OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology
For ophthalmology researchers and clinicians, OphthaDT offers a method to predict disease progression from irregularly sampled data, potentially reducing patient burden and accelerating drug development.
OphthaDT, an LLM-based digital twin, forecasts visual acuity trajectories from multimodal clinical data of 3,220 patients across four Phase III trials, achieving up to 6.0% MAE reduction in nAMD and outperforming Random Forest and XGBoost in DME.
Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a barrier to forecasting. We introduce OphthaDT, an LLM-based digital twin for ophthalmology that serializes longitudinal patient histories from 3,220 patients across four Phase III clinical trials into structured narratives to forecast best corrected visual acuity (BCVA). In benchmarks spanning up to 100 weeks, OphthaDT demonstrated the lowest prediction error in neovascular age-related macular degeneration (nAMD), achieving an average mean absolute error (MAE) reduction of 6.0% compared to all baselines. In diabetic macular edema (DME), OphthaDT demonstrated competitive performance against all baselines while outperforming Random Forest and XGBoost by an average MAE reduction of 2.6% and 6.9%, respectively. Results reveal that OphthaDT's predictive advantage scales with trajectory complexity: whereas linear models remain effective for the more stable treatment responses of DME, OphthaDT's capacity is better suited for capturing the high longitudinal variability of nAMD. Finally, OphthaDT handles irregular sampling without imputation, positioning LLM-based clinical trajectory modeling as a methodology that could reduce patient burden and accelerate drug development.