Fig. 3: Internal and external evaluation of IOMIDS performance on diagnosis and triage. | npj Digital Medicine

Fig. 3: Internal and external evaluation of IOMIDS performance on diagnosis and triage.

From: Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses

Fig. 3

a Radar charts of disease-specific diagnosis (red) and triage (green) accuracy after clinical evaluation of the text model in internal (left, Dataset 1) and external (right, Dataset 6) centers. Asterisks indicate significant differences between diagnosis and triage accuracy based on Fisher’s exact test. b Circular stacked bar charts of disease-specific diagnostic accuracy across different models from internal (left, Dataset 2–4) and external (right, Dataset 7–9) evaluations. Solid bars represent the text model, while hollow bars represent multimodal models. Asterisks indicate significant differences in diagnostic accuracy between two models based on Fisher’s exact test. c Bar charts of overall accuracy (upper) and accuracy of primary anterior segment diseases (lower) for diagnosis (red) and triage (green) across different models in Dataset 2–5 and Dataset 7–10. The line graphs below denote study centers (internal, external), models used (text, text + slit-lamp, text + smartphone, text + slit-lamp + smartphone), and data provider (researchers, patients). * P < 0.05, ** P < 0.01, *** P < 0.001, **** P < 0.0001.

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