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Updated: Jun 20, 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

Hepatology e-consult responses generated by artificial intelligence demonstrate accuracy but require human oversight.

Holly K T Huang¹, Debra W Yen², Michelle Y Li¹

¹Division of Gastroenterology and Hepatology, Department of Medicine, University of California San Francisco, San Francisco, California, USA.

Hepatology Communications

|June 19, 2026

Summary

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In clinical practice, the direct measurement of hepatic blood flow to evaluate liver function presents significant challenges due to the intricate and specialized nature of the necessary techniques. Consequently, healthcare professionals often rely on empirical estimates derived from thorough patient examinations and liver function tests to gauge liver health. Among the tools at their disposal, the Child–Pugh and MELD scoring systems stand out for their ability to categorize and assess the...

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A customized large language model (LLM) for liver diseases, LiVersa, shows promise in drafting electronic consultations (e-consults). While effective, human oversight is crucial, and LLM-as-a-judge offers conservative quality assurance.

Area of Science:

Hepatology
Artificial Intelligence
Medical Informatics

Background:

Electronic consultations (e-consults) enhance specialist access but increase provider workload.
A novel customized large language model (LLM), LiVersa, was developed for liver disease e-consults.
The study evaluates LiVersa's efficacy in drafting responses and compares human and machine reviewer assessments.

Purpose of the Study:

To assess the performance of LiVersa in generating hepatology e-consult responses.
To evaluate the equivalence between human expert reviewers and an LLM-as-a-judge system.
To determine the accuracy, safety, and usability of LLM-generated e-consult drafts.

Main Methods:

LiVersa-generated responses for 61 hepatology e-consults from UCSF (Jan-Mar 2025) were analyzed.

Keywords:

cirrhosis clinical decision support systems liver diseases referral and consultation telemedicine

Related Experiment Videos

Last Updated: Jun 20, 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

Three hepatologists and an "LLM-as-a-judge" system evaluated drafts using a 12-item rubric.

Equivalence testing between human and LLM reviewers was performed using two one-sided tests (TOST).

Main Results:

LiVersa drafts matched original responses in word count and verbosity.
Human reviewers found 72% of drafts reasonable starting points and 83% provided appropriate recommendations.
LLM-as-a-judge was more stringent, identifying more potentially harmful drafts (67% vs. 20%) and fewer clinically equivalent responses (27% vs. 48%) than human reviewers.

Conclusions:

Customized LLMs like LiVersa demonstrate potential for streamlining e-consult drafting.
Human oversight remains essential for ensuring the safety and accuracy of LLM-generated responses.
LLM-as-a-judge serves as a conservative tool for quality assurance, particularly during LLM updates.