Leveraging Professional Radiologists' Expertise to Enhance LLMs' Evaluation for AI-generated Radiology Reports.

ABSTRACT: In radiology, Artificial Intelligence (AI) has significantly advanced report generation, but automatic evaluation of these AI-produced reports remains challenging. Current metrics, such as Conventional Natural Language Generation (NLG) and Clinical Efficacy (CE), often fall short in capturing the semantic intricacies of clinical contexts or overemphasize clinical details, undermining report clarity. To overcome these issues, our proposed method synergizes the expertise of professional radiologists with Large Language Models (LLMs), like GPT-3.5 and GPT-4. Utilizing In-Context Instruction Learning (ICIL) and Chain of Thought (CoT) reasoning, our approach aligns LLM evaluations with radiologist standards, enabling detailed comparisons between human and AI-generated reports. This is further e

SUBMITTER: Zhu Q 

PROVIDER: S-EPMC11651630 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature