Comprehensive overview of the integration of Artificial Intelligence (AI) and Machine Learning (ML) into healthcare, spanning from drug discovery and development to clinical practice and regulatory oversight. Several documents detail the technical applications of ML, such as supervised learning models and deep learning networks, in areas like disease diagnosis, electronic health record (EHR) analysis, and patient risk prediction, noting the potential for significant cost savings and improved efficiency. Crucially, a major focus across the texts is the ethical and regulatory landscape, addressing concerns related to algorithmic bias, data privacy (especially under GDPR and HIPAA), patient informed consent, and the legal liability for harm caused by autonomous AI systems. The sources also highlight international efforts, including EU regulations like the AI Act and the European Health Data Space, and guidance from the WHO and the US FDA, which are establishing frameworks for the safe, trustworthy, and effective deployment of high-risk AI-enabled medical devices.