What question did this study set out to answer?

To evaluate current evidence, technical challenges, regulatory considerations, and practical implementation pathways for large language models and vision-language models in cytopathology.

August 14, 2026Cancer Cytopathology

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

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Authors

KBK. Hasan Bilal JGJoanna Gibson DKDavid Kim

Discussion

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Overview

Clinical committee review evaluates the applications, barriers, and regulatory pathways of large language models in cytopathology, suggesting incremental deployment starting with low-risk tasks.

Key Points

To evaluate current evidence, technical challenges, regulatory considerations, and practical implementation pathways for large language models and vision-language models in cytopathology.
Conducted an expert review by the American Society of Cytopathology Clinical Practice Committee evaluating LLM and vision-language model applications across cytopathology workflows.
Assessed technical barriers, clinical validation gaps, and United States and European Union regulatory frameworks for AI-enabled software as a medical device.
Identified promising cytopathology applications including structured reporting, diagnostic assistance, quality control, workflow optimization, and medical education.
Delineated key barriers to clinical deployment, including hallucination risk, limited model explainability, algorithmic bias, data privacy concerns, and infrastructure demands.
Recommended establishing cytopathology-specific performance benchmarks, conducting multi-institutional validation studies, and pursuing incremental adoption starting with low-risk tasks.

Cite This Study

Bilal et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec79cb70b84ec8b9141cd https://doi.org/10.1002/cncy.70134

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August 14, 2026Cancer Cytopathology

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

View Full Paper

Ask AI

Bookmark

Authors

KBK. Hasan Bilal JGJoanna Gibson DKDavid Kim

Discussion

Member takes

Overview

Clinical committee review evaluates the applications, barriers, and regulatory pathways of large language models in cytopathology, suggesting incremental deployment starting with low-risk tasks.

Key Points

To evaluate current evidence, technical challenges, regulatory considerations, and practical implementation pathways for large language models and vision-language models in cytopathology.
Conducted an expert review by the American Society of Cytopathology Clinical Practice Committee evaluating LLM and vision-language model applications across cytopathology workflows.
Assessed technical barriers, clinical validation gaps, and United States and European Union regulatory frameworks for AI-enabled software as a medical device.
Identified promising cytopathology applications including structured reporting, diagnostic assistance, quality control, workflow optimization, and medical education.
Delineated key barriers to clinical deployment, including hallucination risk, limited model explainability, algorithmic bias, data privacy concerns, and infrastructure demands.
Recommended establishing cytopathology-specific performance benchmarks, conducting multi-institutional validation studies, and pursuing incremental adoption starting with low-risk tasks.

Cite This Study

Bilal et al. (2026) studied this question.

synapsesocial.com/papers/6a7ec79cb70b84ec8b9141cd https://doi.org/10.1002/cncy.70134

View Full Paper

Ask AI

Bookmark