Recent Advances in Artificial Intelligence for Healthcare: A Comprehensive Analysis of Image Processing and Next-Generation Applications | ||||
| النشرة المعلوماتية في الحاسبات والمعلومات | ||||
| Volume 7, Issue 2 - Serial Number 20250702, July 2025 PDF (548.97 K) | ||||
| Document Type: المقالة الأصلية | ||||
| DOI: 10.21608/fcihib.2025.353639.1136 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
عبدالرحمن محمد حلمي  1; سيد عبدالجابر2; سماح بسطاوي3
| ||||
| 1قسم نظم المعلومات - كلية الحاسبات و الذكاء الاصطناعي - جامعة حلوان | ||||
| 2وکيل الکلية لشئون الدراسات العليا والبحث العلمى، کلية الحاسبات والذکاء الاصطناعى، جامعة حلوان، مصر. | ||||
| 3كلية الطب - جامعة حلوان | ||||
| 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. | ||||
| Keywords | ||||
| Artificial Intelligence (AI); Machine Learning (ML); Medical AI; Smart Healthcare | ||||
| References | ||||
|
| ||||
|
Statistics Article View: 81 PDF Download: 78 |
||||
