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<p>A decade ago, we characterized big data in healthcare as a nascent field anchored in distributed computing paradigms. The intervening years have witnessed a transformation so profound that revisiting our original framework is essential. This paper critically examines the evolution of big data in healthcare and medicine, assessing the shift from Hadoop-centric architectures to cloud computing platforms and GPU-accelerated artificial intelligence, including large language models and the emerging paradigm of agentic AI.</p><p>The landscape has been reshaped by landmark biobank initiatives, breakthrough applications such as AlphaFold's Nobel Prize-winning solution to protein structure prediction, and the rapid growth of FDA-cleared AI medical devices from fewer than ten in 2015 to over 1200 by mid-2025. AI has enabled advances across precision oncology, drug discovery, and public health surveillance.</p><p>Yet new challenges have emerged: algorithmic bias perpetuating health disparities, opacity undermining clinical trust, environmental sustainability concerns, and unresolved questions of privacy, security, data ownership, and interoperability. We propose extending the original “4Vs” framework to accommodate veracity through explainability, validity through fairness, and viability through sustainability. The paper concludes with prescriptive implications for healthcare organizations, technology developers, policymakers, and researchers.</p>

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Big data in healthcare and medicine revisited: design and managerial challenges in the age of artificial intelligence

  • 期刊论文

  • 发表日期：

    2026年2月6日

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  • Wullianallur Raghupathi,
  • Viju Raghupathi

摘要

A decade ago, we characterized big data in healthcare as a nascent field anchored in distributed computing paradigms. The intervening years have witnessed a transformation so profound that revisiting our original framework is essential. This paper critically examines the evolution of big data in healthcare and medicine, assessing the shift from Hadoop-centric architectures to cloud computing platforms and GPU-accelerated artificial intelligence, including large language models and the emerging paradigm of agentic AI.

The landscape has been reshaped by landmark biobank initiatives, breakthrough applications such as AlphaFold's Nobel Prize-winning solution to protein structure prediction, and the rapid growth of FDA-cleared AI medical devices from fewer than ten in 2015 to over 1200 by mid-2025. AI has enabled advances across precision oncology, drug discovery, and public health surveillance.

Yet new challenges have emerged: algorithmic bias perpetuating health disparities, opacity undermining clinical trust, environmental sustainability concerns, and unresolved questions of privacy, security, data ownership, and interoperability. We propose extending the original “4Vs” framework to accommodate veracity through explainability, validity through fairness, and viability through sustainability. The paper concludes with prescriptive implications for healthcare organizations, technology developers, policymakers, and researchers.