Commentary: Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Abràmoff, Michael D.; Mortensen, Zachary; Tava, Chris

doi:10.3389/fmed.2021.765936

Cited by 2 publications

(1 citation statement)

References 9 publications

(9 reference statements)

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“…These ethical and other concerns with AI in healthcare have been shown by a number of research studies 27 – 29 . Abramoff et al proposed ethical frameworks for AI 27 – 31 to help proactively address the issue of undesirable algorithmic bias as well as other concerns with AI. More recently, the Foundational Considerations on Algorithmic Interpretation (FPOAI) workgroup of the Collaborative Community on Ophthalmic Imaging published their “Foundational Considerations” 32 on AI as a start to developing metrics for ethics , including metrics for “Equity” 33 , in order to be able to evaluate how specific AI systems adhere to various bioethical principles.…”

Section: Introductionmentioning

confidence: 99%

Considerations for addressing bias in artificial intelligence for health equity

Abràmoff,

Tarver,

Loyo-Berrios

et al. 2023

npj Digit. Med.

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Health equity is a primary goal of healthcare stakeholders: patients and their advocacy groups, clinicians, other providers and their professional societies, bioethicists, payors and value based care organizations, regulatory agencies, legislators, and creators of artificial intelligence/machine learning (AI/ML)-enabled medical devices. Lack of equitable access to diagnosis and treatment may be improved through new digital health technologies, especially AI/ML, but these may also exacerbate disparities, depending on how bias is addressed. We propose an expanded Total Product Lifecycle (TPLC) framework for healthcare AI/ML, describing the sources and impacts of undesirable bias in AI/ML systems in each phase, how these can be analyzed using appropriate metrics, and how they can be potentially mitigated. The goal of these “Considerations” is to educate stakeholders on how potential AI/ML bias may impact healthcare outcomes and how to identify and mitigate inequities; to initiate a discussion between stakeholders on these issues, in order to ensure health equity along the expanded AI/ML TPLC framework, and ultimately, better health outcomes for all.

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Section: Introductionmentioning

confidence: 99%

Considerations for addressing bias in artificial intelligence for health equity

Abràmoff,

Tarver,

Loyo-Berrios

et al. 2023

npj Digit. Med.

View full text Add to dashboard Cite

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Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions

Alsadoun,

Ali,

Mushtaq

et al. 2024

Cureus

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Diabetic retinopathy (DR) remains a leading cause of vision loss worldwide, with early detection critical for preventing irreversible damage. This review explores the current landscape and future directions of artificial intelligence (AI)-enhanced detection of DR from fundus images. Recent advances in deep learning and computer vision have enabled AI systems to analyze retinal images with expert-level accuracy, potentially transforming DR screening. Key developments include convolutional neural networks achieving high sensitivity and specificity in detecting referable DR, multi-task learning approaches that can simultaneously detect and grade DR severity, and lightweight models enabling deployment on mobile devices. While these AI systems show promise in improving the efficiency and accessibility of DR screening, several challenges remain. These include ensuring generalizability across diverse populations, standardizing image acquisition and quality, addressing the "black box" nature of complex models, and integrating AI seamlessly into clinical workflows. Future directions in the field encompass explainable AI to enhance transparency, federated learning to leverage decentralized datasets, and the integration of AI with electronic health records and other diagnostic modalities. There is also growing potential for AI to contribute to personalized treatment planning and predictive analytics for disease progression. As the technology continues to evolve, maintaining a focus on rigorous clinical validation, ethical considerations, and real-world implementation will be crucial for realizing the full potential of AI-enhanced DR detection in improving global eye health outcomes.

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Commentary: Diagnosing Diabetic Retinopathy With Artificial Intelligence: What Information Should Be Included to Ensure Ethical Informed Consent?

Cited by 2 publications

References 9 publications

Considerations for addressing bias in artificial intelligence for health equity

Considerations for addressing bias in artificial intelligence for health equity

Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Images: The Current Landscape and Future Directions

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