Challenges and Opportunities of Artificial Intelligence in Medicine

Dear Colleagues,

Artificial intelligence (AI) in medicine offers revolutionary opportunities to streamline clinical workflows, accelerate drug discovery and improve diagnostic accuracy, but its integration is heavily restricted by challenges regarding algorithmic bias, data privacy, lack of evidence-based support, multimodality, missing explainability, small annotated samples and legal liability.

Currently, AI acts as a powerful companion tool that enhances human capabilities and relieves operational pressure within modern healthcare infrastructure. It has accelerated the diagnosis and precision treatment in several medical settings including medical imaging, urgical guidance and personalized therapeutics. It also has been used for workflow optimization and reducing staff burnout. Other areas where AI have scored very highly include accelerating pharmaceutical research by identifying molecular targets and providing epidemic early warnings. However, deploying AI safely requires solving complex mathematical, structural, ethical and clinical roadblocks.

This issue is an attempt to solve these critical challenges. We invite authors to submit papers reporting novel AI methods with biomedical applications to this Special Issue—in particular, exploration and research into the development of new algorithms for precision medicine where issues like algorithmic vulnerabilities, the "Black Box" problem and hallucinations can be minimized. Topics of interest include, but are not limited to, the following:

• Large language models (LLMs) and multimodal models;
• Generative AI in diagnostics;
• Multimodal learning;
• Federated and privacy-preserving learning;
• Precision medicine;
• Clinical workflow optimization;
• Explainable AI (XAI);
• Responsible AI;
• Human–AI collaboration;
• Thick data analytics;
• Hybridizing knowledge graphs (KGs) with vector embeddings;
• Translational medicine;
• AI-based clinical decision making;
• Medical knowledge engineering;
• Knowledge-based and agent-based systems;
• Computational intelligence in bio- and clinical medicine;
• Intelligent and process-aware information systems in healthcare and medicine;
• Natural language processing in medicine;
• Data analytics and mining for biomedical decision support;
• New computational platforms and models for biomedicine;
• Intelligent exploitation of heterogeneous data sources aimed at supporting decision-based and data-intensive clinical tasks;
• Intelligent devices and instruments;
• Automated reasoning and meta-reasoning in medicine;
• Machine learning in medicine, medically oriented human biology and healthcare;
• AI and data science in medicine, medically oriented human biology and healthcare;
• AI-based modeling and management of healthcare pathways and clinical guidelines;
• Models and systems for AI-based population health.

Prof. Dr. Jinan Fiaidhi
Prof. Dr. Sabah Mohammed
Guest Editors

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