International Journal of Ophthalmology (Sep 2023)

Guidelines on clinical research evaluation of artificial intelligence in ophthalmology (2023)

  • Wei-Hua Yang,
  • Yi Shao,
  • Yan-Wu Xu,
  • Expert Workgroup of Guidelines on Clinical Research Evaluation of Artificial Intelligence in Ophthalmology (2023),
  • Ophthalmic Imaging and Intelligent Medicine Branch of Chinese Medicine Education Association,
  • Intelligent Medicine Committee of Chinese Medicine Education Association

DOI
https://doi.org/10.18240/ijo.2023.09.02
Journal volume & issue
Vol. 16, no. 9
pp. 1361 – 1372

Abstract

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With the upsurge of artificial intelligence (AI) technology in the medical field, its application in ophthalmology has become a cutting-edge research field. Notably, machine learning techniques have shown remarkable achievements in diagnosing, intervening, and predicting ophthalmic diseases. To meet the requirements of clinical research and fit the actual progress of clinical diagnosis and treatment of ophthalmic AI, the Ophthalmic Imaging and Intelligent Medicine Branch and the Intelligent Medicine Committee of Chinese Medicine Education Association organized experts to integrate recent evaluation reports of clinical AI research at home and abroad and formed a guideline on clinical research evaluation of AI in ophthalmology after several rounds of discussion and modification. The main content includes the background and method of developing this guideline, an introduction to international guidelines on the clinical research evaluation of AI, and the evaluation methods of clinical ophthalmic AI models. This guideline introduces general evaluation methods of clinical ophthalmic AI research, evaluation methods of clinical ophthalmic AI models, and commonly-used indices and formulae for clinical ophthalmic AI model evaluation in detail, and amply elaborates the evaluation methods of clinical ophthalmic AI trials. This guideline aims to provide guidance and norms for clinical researchers of ophthalmic AI, promote the development of regularization and standardization, and further improve the overall level of clinical ophthalmic AI research evaluations.

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