Reshaping Pathology: A Review of Generative and Non-Generative AI Applications in Diagnostic Practice

Keywords: Artificial Intelligence, Digital Pathology, Generative AI, Histopathology, Cytology

Introduction: Artificial Intelligence (AI) is revolutionizing pathology and laboratory medicine, offering groundbreaking potential in diagnostics. This review encompasses current evidence comparing the evolving roles of generative AI (e.g., large language models, synthetic image generators) and non-generative AI (e.g., machine learning classifiers, image analysis algorithms) in diagnostic workflows. With mean sensitivity and specificity rates of 96.3% and 93.3%, respectively, recent systematic reviews have shown that AI models applied to digital pathology images achieve high diagnostic accuracy [1]. Regulatory-approved solutions are becoming more popular in clinical labs, and non-generative AI tools are already improving accuracy and effectiveness in slide triaging, pattern recognition, and predictive analytics [2]. 

However, although their clinical implementation is still in its infancy, generative models are becoming increasingly potent instruments for report automation, education, and the creation of synthetic datasets [3]. However, problems still exist, such as lack of standardization, interpretability problems, algorithmic bias, and regulatory restrictions [4]. To ensure safe, accurate, and understandable diagnostics, domain expertise and AI must be combined [5]. 

This critical review emphasizes the possible benefits and real-world challenges of integrating AI in pathology. This discussion intends to help stakeholders, lab professionals, and clinicians comprehend the changing digital pathology landscape and make informed choices regarding future deployment by elucidating the extent and consequences of AI tools.

References:
[1] McGenity C., Clarke E. L., Jennings C. et al. (2024) npj Digit. Med., 7, 114.
[2] Pantanowitz L. et al. (2025) Mod. Pathol., 38(3), 100680.
[3] Rashidi H. H. et al. (2024) Mod. Pathol., 38(3), 100688.
[4] Abels E. et al. (2023) Nat. Rev. Methods Primers, 3, 96.
[5] Plass M. et al. (2023) J. Pathol. Clin. Res., 9(1), 3–13.

Biography: Based in Kolkata, India, Dr. Utso Guha Roy (MBBS, MD) is a consultant pathologist with more than 7 years of clinical diagnostic experience. In addition to his expertise in cytology and histopathology, he is actively involved in digital pathology and the incorporation of artificial intelligence into diagnostic processes. He continues to support research in AI-assisted laboratory medicine and has taken part in cancer screening initiatives. He has multiple peer reviewed publications.

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