Published on in Vol 9, No 12 (2021): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33049, first published .
Differential Biases and Variabilities of Deep Learning–Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

Differential Biases and Variabilities of Deep Learning–Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

Differential Biases and Variabilities of Deep Learning–Based Artificial Intelligence and Human Experts in Clinical Diagnosis: Retrospective Cohort and Survey Study

Journals

  1. Jiang H, Diao Z, Shi T, Zhou Y, Wang F, Hu W, Zhu X, Luo S, Tong G, Yao Y. A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation. Computers in Biology and Medicine 2023;157:106726 View
  2. Habib A, Xu Y, Bock K, Mohanty S, Sederholm T, Weeks W, Dodhia R, Ferres J, Perry C, Sacks R, Singh N. Evaluating the generalizability of deep learning image classification algorithms to detect middle ear disease using otoscopy. Scientific Reports 2023;13(1) View
  3. Wu Q, Wang X, Liang G, Luo X, Zhou M, Deng H, Zhang Y, Huang X, Yang Q. Advances in Image‐Based Artificial Intelligence in Otorhinolaryngology–Head and Neck Surgery: A Systematic Review. Otolaryngology–Head and Neck Surgery 2023;169(5):1132 View
  4. Park H, Kim S, Choi J, Cha D. Human–machine cooperation meta-model for clinical diagnosis by adaptation to human expert’s diagnostic characteristics. Scientific Reports 2023;13(1) View
  5. Zhao G, Cheng W, Cai W, Zhang X, Liu J. Leveraging Interpretable Feature Representations for Advanced Differential Diagnosis in Computational Medicine. Bioengineering 2023;11(1):29 View
  6. Shah M, Sureja N. A Comprehensive Review of Bias in Deep Learning Models: Methods, Impacts, and Future Directions. Archives of Computational Methods in Engineering 2024 View
  7. Malik S, Tenorio B, Moond V, Dahiya D, Vora R, Dbouk N. Systematic review of machine learning models in predicting the risk of bleed/grade of esophageal varices in patients with liver cirrhosis: A comprehensive methodological analysis. Journal of Gastroenterology and Hepatology 2024 View