Torous, Vanda F.

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.

Context.— Treatments are available for common forms of systemic amyloidosis that show promise for extending and improving the quality of life for patients. Early diagnosis and accurate identification of amyloid fibril type are crucial for successful treatment, but the diagnosis and workup of amyloidosis is inconsistent among pathologists and laboratories. Thus, the goal of this guideline is to offer recommendations for proper testing and workup for amyloidosis to optimize patient care. Objective.— To establish evidence-based recommendations for appropriate laboratory testing to detect amyloidosis and identify the specific amyloidogenic protein. Design.— The College of American Pathologists convened a panel of experts to develop recommendations following the standards established by the National Academy of Medicine for developing trustworthy clinical practice guidelines. The panel conducted a systematic literature review addressing 6 key questions. Using the Grading of Recommendations Assessment, Development and Evaluation framework, recommendations were created based on the available evidence, certainty of that evidence, and key judgments as defined in the framework. Results.— Four conditional recommendations and 3 good practice statements were established to provide guidance for proper testing and workup of amyloidosis. Conclusions.— This guideline summarizes the available evidence on the diagnosis and workup of systemic amyloidosis in tissue samples, including the challenges and limitations of common approaches and techniques. Recommendations for pathologists and laboratories receiving these samples are provided.