In conjunction with the MedicinAI workshop, we welcome contributions to this Research Topic that explore the broad spectrum of challenges and opportunities involved in responsibly integrating Artificial Intelligence (AI) into healthcare practice. This Research Topic is open both to public submissions and to manuscripts inspired by discussions and presentations held during the event.

AI is rapidly reshaping medicine, offering unprecedented possibilities to strengthen prevention, diagnosis, treatment planning, and the management of complex health systems. Yet translating data-driven innovation into clinically meaningful, reliable, and safe solutions remains a significant challenge. Biomedical data complexity, the need for explainable and transparent models, and the foundational role of ethical, legal, and organizational considerations all require multidisciplinary reflection. As emphasized in the MedicinAI initiative, there is an urgent need to create a shared space where researchers, clinicians, ethicists, and industry partners can jointly investigate how to deploy AI responsibly and effectively in real-world healthcare settings. Therefore, this Research Topic aims to strengthen interdisciplinary understanding and promote practical pathways linking algorithmic innovation with concrete clinical benefit.

The focus of this Research Topic is to gather contributions that critically examine methodological advances, workflows, governance strategies, and evaluation frameworks for trustworthy, equitable, and context-aware AI in medicine. Submissions may address data science innovations, clinical research applications, digital health governance, bioethical analysis, and implementation science, with the overarching goal of supporting a holistic and actionable vision for AI-enabled care.

Relevant themes include, but are not limited to:

• Multimodal biomedical data integration – Methods and architectures for fusing imaging, genomics, physiological signals, patient-reported outcomes, and clinical records to improve diagnostic accuracy, prognostic modeling, and treatment stratification.

• Federated and privacy-preserving learning for distributed clinical data – Techniques that enable cross-institutional model training without centralizing data, including federated optimization, secure aggregation, differential privacy, and strategies ensuring fairness, robustness, and regulatory compliance.

• Clinical validation, evaluation, and real-world performance – Study designs, metrics, and methodological frameworks for assessing safety, usability, workflow integration, and longitudinal impact in clinical environments.

• Human-centered and context-aware design – Approaches incorporating clinicians’ expertise, patient values, and organizational constraints to promote usability, adoption, and demonstrable clinical value.

• Generative and large-scale models in medicine – Opportunities and challenges related to foundation models, synthetic data generation, and adaptive learning systems for clinical and research use.

• Explainable, transparent, and trustworthy AI – Methods for interpretability, uncertainty quantification, fairness assessment, model monitoring, and clinician-AI collaborative decision-making.

• Ethical, legal, and societal implications – Analyses addressing privacy protection, data stewardship, algorithmic bias, informed consent, liability, and broader frameworks for responsible innovation.

• Digital health ecosystems, data platforms, and governance models – Infrastructure, interoperability standards, and institutional strategies enabling accountable and scalable AI deployment.

Across all themes, contributions should clearly articulate how the proposed approaches can be translated into real-world clinical settings or contribute to improved patient outcomes. All article types supported by the journal sections are welcome. Submissions should adhere to formatting guidelines and ethical standards.

Are you interested in contributing a manuscript to this Research Topic?

MedicinAI: Advancing the Synergy of Medicine and AI from Data to Clinical Impact