Integrating artificial intelligence (AI) into digital medicine is reshaping the future of healthcare by enabling more accurate diagnostics, personalized treatments, real-time monitoring, and broader access to medical services. However, challenges such as the interpretability of AI models, data privacy concerns, and the integration of diverse data sources remain critical barriers to clinical adoption. This Methods Collection aims to explore cutting-edge methodologies and applications that address these challenges and advance the development of secure, transparent, and patient-centered AI systems in medicine.

The collection welcomes contributions on various themes, including explainable AI in healthcare, precision medicine, AI-driven diagnostics, digital health innovations, mobile healthcare, medical imaging with AI, federated learning, and multi-modal data fusion. By highlighting technical advancements and real-world applications, this collection will serve as a valuable resource for researchers, clinicians, and developers seeking to implement effective AI solutions in diverse healthcare settings.

Ultimately, the goal is to foster interdisciplinary collaboration and accelerate the responsible integration of AI into clinical workflows, improving both patient outcomes and healthcare system efficiency.