Pathology AI: Safe Slide, Sample, and Medical Report Analysis in Cancer Care

Abstract

The diagnosis of cancer is usually based on the analysis of tissue slides, biopsies, and medical reports. Conventional pathology is efficient and possesses limitations because of the human factor of fatigue, experience, and interpretation bias that may result in misdiagnosis or postponements in treatment. Deep learning (DL) in artificial intelligence (AI) in digital pathology can enhance the diagnostic accuracy, efficiency, and safety. The literature review will focus on recent studies (2020 to 2025) on AI in pathology, including its application in whole slide image (WSI) analysis, tissue sample interpretation, biomarker scoring, and report mining. We present the major conclusions of the research on the diagnostic performance (sensitivity, specificity, and agreement between observers). Another issue that we address is the problems of AI, including data quality, variability, and regulatory problems. The meta-analyses indicate that AI in digital pathology has a mean sensitivity of approximately 96.3 % and a mean specificity of 93.3 % in the various cancer types. In the latest research on breast cancer, the use of AI allowed the pathologists to increase their diagnostic work and accurately identify lesions that would have otherwise gone unnoticed without the use of AI (97.1 vs. 100). In biomarker scoring, such as PD-L1 in lung cancer, AI was found to be comparable to pathologists. When applied as an aid tool in prostate cancer biopsy, AI minimised the number of diagnostic errors (70%). The findings indicate that AI may be a helpful option in the field of pathology, as it can be used to increase diagnostic accuracy, consistency, and efficiency and decrease human error. To ensure the effective and safe use of AI in cancer care, the technology needs to be carefully tested, validated, and controlled by the government, and it must never be applied without the close monitoring of trained professionals who can interpret the obtained results. The AI tools can be useful in an attempt to diagnose cancer early, standardise the pathology processes, and make the diagnosis of cancer more accessible across geographical boundaries.

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