A Practical Guide to Evaluating Artificial Intelligence Imaging Models in Scientific Literature

IF 4.6 Q1 OPHTHALMOLOGY

Ophthalmology science Pub Date : 2025-06-09 DOI:10.1016/j.xops.2025.100847

Angela McCarthy , Ives Valenzuela MD , Royce W.S. Chen MD , Lora R. Dagi Glass MD , Kaveri Thakoor PhD

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引用次数: 0

Abstract

Objective

Recent advances in artificial intelligence (AI) are revolutionizing ophthalmology by enhancing diagnostic accuracy, treatment planning, and patient management. However, a significant gap remains in practical guidance for ophthalmologists who lack AI expertise to effectively analyze these technologies and assess their readiness for integration into clinical practice. This paper aims to bridge this gap by demystifying AI model design and providing practical recommendations for evaluating AI imaging models in research publications.

Design

Educational review: synthesizing key considerations for evaluating AI papers in ophthalmology.

Participants

This paper draws on insights from an interdisciplinary team of ophthalmologists and AI experts with experience in developing and evaluating AI models for clinical applications.

Methods

A structured framework was developed based on expert discussions and a review of key methodological considerations in AI research.

Main Outcome Measures

A stepwise approach to evaluating AI models in ophthalmology, providing clinicians with practical strategies for assessing AI research.

Results

This guide offers broad recommendations applicable across ophthalmology and medicine.

Conclusions

As the landscape of health care continues to evolve, proactive engagement with AI will empower clinicians to lead the way in innovation while concurrently prioritizing patient safety and quality of care.

Financial Disclosure(s)

Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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科学文献中评估人工智能成像模型的实用指南

人工智能（AI）的最新进展正在通过提高诊断准确性、治疗计划和患者管理来彻底改变眼科。然而，对于缺乏人工智能专业知识的眼科医生来说，在有效分析这些技术并评估其是否准备好融入临床实践方面，仍然存在重大差距。本文旨在通过揭开人工智能模型设计的神秘面纱，并为评估研究出版物中的人工智能成像模型提供实用建议，来弥合这一差距。设计教育综述：综合评价眼科人工智能论文的关键考虑因素。本文借鉴了一个跨学科团队的见解，该团队由眼科医生和具有开发和评估临床应用人工智能模型经验的人工智能专家组成。方法基于专家讨论和对人工智能研究中关键方法论考虑的回顾，开发了一个结构化框架。一种评估眼科人工智能模型的逐步方法，为临床医生提供评估人工智能研究的实用策略。结果本指南提供了广泛的建议，适用于眼科和医学。随着医疗保健领域的不断发展，积极参与人工智能将使临床医生在创新方面处于领先地位，同时优先考虑患者安全和护理质量。财务披露专有或商业披露可在本文末尾的脚注和披露中找到。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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