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<p>This article addresses the complexities and ethical concerns surrounding data collection and artificial intelligence (AI) in general and particularly in medical contexts. It highlights the challenges posed by AI in adhering to the “5 Ps of ethical data handling” (provenance, protection, purpose, preparation, and privacy) and emphasizes the need of supplementation of existing regulations such as the GDPR to fully ensure ethical data practices. This article explores both technical and nontechnical methods for managing sensitive data, with a particular focus on distributed machine learning (DML). DML, while promising for secure and collaborative medical data management, raises unique ethical issues that require thorough examination. The paper underscores the need for a synergy between technological advancements and ethical considerations to uphold values such as patient autonomy, data privacy, and justice in AI applications. It also provides an in-depth analysis of different DML methods (split learning, federated learning, and swarm learning) and their potential applications and drawbacks in healthcare, stressing the importance of developing ethical, secure, and transparent AI systems to prevent misuse and ensure patient trust. </p>

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Ethical challenges and solutions in AI-driven medical data management: a focus on distributed machine learning

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年5月10日

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  • Martin Hähnel

摘要

This article addresses the complexities and ethical concerns surrounding data collection and artificial intelligence (AI) in general and particularly in medical contexts. It highlights the challenges posed by AI in adhering to the “5 Ps of ethical data handling” (provenance, protection, purpose, preparation, and privacy) and emphasizes the need of supplementation of existing regulations such as the GDPR to fully ensure ethical data practices. This article explores both technical and nontechnical methods for managing sensitive data, with a particular focus on distributed machine learning (DML). DML, while promising for secure and collaborative medical data management, raises unique ethical issues that require thorough examination. The paper underscores the need for a synergy between technological advancements and ethical considerations to uphold values such as patient autonomy, data privacy, and justice in AI applications. It also provides an in-depth analysis of different DML methods (split learning, federated learning, and swarm learning) and their potential applications and drawbacks in healthcare, stressing the importance of developing ethical, secure, and transparent AI systems to prevent misuse and ensure patient trust.