Artificial Intelligence in Diagnostic Pathology: A Comprehensive Review of Current Applications and Future Prospects

Abstract

Artificial intelligence (AI) is transforming diagnostic pathology by improving the accuracy, efficiency, and repeatability of histopathological examinations. AI systems can analyze high-resolution whole slide images (WSIs) using machine learning (ML) and deep learning (DL) algorithms to diagnose malignancies, classify tissue types, grade cancers, and quantify biomarkers with accuracy comparable to expert pathologists. These technologies are very useful in cancer diagnosis, such as breast, prostate, and lung malignancies, where they help identify tumor characteristics, mitotic activity, and lymph node metastases. Furthermore, AI is being utilized to predict prognosis, evaluate therapy response, and detect infectious pathogens in tissue samples.AI integration into digital pathology operations has numerous benefits, including faster diagnostic turnaround times, increased consistency, and support for remote consultation and telepathology. However, issues persist in terms of data quality, model generalization across populations and institutions, and the need for rigorous clinical validation. Regulatory permissions and ethical considerations such as data protection, algorithm transparency, and medico-legal accountability are also required for safe deployment. This review presents a comprehensive examination of existing AI applications in pathology, investigates the technology that enables this transition, and analyzes future possibilities. Understanding AI's function is critical for creating dependable, egalitarian, and successful diagnostic tools in contemporary pathology practice.