Abstract
Generative Artificial Intelligence (AI) has the potential to transform medicine. It is helpful to clinicians and radiologists for diagnosis, screening, treatment planning, interventions, and drug development. It benefits the clinical flow with real-time decision-support systems. While generative AI can potentially improve healthcare, it also introduces new ethical issues that require careful analysis and mitigation strategies. This work emphasizes the ethical aspects of generative AI in medical imaging, aiming to ensure that advancements in this field align with established ethical principles and societal values. We delve into the ethical implications surrounding bias, fairness, patient privacy, consent, transparency, explainability, intellectual property, and data ownership. Furthermore, we discuss regulations governing the use of synthetic medical data. To promote equitable application of these powerful tools, we also propose clear guidelines for promoting fairness, mitigating bias, and ensuring diversity within generative AI models.
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Acknowledgements
This project is supported by NIH funding: R01-CA246704, R01-CA240639, U01-DK127384-02S1, and U01-CA268808.
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Jha, D. et al. (2025). Practical and Ethical Considerations for Generative AI in Medical Imaging. In: Puyol-Antón, E., et al. Ethics and Fairness in Medical Imaging. FAIMI EPIMI 2024 2024. Lecture Notes in Computer Science, vol 15198. Springer, Cham. https://doi.org/10.1007/978-3-031-72787-0_17
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