Journal of Digital Health & AI (JDHAI)

About the Journal

The Journal of Digital Health and AI publishes high-quality research at the intersection of data, algorithms, people, and care delivery. Scope includes development, validation, and reporting of machine-learning, deep-learning, and causal inference methods for prevention, diagnosis, prognosis, treatment selection, and operational efficiency across clinical, public-health, and consumer settings. We welcome studies on data infrastructure—EHRs, imaging, genomics, wearables, social determinants, and multimodal fusion; model robustness, drift, bias, and fairness; privacy-preserving analytics; human factors, usability, and clinician–AI collaboration. Implementation science, real-world evidence, prospective trials, and post-deployment monitoring are central, alongside reproducibility, open datasets, benchmarks, and standards for documentation and evaluation. The journal features work on safety, security, and regulatory science; health economics, reimbursement, and value assessment; interoperability, MLOps, and lifecycle governance. Ethical, legal, and social implications—including transparency, accountability, explainability, and equity for underserved populations—are in scope, as are policy analyses shaping responsible innovation. We publish original research, methodological notes, negative or null results, registered reports, protocols, rapid communications, and rigorous reviews. Submissions should demonstrate clinical or societal relevance, clear reporting, and actionable insights to guide practice, procurement, and policy. Our goal is to advance trustworthy, scalable, and inclusive digital health powered by AI. We welcome diverse disciplines and global, cross-sector collaborations worldwide.