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Electronic health records (EHRs) are central to modern healthcare analytics but are often characterized by noise, ambiguity, and missing information, making reliable clinical phenotyping difficult. Clinical phenotypes—observable characteristics derived from patient data—are essential for diagnosis, prognosis, and treatment planning. Yet, traditional supervised machine learning methods depend on large volumes of high-quality annotated data that are difficult to obtain at scale.This review examines the role of weak supervision in enabling scalable clinical phenotyping from noisy and heterogeneous EHR data. Weak supervision frameworks generate labels using heuristic rules, knowledge-based signals, or programmatic labeling functions, allowing models to learn from large datasets without extensive expert annotation. These approaches help address challenges such as inconsistent terminology, missing values, and temporal irregularities commonly found in clinical records.We synthesize recent developments in scalable phenotyping systems that integrate machine learning architectures, probabilistic labeling strategies, and multimodal data representations to extract meaningful patterns from imperfect clinical data. The review also outlines a systems-level perspective on healthcare analytics pipelines, covering data ingestion, model training under label uncertainty, deployment in clinical environments, and governance considerations for responsible AI integration.Overall, weak supervision emerges as a practical strategy for transforming noisy EHR data into usable clinical intelligence, enabling more scalable and trustworthy analytics for healthcare decision support.

Artificial intelligence (AI) has emerged as a transformative force in healthcare systems and analytics, enabling the processing of vast clinical datasets to support diagnostics, prognostics, and personalized interventions. This narrative review synthesizes literature on clinical data engineering for healthcare AI, with a focused examination of labeling theory, data quality assurance, and temporal structuring standards. These elements form the foundational infrastructure for robust AI-driven healthcare systems, addressing the challenges of heterogeneous data sources, bias mitigation, and dynamic patient trajectories.Clinical data engineering encompasses the systematic preparation, integration, and optimization of healthcare data for AI models. Labeling theory, rooted in supervised learning paradigms, involves the annotation of data to train algorithms, but extends to considerations of label accuracy, inter-observer variability, and semi-supervised approaches to reduce manual effort. Data quality assurance ensures reliability through preprocessing, bias detection, and validation protocols, critical for avoiding “garbage in, garbage out” scenarios in clinical applications. Temporal structuring standards facilitate the handling of time-series data, such as electronic health records (EHRs) and longitudinal imaging, enabling predictive modeling of disease progression and real-time decision support.The review highlights AI’s role in healthcare analytics, from image-based diagnostics (e.g., dermatology and retinal disease classification) to system-level optimizations (e.g., resource allocation and workflow efficiency). It underscores the convergence of human and AI intelligence for high-performance medicine, emphasizing ethical implementations to mitigate disparities. Synthesizing cross-study insights, we propose an original framework for integrative data engineering that prioritizes interoperability, fairness, and adaptability across healthcare infrastructures.Key applications include deep learning for stroke management, cancer detection, and cardiovascular risk prediction, where data engineering directly impacts model efficacy. Challenges such as data silos, regulatory gaps, and temporal drift are addressed through original interpretive structures, including a conceptual pipeline for end-to-end AI analytics. This review positions clinical data engineering as essential for sustainable AI integration, advocating for systems-level framing that bridges data ingestion, model deployment, and governance to enhance clinical outcomes and equity in global health systems.