OphthaDT: Generative Digital Twins for Forecasting Visual Acuity Trajectories in Ophthalmology

Pietro Belligoli, Nikita Makarov, Sayedali Shetab Boushehri, Fabian Schmich, Raul Rodriguez-Esteban, Michael Patrick Menden

Keywords: digital twins, large language models, foundation models, clinical trajectory forecasting, time-series forecasting, ophthalmology, structured clinical data, clinical trials, precision medicine, drug development

TL;DR: An LLM-based digital twin that forecasts visual acuity trajectories in ophthalmology by serializing patient histories as text.

Abstract: Precision medicine in ophthalmology requires accurate longitudinal predictions, but the fragmented nature of multimodal clinical data remains a major barrier to accurate forecasting. We introduce OphthaDT, a large language model (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 established 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 explicit imputation, positioning LLM-based clinical trajectory modeling as a methodology that could reduce patient burden and accelerate drug development.

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Submission Number: 7