Exploring Arti-icial Intelligence in Healthcare: A Review

Artificial intelligence in healthcare: Technical advances, clinical integration, and future perspectives

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.

Artificial Intelligence in Healthcare: A Review

The reviewed literature indicates that machine learning, deep learning, explainable AI, and human–AI collaboration can improve diagnostic accuracy, efficiency, and patient-centered care, but challenges related to data privacy, algorithmic bias, explainability, cybersecurity, clinical validation, regulation, and unequal access continue to restrict large-scale implementation.

AI-Enabled Smart Healthcare Systems: Innovative Approaches to Disease Prediction, Accurate Diagnosis, and Effective Public Health Management

It is concluded that AI-driven diagnostic systems have achieved genuine, reproducible performance parity with human specialists on narrow, well-defined tasks, while broader clinical deployment remains constrained by validation, generalizability, and algorithmic-bias challenges that the reviewed literature has only begun to resolve.

Generative AI in Healthcare: Applications and Challenges

Generative AI demonstrably accelerates diagnostic workflows, augments scarce clinical datasets, personalizes communication, and supports discovery pipelines, and the paper concludes with a translational path and research priorities aimed at closing these gaps.

Comprehensive Review of AI Applications in Medical Diagnostics: Techniques, Challenges, and Future Directions

An all-encompassing review of current advances in AI requests across miscellaneous healing concentration, containing endemic disease discovery, main central nervous system, cardiology, tumor, and ophthalmology, and oncology is determined.

From Algorithms to Outcomes: Bridging the Deployment Gap in Medical AI