Scope and positioning

The journal focuses on interdisciplinary research in medical artificial intelligence and related digital medicine. Manuscripts should address a clearly defined question in medicine, life sciences or health services and include substantive artificial intelligence, computational methodology or digital technology. Methodological, ethical and governance research should have a direct connection to these fields.

The journal considers the importance of the research question, the appropriateness of the methods and the credibility of the conclusions. Algorithmic complexity, dataset size or a single performance metric cannot replace an explanation of medical relevance and the quality of the evidence. Research may be at the stage of methodological exploration, retrospective validation, prospective evaluation or real-world implementation, but should clearly state its stage and the limits of its conclusions.

The journal welcomes rigorous replication and validation, negative findings, results showing no significant differences and analyses of failure. Methods that have not undergone clinical evaluation should not be described as having improved diagnostic or treatment outcomes or as being ready for routine clinical practice.

General clinical observations, routine hospital administration, general-purpose algorithms without a clear medical connection, and product promotion lacking empirical evidence are outside the journal’s main scope.

Main research themes

Medical AI Methods and Multimodal Research

This theme focuses on artificial intelligence methods for medical problems and the representation, analysis and integration of different types of medical data. Topics include medical imaging and digital pathology, medical language processing, medical foundation models, multimodal learning, medical knowledge representation and privacy-preserving computation.

Manuscripts should explain the medical problem addressed, data sources, evaluation design, intended use settings and methodological limitations. The journal values meaningful comparisons, independent validation and reproducibility, and considers whether methodological improvements offer a clear medical interpretation or practical value.

Clinical AI and Translational Research

This theme focuses on the validation and translation of artificial intelligence in diagnosis, treatment, clinical decision-making and medical innovation. Topics include clinical decision support, human–AI collaboration, intelligent medical devices and robotics, AI-assisted drug research, and evaluation of the transition from experimental research to clinical application.

The emphasis is on clinical usability, effectiveness, safety and evidence supporting translation. Studies should distinguish technical validation from clinical benefit and describe the study population, workflow, comparators, clinical endpoints and potential risks.

Digital Health and Intelligent Healthcare Delivery

This theme focuses on how artificial intelligence and related digital technologies affect health services and healthcare delivery. Topics include telemedicine, wearable devices, digital therapeutics, digital monitoring, health information systems, healthcare workflows and resource allocation.

The emphasis is on integrating technology into real-world service settings, with attention to service quality, patient experience, accessibility, implementation outcomes, costs and sustainability. Discussions of general administration or business operations fall within the journal’s scope only when they directly address digital medicine research and provide supporting evidence.

AI-enabled Precision Medicine and Disease Prevention

This theme focuses on artificial intelligence research in the analysis of individual differences, disease risk assessment, early detection, prevention and personalized interventions. Topics include multi-omics integration, predictive models, risk stratification, prediction of treatment response and digital biomarkers.

The emphasis is on evaluating data representativeness, model calibration, external validation, subgroup performance and value for clinical decision-making. Risk prediction performance should not be equated directly with intervention effectiveness. Studies should make clear the extent to which their findings support clinical or public health decisions in practice.

AI in Aging and Healthy Longevity

This theme focuses on applications of artificial intelligence and related digital methods in aging research, age-related diseases, functional health and care for older adults. Topics include modeling of aging-related data, digital health measures, multimorbidity, assessment of functional status and intelligent care.

The journal values scientifically grounded research on healthy longevity, with attention to health and functioning, quality of life and differences between populations. Studies of aging measures or predictive models should explain what they measure, the extent of validation and the limits of applicability. Changes in measures that have not been adequately validated should not be described as proven extensions of lifespan or reversals of aging.

Evidence, Ethics and Governance of Medical AI

This theme focuses on the design, evaluation and governance of medical AI research. Topics include research methods, evidence assessment, reporting standards, reproducibility, data bias, fairness, privacy protection, safety, responsibility in human–AI interaction, regulatory science and ongoing monitoring after deployment.

The theme welcomes empirical research as well as well-supported methodological and normative analyses. Manuscripts should clearly distinguish research findings, value judgments and policy recommendations, and explain the evidence underlying recommendations, the settings in which they apply and the potential trade-offs.