The Convergence of Artificial Intelligence, FAIR Data Frameworks, and Regulatory Standards in the Digital Transformation of Healthcare

Authors: Pinaki Bose

DOI: https://doi.org/10.37082/IJIRMPS.v11.i6.233138

Short DOI: https://doi.org/

Country: United States

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Abstract: The integration of advanced computational models and standardized data management architectures is currently driving a fundamental shift in the global healthcare ecosystem. This transformation is characterized by a transition from traditional, hospital-centric, and reactive care models to proactive, distributed, and personalized healthcare paradigms. Central to this evolution is the implementation of the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles, which serve as the essential infrastructure for scaling artificial intelligence (AI) and machine learning (ML) applications within the clinical domain. As healthcare systems increasingly rely on high-velocity data from the Medical Internet of Things (IoMT) and real-time patient monitoring, the inadequacy of conventional data processing cycles has necessitated the development of reactive, non-intrusive Extract-Transform-Load (ETL) systems capable of maintaining soft real-time data availability for strategic decision-making.
Simultaneously, the rise of Large Language Models (LLMs) has revolutionized the ability of informatics systems to parse, synthesize, and validate complex medical literature and patient records. These technical advancements are occurring within a rigorous regulatory landscape, where the Food and Drug Administration (FDA) has established clear frameworks for the use of Real-World Data (RWD) and Real-World Evidence (RWE) to support the approval of new medical indications and post-approval study requirements. However, the spread of predictive technologies also poses major legal and ethical challenges concerning autonomy, purpose limitation, and algorithmic bias. This report examines the technical, architectural, and regulatory pillars of this digital transition, providing a comprehensive analysis of the mechanisms driving the future of healthcare informatics.

Keywords: Artificial Intelligence, Large Language Models, FAIR Data Principles, Real-World Evidence, Digital Health Transformation, Real-Time ETL, Common Data Models, Semantic Interoperability, Clinical Informatics, Regulatory Compliance, Decisional Autonomy, Medical Internet of Things.

Paper Id: 233138

Published On: 2023-12-08

Published In: Volume 11, Issue 6, November-December 2023

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