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

Decision Support for Health Professionals (1492) Electronic/Mobile Data Capture, Internet-based Survey & Research Methodology (979) Crowdsourcing and Mechanical Turks (103) Imaging Informatics (288)

Published on in Vol 11 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/38412, first published .
Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Agreement Between Experts and an Untrained Crowd for Identifying Dermoscopic Features Using a Gamified App: Reader Feasibility Study

Authors of this article:

Jonathan Kentley1, 2 Author Orcid Image ;   Jochen Weber2 Author Orcid Image ;   Konstantinos Liopyris3 Author Orcid Image ;   Ralph P Braun4 Author Orcid Image ;   Ashfaq A Marghoob2 Author Orcid Image ;   Elizabeth A Quigley2 Author Orcid Image ;   Kelly Nelson5 Author Orcid Image ;   Kira Prentice6 Author Orcid Image ;   Erik Duhaime6 Author Orcid Image ;   Allan C Halpern2 Author Orcid Image ;   Veronica Rotemberg2 Author Orcid Image
Article Authors Cited by (9) Tweetations (6) Metrics

Journals

  1. McNeil A, Parks K, Liu X, Jiang B, Coco J, McCool K, Fabbri D, Duhaime E, Dawant B, Tkaczyk E. Crowdsourcing Skin Demarcations of Chronic Graft-Versus-Host Disease in Patient Photographs: Training Versus Performance Study. JMIR Dermatology 2023;6:e48589 View
  2. Friche P, Moulis L, Du Thanh A, Dereure O, Duflos C, Carbonnel F. Training Family Medicine Residents in Dermoscopy Using an e-Learning Course: Pilot Interventional Study. JMIR Formative Research 2024;8:e56005 View
  3. Duggan N, Jin M, Duran Mendicuti M, Hallisey S, Bernier D, Selame L, Asgari-Targhi A, Fischetti C, Lucassen R, Samir A, Duhaime E, Kapur T, Goldsmith A. Gamified Crowdsourcing as a Novel Approach to Lung Ultrasound Data Set Labeling: Prospective Analysis. Journal of Medical Internet Research 2024;26:e51397 View
  4. Skinner G, Chen T, Jentis G, Liu Y, McCulloh C, Harzman A, Huang E, Kalady M, Kim P. Real-time near infrared artificial intelligence using scalable non-expert crowdsourcing in colorectal surgery. npj Digital Medicine 2024;7(1) View
  5. Hasan E, Duhaime E, Trueblood J. Boosting wisdom of the crowd for medical image annotation using training performance and task features. Cognitive Research: Principles and Implications 2024;9(1) View
  6. Kong W, Nascimento F, Struck A, Duhaime E, Kapur S, Amorim E, Kapinos G, Rodriguez A, Thomas B, Desai M, Lee J, Westover M, Jing J. Evaluating crowdsourcing for ICU EEG annotation: A comparison with expert performance. Epilepsia 2025 View

Books/Policy Documents

  1. Xu Z, Yan J, Lu D, Wang Y, Luo J, Zheng Y, Tong R. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. View
  2. Rädsch T, Reinke A, Weru V, Tizabi M, Heller N, Isensee F, Kopp-Schneider A, Maier-Hein L. Computer Vision – ECCV 2024. View

Conference Proceedings

  1. Scaramussa L, Pacheco A. Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS 2025). Anotação de imagens médicas assistida por IA: um estudo sobre segmentação de lesões de pele por não especialistas View