The integration of Artificial Intelligence (AI) in healthcare promises unprecedented improvements in patient care, yet its full potential, especially in precision health, remains underutilized due to significant challenges in transforming real-world data into real-world evidence. This dissertation explores the development and application of clinical foundation models (FMs), specifically Clinical Language Models (CLaMs) and Foundation Models for Electronic Health Records (FEHRs), which are pretrained on extensive clinical narratives and structured records. The research presents innovations in several key areas: Accurate information extraction from clinical notes: We analyzed baseline CLaMs performance in the task of extracting diagnostic code information from clinical notes. We identified two key issues in these CLaMs: their imprecision in extracting rare diseases due to the lack of training data, and their difficulties with recognizing synonyms due to model’s inadequate medical knowledge. To address these issues, we developed a generative knowledge-injected prompt-based fine-tuned transformer, achieving state-of-the-art accuracy. Accurate information extraction paves the road for better-informed decisions during clinical diagnoses. Enhanced quality of patient health assessments: Inferring clinical diagnosis to generate an assessment is a crucial step during the patient encounter. However, there is limited research on generating clinical diagnoses in a free text format. Hence, we propose a new task of generating full-length patient health assessments. We applied CLaM to this task and found that it tend to generate factually incorrect responses. To improve the generated assessment quality, we combined the CLaM with the medical knowledge graph. By reducing the incidence of misleading information generated during the assessment process, our CLaM supports clinicians in making better-informed decisions. Predictive modeling of complex disease interrelations: We developed TransformEHR, a generative transformer FEHR pretrained on a vast dataset of 6.5 million patient electronic health records. With visit level pretraining objective, TransformEHR is designed for predicting complex interrelations among diseases. The high performance in predicting intentional self-harm shows the potential of TransformEHR in building effective clinical intervention systems. TransformEHR is also generalizable and can be easily fine-tuned for clinical prediction tasks with limited data. HealthMamba are multi-purpose long context extraction and prediction engines: Traditional transformer-based FMs have limited performance in long EHR and struggle to extract information from locations distant from the end of the document (position bias). To mitigate this issue, we developed HealthMamba, a Mamba-based FM that addresses the issue of long medical history. HealthMamba uses selective scan algorithm allowing the model to selectively propagate or forget information along the sequence length depending on the input token. With prefix prompt, HealthMamba significantly outperforms transformer-based FMs in 7 clinical information extraction tasks and patient outcome prediction tasks. Notably, it demonstrated less position bias compared to GPT-4, maintaining effectiveness across all parts of EHRs. By training advanced generative clinical FMs on large-scale healthcare data, this dissertation demonstrates AI’s role in enhancing precision health for more personalized and effective healthcare solutions. The findings underscore the potential of AI to transform medical data analysis and patient care, setting a path towards a future where healthcare is increasingly driven by intelligent and automated systems to support healthcare providers.