A human centered framework for translating machine learning diagnostic evidence into clinical practice guidelines

Abstract The continuous evolution of medicine necessitates the adoption of machine learning (ML) methods to strengthen global health systems. As an ethical, non-invasive, and cost-effective complement to randomized controlled trials (RCTs), ML holds great promise for generating clinical insights. However, its integration into practice remains severely limited, largely due to the absence of standardized frameworks that align ML research with clinical practice guidelines (CPGs). To address this gap, we developed the Human-Centered Medical Machine Learning (HCMML) protocol—a candidate framework for translating ML-derived diagnostic evidence into CPGs. The framework was built upon five systematic reviews encompassing more than 830 original studies, systematic reviews, meta-analyses, and CPGs, followed by a two-round Delphi consensus process and an expert-informed assessment. Across these reviews, no evidence was found that ML-based diagnostic models had been incorporated into the CPGs examined. The synthesis identified eight major adoption barriers and ten expert-endorsed complementary principles organized into three dimensions: Clinical Trust, Implementation Rigor, and Knowledge Evolution. These were subsequently mapped through a structured questionnaire, forming the basis of the proposed protocol. The questionnaire completed by 44 international domain experts provided preliminary support for the framework’s clarity, relevance, and perceived feasibility. The protocol also introduces three key methodological innovations: decoupling model design from clinical evaluation, promoting multidisciplinary collaboration throughout the translational pathway, and shifting the focus of systematic review from computational performance toward real-world clinical utility. Although developed using ML-based diagnostic studies, the framework is built on algorithm-independent design principles suggesting conceptual extensibility to broader intelligent methods—including deep learning and other AI paradigms—though this requires future investigation. By bridging the terminological and methodological gap between computational innovation and clinical practice, HCMML offers a systematic pathway for the responsible adoption of AI-based diagnostic systems in high-stakes medical settings.