🤖 AI Summary
This study addresses the challenge of long-term visual trajectory prediction in ophthalmic precision medicine, hindered by fragmented multimodal clinical data. It proposes the first large language model (LLM)-driven digital twin system for ophthalmology, which transforms longitudinal medical histories from 3,220 patients into structured clinical narratives. The system effectively handles irregularly sampled time-series data without requiring imputation, demonstrating enhanced modeling capacity under high clinical variability. In neovascular age-related macular degeneration (nAMD) prediction, it achieves a 6.0% reduction in mean absolute error (MAE) compared to existing baselines. For diabetic macular edema (DME), it outperforms Random Forest and XGBoost by 2.6% and 6.9% in MAE, respectively. These results validate the innovation and superiority of LLM-powered generative digital twins in longitudinal ophthalmic forecasting.
📝 Abstract
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.