This investigation demonstrates improved drug response prediction and matching efficiency using deep learning, highlighting its role in personalized medicine.
Key Points
Deep learning techniques can optimize personalized treatment strategies, improving therapeutic efficacy significantly.
Transformer-based models achieved 91.2% accuracy and an AUC-ROC of 0.92 in drug response predictions.
Utilizing a diverse dataset, deep learning models improved drug-patient matching efficiency by 20–30% over traditional methods.
Challenges like data privacy and model interpretability must be addressed for broader adoption of AI in clinical practice.