This paper presents an AI-based multimodal voice identity recognition system designed for medical institutions. The system integrates OCR, ASR, and deep-learning speaker verification to achieve reliable patient identity confirmation. Experimental evaluations show that ECAPA-TDNN reaches 96% accuracy, remains robust under noise, and maintains high repeat-speaker consistency.