Transforming unstructured clinical data by AI-based decision tools allows embedding models in nearly every patient care, device, and clinical trial information pathway. Such systems remove the need for human-driven data cleaning and parsing, and allow for fully automated solutions to segment medical images, analyze samples, and retrieve critical EMR-level information. Focusing on these approaches for pharma, life sciences, and providers, we identify methods to develop AI tools in a regulated environment and build pipelines for medical subject-matter experts. We discuss the transferable principles found in most deep learning projects, advanced research, and medical imaging, and examining existing tools to accelerate adoption. With the potential to speed up the diagnostic process, enable higher-quality assessment of indications, and standardize reproducible decisions, providers, payers, and healthcare organizations look to leverage these emerging technologies and close capability gaps.