Atypical glandular cells (AGC) are a diagnostic challenge. The aim of this study was to evaluate the efficacy and diagnostic performance of AGC detection on the Hologic Genius Digital Diagnostics System (HGDDS). A retrospective analysis of 451 ThinPrep Pap cases was conducted, including 207 cases of AGC, 27 cases of high-grade squamous intraepithelial lesion (HSIL), 25 cases of low-grade squamous intraepithelial lesion (LSIL), and 192 benign cases. All AGC cases had follow-up histologic diagnoses, with 66 cases subsequently diagnosed as adenocarcinoma. The slides were randomized, scanned, and analyzed by the HGDDS. Patient age and HPV test results were provided to reviewers, an experienced cytologist, who screened the cases, followed by two cytopathologists who independently examined the cases on the HGDDS. Diagnostic concordance between the two cytopathologists indicated strong agreement (κ = 0.829). Sensitivity of AGC on Papanicolaou (Pap) tests for adenocarcinoma detection on HGDDS was 98.5% and 95.5%, respectively, comparable to the original ThinPrep interpretation (OTPI). Specificity for adenocarcinoma detection was significantly higher (84.6% and 85.6%) with the HGDDS than 27.7% with OTPI. Overall, the diagnostic performance for AGC/HSIL interpretation to detect CIN2/3/adenocarcinoma appeared to have improved with HGDDS compared with OTPI, particularly for specificity and positive predictive value (PPV). This is the first study evaluating AGC diagnosis using the HGDDS. The findings demonstrate that the sensitivity of adenocarcinoma detection as AGC on HGDDS is comparable to the ThinPrep Imaging System, but the specificity and PPV are improved. This suggests the potential of artificial intelligence to augment the performance of cervical cancer screening.

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.

Two new studies now make the most substantial case yet that the framework of the International System for Reporting Serous Fluid Cytopathology can be carried across the biologic boundary to include cerebrospinal fluid, and current work on the second edition of the system will address cerebrospinal fluid for the first time as a special category of body fluid-an expansion the international community has explicitly endorsed. The contributions of these authors will stand among the empirical foundations on which a coherent, cerebrospinal fluid-specific-and, ultimately, fluid-agnostic-reporting standard can be built.

Recognition of high-risk human papillomavirus (HPV) as the etiologic agent in nearly all cervical cancers has fundamentally reshaped screening strategies, driving the development and adoption of highly sensitive molecular HPV assays. Over the past several decades, cervical cancer screening guidelines have shifted from primarily cytology-based approaches toward HPV-based modalities, with primary HPV testing now recommended as the preferred method by the American Cancer Society and anticipated to be similarly endorsed by the United States Preventive Services Task Force. This article traces the evolution of cervical cancer screening, from the original Papanicolaou test to current guideline recommendations, and discusses the logistical requirements for transitioning to a primary HPV screening system, including triage strategies for HPV-positive cases. The authors additionally present an institutional experience of initiating primary HPV testing, highlighting its impact on cytopathology laboratory workflow and specimen volumes.