Table_1_Development and evaluation of multimodal AI for diagnosis and triage of ophthalmic diseases using ChatGPT and anterior segment images: protocol for a two-stage cross-sectional study.DOCX

IntroductionArtificial intelligence (AI) technology has made rapid progress for disease diagnosis and triage. In the field of ophthalmic diseases, image-based diagnosis has achieved high accuracy but still encounters limitations due to the lack of medical history. The emergence of ChatGPT enables human-computer interaction, allowing for the development of a multimodal AI system that integrates interactive text and image information.
ObjectiveTo develop a multimodal AI system using ChatGPT and anterior segment images for diagnosing and triaging ophthalmic diseases. To assess the AI system's performance through a two-stage cross-sectional study, starting with silent evaluation and followed by early clinical evaluation in outpatient clinics.
Methods and analysisOur study will be conducted across three distinct centers in Shanghai, Nanjing, and Suqian. The development of the smartphone-based multimodal AI system will take place in Shanghai with the goal of achieving ≥90% sensitivity and ≥95% specificity for diagnosing and triaging ophthalmic diseases. The first stage of the cross-sectional study will explore the system's performance in Shanghai's outpatient clinics. Medical histories will be collected without patient interaction, and anterior segment images will be captured using slit lamp equipment. This stage aims for ≥85% sensitivity and ≥95% specificity with a sample size of 100 patients. The second stage will take place at three locations, with Shanghai serving as the internal validation dataset, and Nanjing and Suqian as the external validation dataset. Medical history will be collected through patient interviews, and anterior segment images will be captured via smartphone devices. An expert panel will establish reference standards and assess AI accuracy for diagnosis and triage throughout all stages. A one-vs.-rest strategy will be used for data analysis, and a post-hoc power calculation will be performed to evaluate the impact of disease types on AI performance.
DiscussionOur study may provide a user-friendly smartphone-based multimodal AI system for diagnosis and triage of ophthalmic diseases. This innovative system may support early detection of ocular abnormalities, facilitate establishment of a tiered healthcare system, and reduce the burdens on tertiary facilities.
Trial registrationThe study was registered in ClinicalTrials.gov on June 25th, 2023 (NCT 05930444).

引言：人工智能（Artificial Intelligence, AI）技术在疾病诊断与分诊领域已取得长足进展。在眼科疾病范畴内，基于影像的诊断虽已实现较高准确率，但因缺乏患者病史信息仍存在显著局限性。ChatGPT的问世实现了自然的人机交互，为整合交互文本与影像信息的多模态AI系统开发提供了可行路径。

研究目的：开发一款基于ChatGPT与眼前节图像（anterior segment images）的多模态AI系统，用于眼科疾病的诊断与分诊；并通过两阶段横断面研究评估该系统的性能，第一阶段为静默评估，第二阶段为门诊早期临床评估。

研究方法与分析方案：本研究将在上海、南京、宿迁三个独立临床中心开展。基于智能手机的多模态AI系统将在上海完成开发，目标是在眼科疾病诊断与分诊任务中达到≥90%的灵敏度与≥95%的特异度。横断面研究的第一阶段将在上海的门诊场景中进行：无需与患者交互即可收集病史信息，同时使用裂隙灯设备（slit lamp equipment）采集眼前节图像，该阶段计划纳入100名患者，目标达到≥85%的灵敏度与≥95%的特异度。第二阶段将在三个中心同步开展，以上海的研究数据作为内部验证集（internal validation dataset），南京与宿迁的研究数据作为外部验证集（external validation dataset）；该阶段将通过与患者面谈收集病史信息，并使用智能手机设备采集眼前节图像。专家委员会将全程制定参考标准，并评估AI系统在诊断与分诊任务中的准确率。数据分析将采用一对多策略（one-vs.-rest strategy），并通过事后功效分析（post-hoc power calculation）评估不同疾病类型对AI系统性能的影响。

讨论：本研究有望推出一款易用的基于智能手机的多模态AI系统，用于眼科疾病的诊断与分诊。该创新系统可助力眼部异常的早期筛查，推动分级医疗体系的构建，并减轻三级医疗机构的接诊负担。

试验注册：本研究于2023年6月25日在临床试验注册平台（ClinicalTrials.gov）完成注册，注册号为NCT 05930444。