Citations 3

Leveraging large language models to enhance cytopathology: Opportunities, challenges, and future directions; a practical review from the ASC Clinical Practice Committee

Cancer Cytopathology | August 12, 2026 | Volume: 134 | Issue: 9 | Pages: No page available

10.1002/cncy.70134 | PMID 42585107

Abstract Large language models (LLMs) and vision‐language models represent a fundamentally different category of artificial intelligence (AI) compared to prior image analysis approaches in digital pathology, which have largely been based on convolutional neural network architectures. This review from the American Society of Cytopathology Clinical Practice Committee examines the current evidence for LLM and vision‐language model applications in cytopathology, including structured reporting, diagnostic assistance, quality control, education, and workflow integration. The distinction between applications with preliminary evidence and those that remain hypothetical is described. A detailed assessment of the challenges that must be addressed before clinical deployment, including hallucination risk, limited explainability, bias, data privacy, validation gaps, and infrastructure barriers is discussed. A review of the regulatory landscape in the United States and European Union as it applies to AI‐enabled software as a medical device is provided. Recommendations addressing cytopathology‐specific benchmarks, multi‐institutional validation, transparent governance, and incremental deployment beginning with low‐risk applications are suggested. In the current environment, LLMs have the potential to augment cytopathology practice, but responsible adoption requires rigorous validation and sustained collaboration among cytopathologists, AI researchers, and regulatory bodies.
Cytologic grading of primary malignant salivary gland tumors: A blinded review by an international panel

Cancer Cytopathology | April 8, 2020 | Volume: 128 | Issue: 6 | Pages: 392-402

10.1002/cncy.22271 | PMID 32267606

BackgroundFine needle aspiration (FNA) is commonly used for the preoperative evaluation of salivary gland tumors. Tumor grade is a key factor influencing clinical management of salivary gland carcinomas (SGCs). To assess the ability to grade nonbasaloid SGCs in FNA specimens, an international panel of cytopathologists convened to review and score SGC cases.MethodsThe study cohort included 61 cases of primary SGC from the pathology archives of 3 tertiary medical centers. Cases from 2005 to 2016 were selected, scanned, and digitized. Nineteen cytopathologists blinded to the histologic diagnosis reviewed the digitized cytology slides and graded them as low, high, or indeterminate. The panelists' results were then compared to the tumor grades based on histopathologic examination of the corresponding resection specimens.ResultsAll but 2 of the 19 (89.5%) expert panelists review more than 20 salivary gland FNAs per year; 16 (84.2%) of the panelists work at academic medical centers, and 13 (68.4%) have more than 10 years' experience. Participants had an overall accuracy of 89.4% in the grading of SGC cases, with 90.2% and 88.3% for low‐ and high‐grade SGC, respectively. Acinic cell carcinoma and mucoepidermoid carcinoma had the highest degree of accuracy, while epithelial‐myoepithelial carcinoma and salivary duct carcinoma had the lowest degree of accuracy. As expected, the intermediate‐grade SGC cases showed the greatest variability (high‐grade, 42.1%; low‐grade, 37.5%, indeterminate, 20.4%).ConclusionThis study confirms the high accuracy of cytomorphologic grading of primary SGC by FNA as low‐ or high‐grade. However, caution should be exercised when a grade cannot be confidently assigned.
The Lower Anogenital Squamous Terminology Standardization Project for HPV-Associated Lesions: Background and Consensus Recommendations from the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology

Archives of Pathology & Laboratory Medicine | June 28, 2012 | Volume: 136 | Issue: 10 | Pages: 1266-1297

10.5858/arpa.lgt200570 | PMID 22742517

Abstract The terminology for human papillomavirus (HPV)–associated squamous lesions of the lower anogenital tract has a long history marked by disparate diagnostic terms derived from multiple specialties. It often does not reflect current knowledge of HPV biology and pathogenesis. A consensus process was convened to recommend terminology unified across lower anogenital sites. The goal was to create a histopathologic nomenclature system that reflects current knowledge of HPV biology, optimally uses available biomarkers, and facilitates clear communication across different medical specialties. The Lower Anogenital Squamous Terminology (LAST) Project was cosponsored by the College of American Pathologists and the American Society for Colposcopy and Cervical Pathology and included 5 working groups; 3 work groups performed comprehensive literature reviews and developed draft recommendations. Another work group provided the historical background and the fifth will continue to foster implementation of the LAST recommendations. After an open comment period, the draft recommendations were presented at a consensus conference attended by LAST work group members, advisors, and representatives from 35 stakeholder organizations including professional societies and government agencies. Recommendations were finalized and voted on at the consensus meeting. The final, approved recommendations standardize biologically relevant histopathologic terminology for HPV-associated squamous intraepithelial lesions and superficially invasive squamous carcinomas across all lower anogenital tract sites and detail the appropriate use of specific biomarkers to clarify histologic interpretations and enhance diagnostic accuracy. A plan for disseminating and monitoring recommendation implementation in the practicing community was also developed. The implemented recommendations will facilitate communication between pathologists and their clinical colleagues and improve accuracy of histologic diagnosis with the ultimate goal of providing optimal patient care.