Yıldırım A, Özdemir Ö. Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives. Artif Intell Med Imaging 2026; 7(1): 117331 [DOI: 10.35711/aimi.v7.i1.117331]
Corresponding Author of This Article
Öner Özdemir, MD, Department of Pediatric Allergy and Immunology, Faculty of Medicine, Sakarya University, Research and Training Hospital, Adnan Menderes Cad, Adapazarı 54100, Türkiye. ozdemir_oner@hotmail.com
Research Domain of This Article
Computer Science, Artificial Intelligence
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review-article
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Yıldırım A, Özdemir Ö. Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives. Artif Intell Med Imaging 2026; 7(1): 117331 [DOI: 10.35711/aimi.v7.i1.117331]
Artif Intell Med Imaging. Sep 8, 2026; 7(1): 117331 Published online Sep 8, 2026. doi: 10.35711/aimi.v7.i1.117331
Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives
Abdulkadir Yıldırım, Öner Özdemir
Abdulkadir Yıldırım, Medical Faculty, Sakarya University, Adapazarı 54100, Türkiye
Öner Özdemir, Department of Pediatric Allergy and Immunology, Faculty of Medicine, Sakarya University, Research and Training Hospital, Adapazarı 54100, Türkiye
Author contributions: Yıldırım A and Özdemir Ö contributed to manuscript preparation, all aspects of this manuscript.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Corresponding author: Öner Özdemir, MD, Department of Pediatric Allergy and Immunology, Faculty of Medicine, Sakarya University, Research and Training Hospital, Adnan Menderes Cad, Adapazarı 54100, Türkiye. ozdemir_oner@hotmail.com
Received: December 5, 2025 Revised: December 26, 2025 Accepted: January 22, 2026 Published online: September 8, 2026 Processing time: 270 Days and 22.6 Hours
Abstract
This narrative review examines the evolution of artificial intelligence (AI) in healthcare, with a focus on the transition from early rule-based systems to modern deep learning architectures and their integration into clinical practice. We examine foundational technologies, including convolutional neural networks for image interpretation, vision transformers for modeling long-range dependencies, and generative adversarial networks for image reconstruction and synthesis. The review further discusses the emergence of multimodal foundation models that integrate imaging with textual and genomic data to enhance diagnostic robustness. The application of these technologies is analyzed across three primary domains: Radiology (image enhancement and automated interpretation), cardiology (electrocardiographic and echocardiography analysis), and oncology (tumor classification and treatment planning). Specific attention is given to the national context in Türkiye, highlighting local initiatives such as TEKNOFEST and TÜBİTAK-supported projects that foster domestic AI development. While AI offers significant benefits in terms of diagnostic accuracy and treatment workflow optimization, challenges regarding data privacy, algorithmic bias, and interpretability (“black box” issues) persist. Future progress depends on the development of explainable AI, rigorous prospective validation, and the establishment of ethical regulatory frameworks.
Core Tip: Artificial intelligence (AI) is undergoing a paradigm shift from task-specific convolutional architectures to multimodal foundation models. While AI has demonstrated diagnostic performance comparable to experts in radiology, cardiology, and oncology-particularly in retrospective imaging cohorts-its seamless clinical integration remains contingent on overcoming barriers related to explainability, data privacy, and the transition from retrospective validation to prospective clinical trials. This review synthesizes current technical milestones and critically evaluates the path toward implementing human-centric AI.
