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<p>The secondary use of medical data, amplified by the power of artificial intelligence and deep learning, holds immense promise for transforming healthcare discovery and delivery. However, navigating this landscape requires careful consideration of the intricate interplay between data sources, computational methods, and profound ethical responsibilities. This paper provides a comprehensive analysis of this domain, beginning with an overview of diverse medical datasets—spanning electronic health records, imaging, and genomics—and the fundamental deep learning architectures adept at extracting insights from these complex modalities. We then critically examine the significant hurdles inherent in this work: the technical challenges posed by data heterogeneity and limitations, and the paramount ethical imperatives surrounding patient privacy, informed consent, data security, and the mitigation of algorithmic bias that could deepen health inequities. The discussion extends to advanced computational strategies specifically developed to enhance model robustness, preserve privacy, and effectively utilize available data under these constraints. Synthesizing these multifaceted considerations—data characteristics, modeling capabilities, technical solutions, ethical mandates, and regulatory boundaries—reveals the critical need for a unified, principled approach. Addressing this gap, we introduce the FAIR-MEDS framework (Fair, Accountable, Interpretable, Robust—MEdical Data Secondary-use). FAIR-MEDS operationalizes responsible innovation by proposing a structured, five-phase workflow. This framework is designed to systematically embed ethical considerations, rigorous validation, and transparent practices throughout the entire lifecycle of secondary medical data analysis, particularly when employing deep learning. By connecting the potential of advanced analytics with the non-negotiable requirements for ethical conduct and trustworthiness, FAIR-MEDS offers a practical pathway to harness medical data responsibly, ultimately fostering innovation that benefits society.</p>

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Secondary use of health data: applications, models, algorithms, and ethical considerations

  • 期刊论文

  • 发表日期：

    2026年2月22日

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  • Mahmoud Soliman,
  • Omar Abdelziz,
  • Ahmed Radwan,
  • Mohamed S. Shehata,
  • Charlene Ronquillo,
  • Karis Shearer

摘要

The secondary use of medical data, amplified by the power of artificial intelligence and deep learning, holds immense promise for transforming healthcare discovery and delivery. However, navigating this landscape requires careful consideration of the intricate interplay between data sources, computational methods, and profound ethical responsibilities. This paper provides a comprehensive analysis of this domain, beginning with an overview of diverse medical datasets—spanning electronic health records, imaging, and genomics—and the fundamental deep learning architectures adept at extracting insights from these complex modalities. We then critically examine the significant hurdles inherent in this work: the technical challenges posed by data heterogeneity and limitations, and the paramount ethical imperatives surrounding patient privacy, informed consent, data security, and the mitigation of algorithmic bias that could deepen health inequities. The discussion extends to advanced computational strategies specifically developed to enhance model robustness, preserve privacy, and effectively utilize available data under these constraints. Synthesizing these multifaceted considerations—data characteristics, modeling capabilities, technical solutions, ethical mandates, and regulatory boundaries—reveals the critical need for a unified, principled approach. Addressing this gap, we introduce the FAIR-MEDS framework (Fair, Accountable, Interpretable, Robust—MEdical Data Secondary-use). FAIR-MEDS operationalizes responsible innovation by proposing a structured, five-phase workflow. This framework is designed to systematically embed ethical considerations, rigorous validation, and transparent practices throughout the entire lifecycle of secondary medical data analysis, particularly when employing deep learning. By connecting the potential of advanced analytics with the non-negotiable requirements for ethical conduct and trustworthiness, FAIR-MEDS offers a practical pathway to harness medical data responsibly, ultimately fostering innovation that benefits society.