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Updated: Oct 21, 2025

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Foundational Considerations for Artificial Intelligence Using Ophthalmic Images.

Michael D Abràmoff1, Brad Cunningham2, Bakul Patel3

  • 1Department of Ophthalmology and Visual Sciences, University of Iowa, Iowa City, Iowa; Department of Elecrical and Computer Engineering, University of Iowa, Iowa City, Iowa; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa.

Ophthalmology
|September 3, 2021
PubMed
Summary
This summary is machine-generated.

Developing bioethically sound artificial intelligence (AI) in medicine requires collaboration among stakeholders. Addressing ethical principles like nonmaleficence, autonomy, and equity is crucial for AI

Keywords:
Artificial intelligenceAugmented intelligenceClinical standardsClinical trialCorneaEthicsFDAGlaucomaOculoplasticsRegulationRetinaSafetyautonomyclinical outcomeequityexplainabilityhealth disparitiesimagingnon-maleficencepatient benefitpopulation achieved sensitivitypopulation healthscalabilitytransparencyvalidabilityvalidationvernacular medicine

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Area of Science:

  • Bioethics
  • Medical Artificial Intelligence
  • Ophthalmic Imaging

Background:

  • The integration of artificial intelligence (AI) and machine diagnostic systems (software as a medical device) into clinical practice necessitates a strong bioethical framework.
  • Stakeholder engagement is vital for global regulatory consideration of AI in medicine.

Purpose of the Study:

  • To initiate a dialogue on establishing a bioethically sound foundation for AI in medicine, specifically for ophthalmic imaging.
  • To foster discussion among all relevant stakeholders regarding the ethical considerations of AI in healthcare.

Main Methods:

  • Reviewing the scope of issues and summaries of discussions from the Foundational Principles of Ophthalmic Imaging and Algorithmic Interpretation Working Group.
  • Presenting findings from the Collaborative Community on Ophthalmic Imaging inaugural meeting and subsequent working group sessions.

Main Results:

  • AI holds potential to enhance healthcare access, improve patient outcomes, reduce disparities, and lower costs.
  • Significant ethical concerns regarding nonmaleficence, autonomy, and equity must be addressed through collaborative efforts.
  • Meticulous attention to the design, validation, and implementation of AI in medicine is essential.

Conclusions:

  • A bioethically sound foundation for AI in medicine can be built upon the principles of nonmaleficence, autonomy, and equity.
  • Establishing this foundation is key for the successful integration of AI into medical practice and regulatory approval.
  • This approach can lead to significant improvements in healthcare accessibility, quality, and cost-effectiveness.