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Ethical Standards II

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Ethical standards are the backbone of nursing practice, guiding nurses as they interact with patients, families, and colleagues. These standards are crucial for providing safe, empathetic care centered on the patient's needs.
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Ethical Considerations in Patient Privacy and Data Handling for AI in Cardiovascular Imaging and Radiology.

Saba Mehrtabar¹, Ahmed Marey², Anushka Desai³

¹Tabriz University of Medical Sciences, Tabriz, Iran.

Journal of Imaging Informatics in Medicine

|September 24, 2025

Summary

Artificial intelligence (AI) in medical imaging improves diagnostics but raises ethical concerns regarding patient privacy and data security. Responsible AI implementation requires shared accountability and robust governance for trust and equity.

Keywords:

Artificial intelligence (AI)Blockchain in healthcare Cardiovascular imaging Data handling protocols Data security Ethical and legal considerations Informed consent Machine learning in medical imaging Patient privacy Radiology ethics

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Area of Science:

Cardiovascular Imaging
Radiology
Medical Artificial Intelligence

Background:

Artificial intelligence (AI) integration in cardiovascular imaging and radiology promises enhanced diagnostics and personalized care.
Rapid AI adoption presents significant ethical challenges, including patient privacy, data handling, consent, and ownership.
Existing ethical and legal frameworks struggle to keep pace with AI advancements.

Purpose of the Study:

To review the ethical challenges of AI in cardiovascular imaging and radiology.
To synthesize literature from clinical, technical, and regulatory viewpoints.
To compare international ethical and legal frameworks and propose mitigation strategies.

Main Methods:

Narrative review synthesizing existing literature.
Analysis of ethical considerations: data utility vs. protection, transparency, explainable AI.
Comparison of regulatory landscapes (EU, USA, China) and AI vulnerabilities (cloud, adversarial attacks, commercial data).

Main Results:

Tensions exist between maximizing data utility and ensuring data protection.
Vulnerabilities arise from cloud computing, adversarial attacks, and commercial datasets.
Disparities in ethical and legal frameworks necessitate harmonized approaches.

Conclusions:

Ethical AI implementation requires shared accountability among clinicians, developers, institutions, and policymakers.
Mitigation strategies like federated learning, blockchain, and differential privacy are crucial.
Prioritizing patient trust, fairness, and equity through robust governance and transparent data stewardship is essential for responsible AI development.