Literature review assesses AI's effect on diagnostic accuracy and efficiency in pathology, indicating potential benefits and challenges.
Histological diagnostics enable patient-specific therapy options. Given rising cancer incidence and a growing shortage of qualified personnel, implementing digital pathology workflows with AI integration appears to be a promising solution. This review assesses the literature on AI implementation in pathology regarding diagnostic accuracy, workflow efficiency, and documented challenges. Publications from January 2025 to February 2026 focusing on AI in histology and cytology were evaluated. From 318 identified studies, 16 articles were included after applying inclusion and quality criteria, comprising narrative reviews, pilot studies, validation and implementation studies. Included studies demonstrated high diagnostic accuracy and potential for improved workflow efficiency. Reduction of interobserver variability and standardization of biomarker testing were identified as key benefits. Open questions regarding validation, regulation, and ethics remain. AI-assisted pathology diagnostics shows promising potential regarding diagnostic quality, efficiency, and addressing current staffing challenges. Cost-effectiveness and regulatory aspects require further evaluation.
No takes yet. Share an insight, caveat, or question.
Sophia Petschnak (2026) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: