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Using artificial intelligence to improve human performance: efficient retinal disease detection training with

Hitoshi Tabuchi1,2, Justin Engelmann3, Fumiatsu Maeda4

  • 1Department of Technology and Design Thinking for Medicine, Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Hiroshima, Japan htabuchi@hiroshima-u.ac.jp.

The British Journal of Ophthalmology
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Summary

Generative AI created synthetic retinal images for training, significantly improving diagnostic accuracy in medical students. This AI-aided teaching method highlights the irreplaceable role of human judgment in diagnostics.

Keywords:
Diagnostic tests/InvestigationImagingPublic healthRetinaTelemedicine

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Area of Science:

  • Ophthalmology
  • Medical Education
  • Artificial Intelligence

Background:

  • Artificial intelligence (AI) offers significant potential in medical imaging diagnostics, yet human judgment remains crucial.
  • A novel AI-aided teaching method is proposed, utilizing generative AI to train students on diverse medical images while ensuring patient privacy.

Purpose of the Study:

  • To evaluate the efficacy of a web-based course using synthetic ultra-widefield (UWF) retinal images for training medical students in disease detection.
  • To assess the impact of AI-generated synthetic images on diagnostic accuracy compared to traditional methods and AI models.

Main Methods:

  • A web-based course was developed featuring 600 synthetic UWF retinal images generated by fine-tuned stable diffusion models.
  • The training dataset comprised 6285 real UWF images across six categories, including five retinal diseases and normal cases.
  • 161 trainee orthoptists completed the course and were assessed using pre- and post-training tests with both UWF and standard field (SF) real patient images.

Main Results:

  • Students demonstrated a significant improvement in diagnostic accuracy after completing the course, with UWF image accuracy rising from 43.6% to 74.1% (p<0.0001).
  • Diagnostic accuracy for SF images also increased substantially, from 42.7% to 68.7% (p<0.0001), surpassing a state-of-the-art AI model's performance.
  • The AI-aided training nearly matched the accuracy of a leading AI model for UWF images and exceeded it for SF images.

Conclusions:

  • Synthetic medical images generated by AI are effective tools for medical education and training.
  • Human diagnostic performance proved more robust in novel situations than AI, underscoring the continued essential role of human judgment in medical diagnosis.