Al-Driven Innovation in Healthcare: Mathematical Methods, Security, and Applications

Dear Colleagues,

Artificial Intelligence (AI) is reshaping healthcare by driving innovation in diagnostics, treatment planning, mental healthcare, and healthcare system security. With the explosion in healthcare-related data, from clinical records and medical images to genomic profiles and real-time monitoring, there is a pressing need for intelligent, secure, and interpretable AI systems that can transform data into actionable knowledge.

This Special Issue aims to highlight emerging AI methods, applications, and security considerations in healthcare. We welcome interdisciplinary research that addresses key challenges such as data privacy, multimodal data integration and visualisation, robustness, explainability, and real-world deployment. Contributions at the intersection of AI and cybersecurity are particularly encouraged to address the growing need to protect sensitive health data and infrastructure.

We also seek to explore the transformative potential of generative AI (e.g., large language models and synthetic data generation) and AI agents (e.g., autonomous virtual assistants) in healthcare. These technologies offer novel capabilities for clinical decision support, patient engagement, medical content creation, and cybersecurity automation.

Submissions grounded in mathematical modelling, computational frameworks, optimisation, or statistical learning theory are particularly welcome. Topics may include the mathematical formulation of AI models, algorithmic performance analysis, graph-based learning, and the development of interpretable and robust methods with a solid mathematical foundation.

We welcome original research, review articles, and methodological contributions demonstrating innovative AI applications across healthcare and related fields. This Special Issue provides a platform for interdisciplinary collaboration among researchers in mathematics, computer science, medicine, public health, and cybersecurity.

Prof. Dr. Xujuan Zhou
Dr. A. Alice Nithya
Dr. Michael Lane
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 2600 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.

• artificial intelligence in healthcare
• clinical decision support systems
• computational drug discovery
• generative AI and large language models (LLMs)
• explainable AI (XAI)
• deep learning for medical imaging; graph theory in medical applications;
• privacy-preserving machine learning
• healthcare cybersecurity
• mathematical modelling and optimisation
• multimodal data fusion and visualisation mathematical modelling and optimisation techniques in AI-driven healthcare
• applications of graph theory in medical diagnostics and predictive modelling
• secure and privacy-preserving AI algorithms for health data analysis
• generative AI and synthetic data generation in clinical and pharmaceutical research
• explainable AI (XAI) methods with rigorous mathematical formulations
• AI-based medical imaging and radiomics with a focus on interpretability
• multimodal data fusion and visualisation using statistical and computational approaches
• AI agents and autonomous decision-making systems in healthcare
• robust machine learning algorithms for real-world clinical deployment
• AI-enhanced cybersecurity frameworks for protecting healthcare infrastructure
• AI for modelling climate–health interactions and environmental health impacts