In clinical trials, AI has already been applied to personal disease prediction. Rajpurkar et al. utilized gradient boosted decision trees to predict the progression of depressive symptoms in patients receiving antidepressant therapy, incorporating pre-treatment symptom scores and electroencephalographic measurements. Hong et al. utilized an ensemble of classifiers to predict toxicity by considering drug properties and target property features. de Jong et al. constructed a model that integrates genetics data to anticipate drug response in patients with neurological disorders. Wang and Sun developed a transformer-based approach for modeling and predicting the survival rate of breast oncology patients. Additionally, they suggested a transferable transformer model that utilizes information from various oncology trials to improve mortality predictions for individual trials, showcasing its potential adaptability across heterogeneous datasets .