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

Machine Learning (1501) Secondary Use of Clinical Data for Research and Surveillance (375) Clinical Information and Decision Making (1434) Artificial Intelligence (1421) Decision Support for Health Professionals (1210) Innovations and Technology for Cancer Prevention and Screening (150)

Published on in Vol 10, No 3 (2022): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33006, first published .
Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

Web-Based Skin Cancer Assessment and Classification Using Machine Learning and Mobile Computerized Adaptive Testing in a Rasch Model: Development Study

Authors of this article:

Ting-Ya Yang1 Author Orcid Image ;   Tsair-Wei Chien2 Author Orcid Image ;   Feng-Jie Lai3 Author Orcid Image
Article Authors Cited by (9) Tweetations (4) Metrics

Journals

  1. Ho S, Chien T, Lin M, Tsai K. An app for predicting patient dementia classes using convolutional neural networks (CNN) and artificial neural networks (ANN): Comparison of prediction accuracy in Microsoft Excel. Medicine 2023;102(4):e32670 View
  2. Hu T, Chow J, Chien T, Chou W. Detecting dengue fever in children using online Rasch analysis to develop algorithms for parents: An APP development and usability study. Medicine 2023;102(13):e33296 View
  3. Kao H, Chien T, Wang W, Chou W, Chow J. Assessing ChatGPT’s capacity for clinical decision support in pediatrics: A comparative study with pediatricians using KIDMAP of Rasch analysis. Medicine 2023;102(25):e34068 View
  4. Chow J, Cheng T, Chien T, Chou W. Assessing ChatGPT’s Capability for Multiple Choice Questions Using RaschOnline: Observational Study. JMIR Formative Research 2024;8:e46800 View
  5. Li Z, Ji Q, Yang X, Zhou Y, Zhi S. An Identification Method of Feature Interpretation for Melanoma Using Machine Learning. Applied Sciences 2023;13(18):10076 View
  6. Hsieh J, Chow J. Development of mobile CAT for patient feedback on pediatric consultations based on Rasch analysis of online techniques. Medicine 2024;103(18):e37993 View
  7. Lyakhova U, Lyakhov P. Systematic review of approaches to detection and classification of skin cancer using artificial intelligence: Development and prospects. Computers in Biology and Medicine 2024;178:108742 View
  8. Gamil S, Zeng F, Alrifaey M, Asim M, Ahmad N. An Efficient AdaBoost Algorithm for Enhancing Skin Cancer Detection and Classification. Algorithms 2024;17(8):353 View
  9. Aboulmira A, Lachgar M, Hrimech H, Camara A, Elbahja C, Elmansouri A, Hassini Y. SkinHealthMate app: An AI-powered digital platform for skin disease diagnosis. Systems and Soft Computing 2024;6:200166 View