With the rapid evolution of artificial intelligence (AI) technologies, the medical industry is undergoing a profound transformation driven by data intelligence. As the foundational element for intelligent diagnosis, precision prevention, and public health governance, medical data is characterized by massive volume, complex structure, diverse sources, high dimensionality, strong privacy, and high timeliness. Traditional data analysis methods are no longer sufficient to meet the comprehensive requirements of data security, intelligent processing, and decision support. Through techniques such as machine learning, deep learning, natural language processing, and multimodal fusion, AI provides robust technical support for medical data cleaning, governance, mining, and application. At the data level, intelligent algorithms enable the standardization, structuring, and interoperability of medical data, promoting information sharing across medical systems. At the model level, AI supports auxiliary diagnosis and precision treatment through image recognition, medical record analysis, and knowledge graph construction. At the system level, intelligent decision-support platforms continuously enhance the efficiency and accuracy of healthcare services. However, the widespread adoption of AI in medicine still faces challenges such as privacy protection, data security, model interpretability, and the lack of unified industry standards. Based on a systematic review of AI’s key supporting technologies in medical data processing and application, this paper focuses on the compliance challenges and adaptation strategies during industry integration and proposes an adaptation framework centered on “technological trustworthiness, data security, and industry collaboration.” The study provides theoretical and practical insights for promoting the standardized and sustainable development of AI in the healthcare industry.