Transforming Healthcare with Generative AI
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 20 May 2025 | Viewed by 701
Special Issue Editors
Interests: information systems and technologies; business intelligence; big data; intelligent software agents; machine learning; data mining; multi-criteria decision making; fuzzy sets
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; classification; neural networks and artificial intelligence; supervised learning; data mining and knowledge discovery; text mining; predictive analytics
Special Issue Information
Dear Colleagues,
Artificial intelligence (AI) is rapidly transforming the healthcare industry, bringing forth unprecedented innovations in diagnosis, treatment and patient care. Recent developments in AI, particularly in explainable AI (XAI), federated learning and other privacy-preserving AI techniques, are paving the way for more transparent, interpretable and secure decision-making processes in healthcare. This Special Issue is dedicated to exploring the latest advancements in these AI subfields and their applications within the healthcare sector.
AI for healthcare holds the promise of revolutionizing medical practices by enhancing the accuracy of diagnoses, personalizing treatment plans and optimizing resource management. However, the complexity of AI models often poses challenges in understanding and interpreting their decisions, leading to concerns about trust and accountability. Explainable AI seeks to address these issues by making AI systems more interpretable, enabling healthcare professionals to better understand the rationale behind AI-driven decisions and improving the overall quality of care.
Federated learning and other privacy-preserving AI methods offer innovative solutions for training AI models on decentralized data sources without compromising patient privacy. In an era where data security and patient confidentiality are paramount, these techniques allow for the collaborative development of AI models across institutions, ensuring that sensitive information remains protected while benefiting from the collective knowledge of a broader dataset.
This Special Issue invites researchers and practitioners from the fields of AI, healthcare and related disciplines to submit original research papers, reviews and case studies that explore the integration of explainable AI, federated learning and privacy-preserving techniques in healthcare. Contributions that address key challenges, propose novel methodologies or demonstrate practical applications in areas such as diagnostic support systems, personalized medicine, telemedicine and health data management are highly encouraged.
By fostering an interdisciplinary dialogue, this Special Issue aims to advance the understanding of how these cutting-edge AI techniques can be effectively implemented in healthcare, ultimately leading to more transparent, secure and patient-centric solutions.
Prof. Dr. Galina Ilieva
Dr. Zhelyazko Terziyski
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI in healthcare
- explainable AI
- medical AI applications
- interpretable machine learning
- federated learning
- privacy-preserving AI methods
- patient data security
- healthcare data privacy
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