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Today, scientists agree that good research is ethical in nature and is guided by a basic respect for human dignity and safety. However, this has not always been the case. Modern researchers must demonstrate that the research they perform is ethically sound.
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Ethical Knowledge, Challenges, and Institutional Strategies Among Medical AI Developers and Researchers: Focus Group

Sophia Fantus1, Jinxu Li2, Tianci Wang3

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Medical AI developers need better ethics training and institutional support to address challenges like bias and privacy. Enhancing ethical development is crucial for responsible AI in healthcare.

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Area of Science:

  • Medical Artificial Intelligence (AI)
  • Clinical Decision-Making
  • Healthcare Ethics

Background:

  • AI integration in healthcare raises ethical concerns (bias, privacy, transparency).
  • Limited understanding of how AI developers perceive and address these ethical challenges.
  • Developers' ethical awareness influences AI tool conceptualization and deployment.

Purpose of the Study:

  • Examine AI developers' knowledge, attitudes, and experiences with AI ethics.
  • Identify recommendations for ethics training and support in medical AI development.

Main Methods:

  • Conducted 2 semistructured focus groups with 13 US-based AI developers/researchers.
  • Covered diverse medical AI applications (e.g., Alzheimer's prediction, clinical imaging).
  • Applied qualitative content analysis to identify themes.

Main Results:

  • Ethics knowledge acquired informally (literature, feedback, mentorship), not structured training.
  • Key ethical challenges: data bias, patient privacy, generative AI, commercialization pressures.
  • Concerns about downstream patient care effects and rapid innovation outpacing regulation.

Conclusions:

  • Medical AI developers face ethical challenges but lack structured support.
  • Institutions must embed ethics into research via practical tools and interdisciplinary partnerships.
  • Strengthening support is vital for ethical AI development in healthcare.