Submission Deadline: 31 August 2025 View: 388 Submit to Special Issue
Dr. Antonio Sarasa-Cabezuelo
Email: asarasa@ucm.es
Affiliation: Department of Computer Systems and Computing, School of Computer Science, Complutense University of Madrid, Madrid, 28040, Spain
Research Interests: artificial intelligence, machine learning, medical informatics, public health, deep learning, generative artificial intelligence
Dr. Ana M. Gonzlez de Miguel
Email: ana.gonzalez@fdi.ucm.es
Affiliation: Department of Software Engineering and Artificial Intelligence, Complutense University of Madrid, Madrid, 28040, Spain
Research Interests: Generative artificial intelligence, Behavioral-based artificial intelligence, Software engineering methods
Dr. Ulises Roman-Concha
Email: nromanc@unmsm.edu.pe
Affiliation: Faculty of System Engineering, Universidad Nacional Mayor de San Marcos UNMSM, Lima, 15081, Peru
Research Interests: Data Mining, Big Data, Machine Learning, BI, Educacion Virtual
Dr. Krishna Kumar Sharma
Email: krisshna.sharma@uok.ac.in
Affiliation: Department of Computer Science and Informatics, University of Kota, Kota, Rajasthan, 324005, India
Research Interests: Data Mining, Big Data, Machine Learning
The integration of artificial intelligence (AI) models in healthcare is transforming medical diagnostics, treatment planning, and patient management. However, their deployment in critical healthcare applications presents unique challenges, including issues of reliability, interpretability, and ethical concerns. While deep learning and other AI techniques have demonstrated remarkable success in medical imaging, predictive analytics, and personalized medicine, their black-box nature can hinder trust among healthcare professionals and patients. Addressing these challenges requires a comprehensive approach that balances model performance with transparency, robustness, and ethical considerations. This special issue aims to explore recent advancements, challenges, and applications of AI in healthcare, fostering discussions on how to enhance their reliability, effectiveness, and trustworthiness.
This special issue invites contributions that address both theoretical and practical advancements in the development, implementation, and evaluation of AI models in healthcare. The scope of this issue includes, but is not limited to:
· Theoretical foundations and methodological advancements in AI for healthcare
· Explainable and interpretable AI models in medical applications
· Deep learning applications in medical diagnostics and imaging
· AI-driven predictive analytics for disease progression and treatment outcomes
· Ethical, legal, and social implications of AI in healthcare
· Human-AI collaboration in clinical decision-making
· Bias, fairness, and generalizability of AI models in healthcare
· Evaluation metrics and benchmarking for AI in healthcare
· Regulatory frameworks and compliance challenges for AI-driven healthcare solutions
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