural DC-AI RCTs across 14 medical centers with 125 clinicians, our results
demonstrate the necessity of DC-AI RCTs and the effectiveness of VC-MedAI.
Notably, VC-MedAI performs comparably to human clinicians, replicating insights
and conclusions from prospective DC-AI RCTs. We envision DC-AI RCTs and
VC-MedAI as pivotal advancements, presenting innovative and transformative
evaluation methodologies for AI models in clinical practice, offering a
preclinical-like setting mirroring conventional medicine, and reshaping
development paradigms in a cost-effective and fast-iterative manner. Chinese
Clinical Trial Registration: ChiCTR2400086816.
