This paper is in the following e-collection/theme issue:

Tutorial (62) Artificial Intelligence (1216) Machine Learning (1401) Advanced Data Analytics in eHealth (112) Decision Support for Health Professionals (1147) Clinical Information and Decision Making (1340)

Published on in Vol 25 (2023)

This is a member publication of University of Cambridge (Jisc)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/49949, first published .
Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Democratizing Artificial Intelligence Imaging Analysis With Automated Machine Learning: Tutorial

Authors of this article:

Arun James Thirunavukarasu1, 2 Author Orcid Image ;   Kabilan Elangovan2 Author Orcid Image ;   Laura Gutierrez2 Author Orcid Image ;   Yong Li2 Author Orcid Image ;   Iris Tan2 Author Orcid Image ;   Pearse A Keane3 Author Orcid Image ;   Edward Korot4, 5 Author Orcid Image ;   Daniel Shu Wei Ting2, 4, 6 Author Orcid Image
Article Authors Cited by (10) Tweetations (5) Metrics

Journals

  1. Ng F, Thirunavukarasu A, Cheng H, Tan T, Gutierrez L, Lan Y, Ong J, Chong Y, Ngiam K, Ho D, Wong T, Kwek K, Doshi-Velez F, Lucey C, Coffman T, Ting D. Artificial intelligence education: An evidence-based medicine approach for consumers, translators, and developers. Cell Reports Medicine 2023;4(10):101230 View
  2. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3):187 View
  3. Litvin A, Stoma I, Sharshakova T, Rumovskaya S, Kyovalev A. New possibilities of artificial intelligence in medicine: a narrative review. Health and Ecology Issues 2024;21(1):7 View
  4. Thirunavukarasu A, Elangovan K, Gutierrez L, Hassan R, Li Y, Tan T, Cheng H, Teo Z, Lim G, Ting D. Clinical performance of automated machine learning: A systematic review. Annals of the Academy of Medicine, Singapore 2024;53(3 - Correct DOI):187 View
  5. Shin D, Choi H, Kim D, Park J, Yoo T, Koh K. Code-Free Machine Learning Approach for EVO-ICL Vault Prediction: A Retrospective Two-Center Study. Translational Vision Science & Technology 2024;13(4):4 View
  6. Florentino B, Parmezan Bonidia R, Sanches N, da Rocha U, de Carvalho A. BioPrediction-RPI: Democratizing the prediction of interaction between non-coding RNA and protein with end-to-end machine learning. Computational and Structural Biotechnology Journal 2024;23:2267 View
  7. Wong C, Liu T, Wong T, Tong J, Lau H, Keane P. Development and validation of an automated machine learning model for the multi-class classification of diabetic retinopathy, central retinal vein occlusion and branch retinal vein occlusion based on color fundus photographs. JFO Open Ophthalmology 2024;7:100117 View
  8. Sabrigiriraj M, Manoharan K. Teaching Machine Learning and Deep Learning Introduction: An Innovative Tutorial-Based Practical Approach. WSEAS TRANSACTIONS ON ADVANCES in ENGINEERING EDUCATION 2024;21:54 View
  9. Adigun G, Ajani Y, Enakrire R. The Intelligent Libraries: Innovation for a Sustainable Knowledge System in the Fifth (5th) Industrial Revolution. Libri 2024;0(0) View