About this Research Topic
Advances in artificial intelligence are transforming digital medicine, enhancing precision, personalization, and accessibility in healthcare. AI-driven technologies enable providers to efficiently analyze extensive multimodal datasets, including clinical records, medical imaging, and genomic information, yielding insights for early diagnosis, individualized treatments, and ongoing patient monitoring. Explainable AI (XAI) is crucial to this process, promoting transparency and trust by making AI decisions interpretable to clinicians and patients. Additionally, federated learning facilitates the collaborative training of AI models across institutions, safeguarding patient privacy while utilizing expansive datasets for robust model development. From predictive analytics to mobile health applications, AI-powered digital medicine redefines healthcare delivery, making it more proactive, adaptive, and patient-centered. This Research Topic aims to showcase pioneering research within digital health, highlighting the pivotal role of AI in advancing patient care and expanding medical knowledge.
This Research Topic addresses integrating artificial intelligence (AI) into digital medicine to enhance healthcare quality, personalization, and accessibility. AI has the potential to revolutionize diagnostics, treatment customization, and patient monitoring. However, challenges include the interpretability of complex AI models, data privacy concerns in cross-institutional collaborations, and the integration of diverse data sources—such as clinical records, imaging, and genomics—into unified diagnostic frameworks. This Research Topic highlights research that advances solutions in Explainable AI (XAI) for model transparency, federated learning to maintain privacy in collaborative data use, and multimodal data fusion for comprehensive patient insights. We welcome contributions that demonstrate innovative applications while addressing ethical, technical, and practical concerns, supporting the development of secure, interpretable AI systems that focus on improving patient outcomes in various healthcare contexts.
The scope of this Research Topic focuses on the transformative role of AI in modern healthcare. This collection invites contributions examining how AI technologies enhance digital medicine through precision, personalization, and innovation across diverse healthcare applications. The topic welcomes various manuscript types, including original research, reviews, case studies, perspectives, methodological papers, and data reports. We welcome articles on:
- Explainable AI (XAI) in Healthcare
- Precision Medicine
- AI-driven Diagnostics
- Digital Health Innovations
- Medical Imaging and AI Integration
- Mobile Healthcare
- Federated Learning and Digital Medicine
- Multi-modal Data Fusion and AI in Medicine
This Research Topic addresses integrating artificial intelligence (AI) into digital medicine to enhance healthcare quality, personalization, and accessibility. AI has the potential to revolutionize diagnostics, treatment customization, and patient monitoring. However, challenges include the interpretability of complex AI models, data privacy concerns in cross-institutional collaborations, and the integration of diverse data sources—such as clinical records, imaging, and genomics—into unified diagnostic frameworks. This Research Topic highlights research that advances solutions in Explainable AI (XAI) for model transparency, federated learning to maintain privacy in collaborative data use, and multimodal data fusion for comprehensive patient insights. We welcome contributions that demonstrate innovative applications while addressing ethical, technical, and practical concerns, supporting the development of secure, interpretable AI systems that focus on improving patient outcomes in various healthcare contexts.
The scope of this Research Topic focuses on the transformative role of AI in modern healthcare. This collection invites contributions examining how AI technologies enhance digital medicine through precision, personalization, and innovation across diverse healthcare applications. The topic welcomes various manuscript types, including original research, reviews, case studies, perspectives, methodological papers, and data reports. We welcome articles on:
- Explainable AI (XAI) in Healthcare
- Precision Medicine
- AI-driven Diagnostics
- Digital Health Innovations
- Medical Imaging and AI Integration
- Mobile Healthcare
- Federated Learning and Digital Medicine
- Multi-modal Data Fusion and AI in Medicine
Keywords: Explainable AI in Healthcare, Precision Medicine, AI-driven Diagnostics, Digital Health Innovations, Medical Imaging and AI Integration, Mobile Healthcare, Federal Learning and Digital Medicine, Multi-modal Data Fusion and AI in Medicine
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
