Artificial Intelligence and Digital Pathology: Technological Transformation and Strategic Impact in Clinical Research and Medical Affairs

Artificial intelligence in digital pathology diagnosis and analysis: Technologies, clinical integration, and future prospects (Review)

The present review analyzes the existing context of AI pathology systems, particularly diagnostic precision, clinical validation, and technical systems such as convolutional neural networks and transformers and discusses the integration challenge in clinical workflows for these systems.

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.

Deep learning-based computational pathology: Technologies, clinical applications, and future directions.

A dual-perspective framework to systematically bridge the gap between algorithmic advances and practical demands of pathological diagnosis and prognosis is proposed, which offers practical guidance for selecting and designing AI solutions tailored to specific clinical goals.

Artificial intelligence in prostate cancer diagnosis and management: state-of-the-art approaches and emerging innovations.

Deployment of Artificial Intelligence in Clinical Oral Pathology: Evidence Summary and Implementation Gaps

AI has the capacity to strengthen diagnostic pathology by improving consistency, measurement and efficiency and moving from experimental use to routine reporting will require broad validation across centres, enhanced model transparency, strong quality‐assurance systems and close cooperation between developers and pathologists.

Artificial Intelligence in Radiation Oncology: Current applications, clinical impact, and future perspectives. A Narrative Review

Radiation oncology is a highly digital and data-intensive discipline in which artificial intelligence (AI) is increasingly being incorporated into the clinical workflow. From image analysis and automatic segmentation to treatment planning, adaptive radiotherapy, radiomics, and outcome prediction, AI has the potential to improve efficiency, reproducibility, and treatment personalization. This narrative review summarizes the principal concepts underlying machine learning and deep learning and examines their current applications across the radiation therapy pathway. Particular attention is given to automated contouring, knowledge-based and automated planning, cone-beam computed tomography (CBCT)- and magnetic resonance imaging (MRI)-guided adaptive radiotherapy, toxicity and tumor-control prediction, and the integration of radiomic, clinical, dosimetric, and molecular data. The available literature suggests that AI can reduce repetitive workload and inter-observer variability and may facilitate more consistent treatment planning. However, technical performance alone does not establish clinical benefit. Major challenges include data quality, dataset bias, limited external generalizability, interpretability, cybersecurity and data governance, and the need to define responsibility when AI-generated outputs are used in patient care. Prospective, multicenter validation and continuous quality assurance are therefore essential. The future of AI in radiation oncology is unlikely to be the replacement of the radiation oncologist, but rather a human–AI partnership in which automation supports clinical expertise. If AI successfully releases clinical time, this resource should be reinvested in patient communication, shared decision-making, research, innovation, and education.