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Updated: Apr 25, 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

ConsultCraft: Reimagining Perioperative Education With AI-Based Adaptive Case Discussions.

Lauren K Buhl¹, Kate Salotto¹, Thomas Thesen¹

¹is an Assistant Professor in the Department of Anesthesiology at Dartmouth Hitchcock Medical Center in Lebanon, NH; is a medical student in the Department of Medical Education at the Geisel School of Medicine at Dartmouth in Hanover, NH; is an Associate Professor in the Department of Computer Science and the Department of Medical Education at the Geisel School of Medicine at Dartmouth College in Hanover, NH.

The Journal of Education in Perioperative Medicine : JEPM

|April 15, 2026

Summary

ConsultCraft, an AI tool, offers personalized medical education by combining large language models with expert content, minimizing misinformation risks for precision learning.

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Last Updated: Apr 25, 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 Technology
Artificial Intelligence in Healthcare
Precision Education

Background:

Current medical education lacks integrated data analytics and efficient content delivery systems.
Personalized, scalable learning tools are essential for the American Medical Association's precision education vision.
Large language models (LLMs) show promise but require expert oversight to prevent misinformation.

Purpose of the Study:

To develop and evaluate ConsultCraft, a web-based application for perioperative case discussions.
To assess the efficacy of LLM-driven simulated clinical case discussions in enhancing critical thinking and decision-making skills.
To analyze the accuracy of feedback and identify potential misinformation generated by the LLM.

Main Methods:

Developed ConsultCraft using GPT-4o API, referencing expert-revised cases from Claude 3.5 Sonnet.
Users engaged in simulated case discussions with options for immediate (tutor) or deferred (immersion) feedback.
Analyzed 4-week transcripts for question progression, feedback accuracy, inferences, false credit, and implied knowledge gaps.

Main Results:

Analyzed 49 sessions across 9 cases, with 80% in tutor mode.
The LLM generated 306 questions and 236 feedback responses.
Low rates of misleading inferences (2.1%), false credit (4.2%), and implied knowledge gaps (9.7%) were observed.

Conclusions:

ConsultCraft effectively integrates LLM capabilities with expert content, minimizing misinformation in personalized case discussions.
Future work includes mapping content to certification requirements and ICD-10 codes, and integrating assessment data.
This approach demonstrates leveraging augmented intelligence for tailored educational tools in precision education.