Recent Advances in Artificial Intelligence for Healthcare: A Comprehensive Analysis of Image Processing and Next-Generation Applications

Document Type : Original Article

Authors

1 Information Systems Depart. - Faculty of Computers and AI - Helwan Unniversity

2 Professor of Information Systems, Vice Dean for Graduate Studies and Scientific Research, Helwan University, Egypt.

3 Faculty of Medicine - Helwan University

Abstract

Artificial Intelligence (AI) is transforming healthcare, particularly in medical imaging, predictive diagnostics, robotic surgery, and clinical workflow support. As the field advances rapidly, it becomes critical to assess the emerging models, their applications, and the challenges impeding full-scale clinical adoption. This structured review surveyed AI applications in healthcare published between 2023 and early 2025. Studies were retrieved from major academic databases using defined search queries. After applying rigorous inclusion and exclusion criteria, 70 peer-reviewed articles were selected as 40 focused on image processing and 30 addressing broader applications such as electronic health record analysis, virtual assistants, and federated learning. AI technologies continue to demonstrate significant advancements across diagnostic accuracy, patient monitoring, and decision support. Key trends include the adoption of federated learning for secure model training, synthetic data generation to overcome limited datasets, and the use of explainable AI (XAI) to enhance clinician trust. However, persistent challenges such as algorithmic bias, limited model interpretability, infrastructure barriers, and ethical concerns remain prevalent. The review also highlights a lack of geographical representation in literature, underlining the need for globally inclusive AI research. AI holds enormous potential for reshaping healthcare systems, but meaningful clinical integration requires addressing ethical, technical, and organizational challenges. This review synthesizes current trends and limitations while providing a roadmap for future research focused on equity, transparency, and collaboration. It emphasizes the importance of interdisciplinary engagement to ensure the development of trustworthy, effective, and widely accessible AI-driven healthcare solutions.

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