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 - Peng, Zhiyu; Ma, Ruiqi; Zhang, Yihan; Yan, Mingxu; Lu, Jie; Cheng, Qian; Liao, Jingjing; Zhang, Yunqiu; Wang, Jinghan; Zhao, Yue; Zhu, Jiang; Qin, Bing; et al. (Frontiers in Artificial Intelligence, 2023)
Introduction: Artificial 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. Objective: To 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 analysis: Our 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 and Nanjing serving as the internal validation dataset, 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-versus-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. Discussion: Our 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.