Artificial Intelligence and Its Effect on Dermatologists’ Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study
Journal of Medical Internet Research2020Vol. 22(9), pp. e18091–e18091
Citations Over TimeTop 10% of 2020 papers
Roman C. Maron, Jochen Utikal, Achim Hekler, Axel Hauschild, Elke Sattler, Wiebke Sondermann, Sebastian Haferkamp, Bastian Schilling, Markus V. Heppt, Philipp Jansen, Markus Reinholz, Cindy Franklin, Laurenz Schmitt, Daniela Hartmann, Eva Krieghoff‐Henning, Max Schmitt, Michael Weichenthal, Christof von Kalle, Stefan Fröhling, Titus J. Brinker
Abstract
The findings of our study show that AI support can improve the overall accuracy of the dermatologists in the dichotomous image-based discrimination between melanoma and nevus. This supports the argument for AI-based tools to aid clinicians in skin lesion classification and provides a rationale for studies of such classifiers in real-life settings, wherein clinicians can integrate additional information such as patient age and medical history into their decisions.
Related Papers
- → The malignant potential of small congenital nevocellular nevi(1982)231 cited
- → Diagnoses from an On-Line Expert System for Chronic Pain Confirmed by IntraOperative Findings(2016)7 cited
- → Relationship of cell-mediated cytotoxicity against melanoma cells to prognosis in melanoma patients(1978)41 cited
- → Inhibition of leukocyte migration in agarose by KC1 extracts of a human melanoma cell line grown in serum‐free medium(1975)16 cited
- → Diagnosis in Bytes: Comparing the Diagnostic Accuracy of Google and ChatGPT 3.5 as Diagnostic Support Tools(2023)