Role of artificial intelligence in histopathological diagnosis of cancer current status and future directions

• Author Details:   
• Trushali Mandhare ^*
• Pooja Kashid
• Devata Shinde
• Pankaj Khuspe
• Ritesh Digambar Vyavahare

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Histopathological diagnosis remains the cornerstone of cancer detection, classification, and prognostication. However, conventional approaches are often challenged by inter-observer variability, workload burden, and the growing complexity of oncological pathology. Recent advances in artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), have introduced transformative opportunities for digital pathology. AI-enabled algorithms have demonstrated remarkable accuracy in tasks such as tumor detection, grading, subtyping, and prediction of molecular alterations directly from histology slides. Whole-slide imaging (WSI), coupled with convolutional neural networks (CNNs), has enabled automated quantification of morphological patterns, mitotic figures, and tumor–stroma interactions with precision comparable to expert pathologists. Furthermore, AI systems are increasingly being integrated into prognostic and predictive frameworks, facilitating personalized medicine through the correlation of histopathological features with clinical outcomes and therapeutic responses. Despite this progress, several limitations hinder widespread adoption, including variability in data quality, lack of standardized validation, interpretability challenges, and regulatory concerns. Moreover, integration into clinical workflows demands rigorous evaluation of algorithmic transparency, generalizability across populations, and acceptance by pathologists. This review critically examines the current landscape of AI in histopathological cancer diagnosis, highlighting state-of-the-art applications, translational challenges, and emerging trends. Emphasis is placed on the potential synergy between human expertise and AI-driven decision support, which may reshape the future of oncological pathology. Ultimately, AI holds the promise of augmenting diagnostic accuracy, reducing workload, and enabling precision oncology, provided that ethical, technical, and implementation barriers are systematically addressed.