Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses - Ruiqi Ma; Qian Cheng; Jing Yao; Zhiyu Peng; Mingxu Yan; Jie Lu; Jingjing Liao; Lejin Tian; Wenjun Shu; Yunqiu Zhang; Jinghan Wang; Pengfei Jiang; et al. (npj Digital Medicine)
Abstract Chatbot-based multimodal AI holds promise for collecting medical histories and diagnosing ophthalmic diseases using textual and imaging data. This study developed and evaluated the ChatGPT-powered Intelligent Ophthalmic Multimodal Interactive Diagnostic System (IOMIDS) to enable patient self-diagnosis and self-triage. IOMIDS included a text model and three multimodal models (text + slit-lamp, text + smartphone, text + slit-lamp + smartphone). The performance was evaluated through a two-stage cross-sectional study across three medical centers involving 10 subspecialties and 50 diseases. Using 15640 data entries, IOMIDS actively collected and analyzed medical history alongside slit-lamp and/or smartphone images. The text + smartphone model showed the highest diagnostic accuracy (internal: 79.6%, external: 81.1%), while other multimodal models underperformed or matched the text model (internal: 69.6%, external: 72.5%). Moreover, triage accuracy was consistent across models. Multimodal approaches enhanced response quality and reduced misinformation. This proof-of-concept study highlights the potential of chatbot-based multimodal AI for self-diagnosis and self-triage. (The clinical trial was registered on June 26, 2023, on ClinicalTrials.gov under the registration number NCT05930444.).