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Related Experiment Video

Updated: Sep 15, 2025

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AI Workflow, External Validation, and Development in Eye Disease Diagnosis.

Qingyu Chen1,2, Tiarnan D L Keenan3, Elvira Agron3

  • 1National Library of Medicine, National Institutes of Health, Bethesda, Maryland.

JAMA Network Open
|July 16, 2025
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly improved accuracy and efficiency in diagnosing age-related macular degeneration (AMD). Further development is crucial for AI generalizability and clinical adoption, emphasizing downstream accountability.

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Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence
  • Diagnostic Imaging

Background:

  • Timely disease diagnosis is hampered by limited clinical resources and increasing patient loads.
  • Artificial intelligence (AI) demonstrates expert-level diagnostic accuracy but faces adoption barriers due to a lack of downstream accountability, including workflow integration and external validation.

Purpose of the Study:

  • To address the challenges in downstream accountability for medical AI.
  • To evaluate an AI-assisted workflow for diagnosing and classifying age-related macular degeneration (AMD).

Main Methods:

  • A diagnostic study involving 24 clinicians from 12 institutions assessed AI-assisted AMD diagnosis and classification.
  • Four randomized rounds compared manual diagnosis with AI-assisted diagnosis using 960 images.
  • The DeepSeeNet AI model was enhanced to DeepSeeNet+ and validated on 3 datasets, including an external cohort from Singapore.

Main Results:

  • AI assistance significantly improved diagnostic accuracy for 23 of 24 clinicians, increasing the mean F1 score.
  • AI-assisted diagnosis reduced diagnostic time per patient by an average of 6.9 to 8.6 seconds.
  • The enhanced DeepSeeNet+ model achieved a higher F1 score on external datasets compared to the original model, with the Singapore cohort showing a mean F1 score of 38.95.

Conclusions:

  • AI assistance enhances accuracy and efficiency in AMD diagnosis, supporting clinical workflows.
  • Further AI development is necessary to improve generalizability across diverse populations.
  • This study underscores the importance of downstream accountability in the early clinical evaluation of medical AI tools.