Artificial intelligence (AI) is reshaping medicine across diagnostic imaging, predictive analytics, clinical decision support, and personalized therapeutics (Topol, 2019; Jiang et al., 2017). Yet a persistent “AI–Real World Gap” separates algorithmic breakthroughs from routine clinical use (Kelly et al., 2019). This gap reflects deficits in data representativeness, workflow integration, interpretability, scalability, and ethical trustworthiness (Rajpurkar et al., 2022). We propose a translational framework that links data-driven discovery to frontline deployment through five pillars: (1) Data Diversity & Curation—multi-institutional datasets and inclusion of underrepresented populations to mitigate bias and improve fairness (Adamson & Smith, 2018); (2) Human–AI Collaboration—clinicians as co-developers to embed domain expertise and align with real tasks (Mesko & Győrffy, 2020); (3) Interoperability & Infrastructure—use of standards such as FHIR and cloud APIs for secure, scalable exchange (Mandel et al., 2016); (4) Ethical & Regulatory Alignment—transparency, accountability, and compliance with evolving guidance, including FDA GMLP (U.S. FDA, 2021); and (5) Continuous Learning & Evaluation—prospective validation, post-market surveillance, real-time feedback, and adaptive retraining (Wiens et al., 2019). Equity is central. Historic data inequities produce algorithmic disparities that disproportionately burden minority and low-income populations (Obermeyer et al., 2019). Participatory design—engaging patients, public health stakeholders, and community organizations from problem definition through dissemination—builds trust and aligns tools with social determinants of health. Scalability remains the final frontier: systems that excel in pilots often underperform beyond single sites or narrow specialties (Shortliffe & Sepúlveda, 2018). We outline a layered approach that integrates adaptive models with digital health ecosystems—telemedicine, wearables, and real-time analytics—to create feedback loops between prediction, intervention, and outcome measurement, enabling population-level monitoring, early detection, and cost-efficient coordination. Anchored in initiatives such as the Electronic Prescription Authentication System (EPAX™) and the SPEED Initiative (Sports, Physical health, Education, Empowerment, Development), this agenda demonstrates how AI can transition from controlled experiments to transformative practice. Bridging the AI–real world divide demands interdisciplinary collaboration across computer science, medicine, policy, and ethics with the shared aim of improving outcomes while safeguarding human values.