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Related Experiment Video

Updated: Jul 1, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Evaluating artificial intelligence-generated multiple-choice questions in clinical pathology.

A Aziz Ould Ismail¹, Weijie Ma¹, Nancy M Dunbar¹

¹Department of Pathology and Laboratory Medicine, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA.

Academic Pathology

|June 30, 2026

Summary

This summary is machine-generated.

Large language models can generate useful pathology education questions, with nearly 75% usable after expert review. However, quality varies by subspecialty, requiring careful oversight.

Keywords:

Artificial intelligence Assessments Clinical pathology Pathology education

Related Experiment Videos

Last Updated: Jul 1, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Medical Education
Artificial Intelligence in Pathology
Assessment Development

Background:

Large language models (LLMs) show promise in medical education, but their utility in clinical pathology assessment is not well-defined.
Evaluating the effectiveness of LLMs like GPT-4 for generating pathology resident educational content is crucial.

Purpose of the Study:

To prospectively evaluate the use of GPT-4 for generating multiple-choice questions for pathology resident education.
To assess the quality, clarity, and accuracy of AI-generated questions across various pathology subspecialties.

Main Methods:

A standardized prompting protocol was used with GPT-4 to create questions for clinical chemistry, hematopathology, microbiology, molecular pathology, bioinformatics, and transfusion medicine.
Faculty experts reviewed 260 AI-generated items, scoring clarity and accuracy, and assigning a disposition (kept, edited, or discarded).

Main Results:

Of 260 items, 41.5% were kept as is, 33.1% were edited, and 25.4% were discarded.
Molecular pathology had the highest retention rate (60% unedited), while clinical microbiology had a higher discard rate (41.7%).
Retained items showed significantly higher clarity and accuracy than discarded items (P < 0.05).

Conclusions:

Nearly three-quarters of AI-generated pathology questions were usable after expert review, indicating LLMs' potential to aid assessment creation.
Subspecialty performance varied, and AI-generated questions often lacked appropriate difficulty, board relevance, or real-world applicability.
Sustained expert oversight is essential due to uneven performance and potential regulatory misalignment.