Scholarlink

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.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Practical and Ethical Considerations for Generative AI in Medical Imaging

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • 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

摘要

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.