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Practical and Ethical Considerations for Generative AI in Medical Imaging

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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Category

Academic chapter

Language

English

Author(s)

  • Debesh Jha
  • Ashish Rauniyar
  • Desta Haileselassie Hagos
  • Vanshali Sharma
  • Nikhil Kumar Tomar
  • Zheyuan Zhang
  • Ilkin Isler
  • Gorkem Durak
  • Michael Wallace
  • Cemal Yazici
  • Tyler Berzin
  • Koushik Biswas
  • Ulas Bagci

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies
  • USA
  • Howard University
  • University of Central Florida
  • Northwestern University
  • University of Chicago
  • Harvard Medical School

Year

2024

Publisher

Springer

Book

Ethics and Fairness in Medical Imaging: Second International Workshop on Fairness of AI in Medical Imaging, FAIMI 2024, and Third International Workshop on Ethical and Philosophical Issues in Medical Imaging, EPIMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 6–10, 2024, Proceedings

ISBN

9783031727870

Page(s)

176 - 187

View this publication at Norwegian Research Information Repository