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Related Experiment Video

Updated: Jun 11, 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

Enhancing Patient Understanding of Radiology Reports Through Large Language Model-Generated Summaries, Clickable

Hanzhou Li¹, John T Moon², Patricia Balthazar³

¹Co-lead and Principal Investigator of Biodesign & Innovation of Minimally-Invasive Technologies Lab, Emory University, School of Medicine, Atlanta, Georgia.

Journal of the American College of Radiology : JACR

|June 9, 2026

Summary

AI-assisted radiology reports significantly improved patient comprehension. However, patients preferred clinician-edited summaries, highlighting the need for oversight in AI implementation for better patient communication.

Keywords:

Radiology report comprehension artificial intelligence (AI)clinical workflow integration large language models (LLM)patient-centered communication

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Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

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Last Updated: Jun 11, 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

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Area of Science:

Medical Informatics
Radiology
Artificial Intelligence

Background:

Radiology reports can be complex for patients to understand.
Large Language Models (LLMs) offer potential for summarizing complex medical information.

Purpose of the Study:

To evaluate the impact of a custom web application on radiology report comprehension.
To assess patient preferences for AI-assisted report features.
To gauge patient sentiment towards AI-generated summaries.

Main Methods:

A prospective study involving 101 outpatients.
Participants reviewed AI-augmented radiology reports with clinician-edited LLM summaries, definitions, and AI videos.
Surveys assessed comprehension, feature usefulness, and attitudes.

Main Results:

Significant improvement in patient comprehension of radiology reports (p<0.001).
47.52% found LLM summaries most helpful, but most participants (53.46%) were uncomfortable with unedited LLM summaries.
Clinician edits were substantial, indicating LLM summaries require professional oversight.

Conclusions:

Interactive LLM applications can enhance patient comprehension of radiology reports.
Patient comfort with AI summaries is contingent on clinician oversight.
Scalable implementation requires addressing the workload associated with professional review.