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Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study

Author

Listed:
  • Young Jae Kim
  • Jung-Im Na
  • Seung Seog Han
  • Chong Hyun Won
  • Mi Woo Lee
  • Jung-Won Shin
  • Chang-Hun Huh
  • Sung Eun Chang

Abstract

Background: Although deep neural networks have shown promising results in the diagnosis of skin cancer, a prospective evaluation in a real-world setting could confirm these results. This study aimed to evaluate whether an algorithm (http://b2019.modelderm.com) improves the accuracy of nondermatologists in diagnosing skin neoplasms. Methods: A total of 285 cases (random series) with skin neoplasms suspected of malignancy by either physicians or patients were recruited in two tertiary care centers located in South Korea. An artificial intelligence (AI) group (144 cases, mean [SD] age, 57.0 [17.7] years; 62 [43.1%] men) was diagnosed via routine examination with photographic review and assistance by the algorithm, whereas the control group (141 cases, mean [SD] age, 61.0 [15.3] years; 52 [36.9%] men) was diagnosed only via routine examination with a photographic review. The accuracy of the nondermatologists before and after the interventions was compared. Results: Among the AI group, the accuracy of the first impression (Top-1 accuracy; 58.3%) after the assistance of AI was higher than that before the assistance (46.5%, P = .008). The number of differential diagnoses of the participants increased from 1.9 ± 0.5 to 2.2 ± 0.6 after the assistance (P

Suggested Citation

  • Young Jae Kim & Jung-Im Na & Seung Seog Han & Chong Hyun Won & Mi Woo Lee & Jung-Won Shin & Chang-Hun Huh & Sung Eun Chang, 2022. "Augmenting the accuracy of trainee doctors in diagnosing skin lesions suspected of skin neoplasms in a real-world setting: A prospective controlled before-and-after study," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0260895
    DOI: 10.1371/journal.pone.0260895
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    References listed on IDEAS

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    1. Sebastian Lapuschkin & Stephan Wäldchen & Alexander Binder & Grégoire Montavon & Wojciech Samek & Klaus-Robert Müller, 2019. "Unmasking Clever Hans predictors and assessing what machines really learn," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
    2. Seung Seog Han & Ik Jun Moon & Seong Hwan Kim & Jung-Im Na & Myoung Shin Kim & Gyeong Hun Park & Ilwoo Park & Keewon Kim & Woohyung Lim & Ju Hee Lee & Sung Eun Chang, 2020. "Assessment of deep neural networks for the diagnosis of benign and malignant skin neoplasms in comparison with dermatologists: A retrospective validation study," PLOS Medicine, Public Library of Science, vol. 17(11), pages 1-21, November.
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