Artificial intelligence in a commercial clinical decision support tool: Insightful or dominated by heuristics?

PURPOSE: To systematically evaluate a commercially available myopia risk artificial intelligence (AI) supported clinical decision support screening tool in pediatric patients and to determine the additional insights provided by the AI component. METHODS: Eighteen synthetic images of myopic-appearing and 18 non-myopic-appearing pediatric 'subjects' were generated using three commercially available AI software (ChatGPT, Google Gemini, and Reve), and uploaded to Magnifeye, which performs AI-guided image analysis and assessment of environmental risk factors to evaluate output myopia risk. The first analysis was image-based, where we assessed each image but modified the environmental risk to the lowest possible and the highest possible level. The second analysis was scenario-based, where we assessed two 'subjects', and systematically evaluated all combinations of environmental risk. RESULTS: There were significant differences in eye aspect ratio between myopic and non-myopic groups (squinting). Under low environmental risk conditions, both myopic and non-myopic groups were regarded as low overall risk of myopia. Conversely, high environmental risk led to a high overall risk of myopia. Systematically adjusting the environmental risk factors demonstrated a gradual uptitration of overall myopia risk. Family history of myopia and proximity behaviors represented the highest environmental risk. A non-myopic appearance moderated the overall myopia risk, decreasing it slightly in some situations. CONCLUSIONS: A simple clinical problem with a small number of outcomes in a low-stakes situation could be crudely reverse-engineered. This highlights the notion that some clinical issues may not necessarily require a complex AI system, which may be no more effective than conventional clinical heuristics.