Synthetic Health Data: Real Ethical Promise and Peril.

Area of Science:

• Health Informatics
• Data Science
• Bioethics

Background:

• Machine-generated synthetic data is increasingly used in health science to improve data access and address privacy concerns.
• While promising, the use of synthetic data introduces significant ethical, legal, and policy challenges.

Purpose of the Study:

• To critically examine the ethical, legal, and policy implications of using synthetic data in health science.
• To propose a framework for more meaningful engagement of data subjects in the synthetic data lifecycle.

Main Methods:

• This study presents a critical analysis of existing literature and case studies on synthetic data in healthcare.
• It employs ethical reasoning and policy analysis to identify potential risks and benefits.

Main Results:

• Synthetic data usage raises concerns regarding privacy, security, accuracy, reliability, fairness, bias, and regulatory compliance.
• Current approaches often overlook the importance of data subject involvement in synthetic data generation and validation.

Conclusions:

• Addressing the ethical challenges of synthetic data requires a paradigm shift towards greater data subject engagement.
• Developing robust institutional safeguards and collaborative frameworks is essential for the responsible innovation and application of synthetic data in health.