Literature DB >> 29927518

Patient acceptance and trust in automated computer-assisted diagnosis of melanoma with dermatofluoroscopy.

Christine Fink1, Lorenz Uhlmann2, Maja Hofmann3, Andrea Forschner4, Thomas Eigentler4, Claus Garbe4, Alexander Enk1, Holger A Haenssle1.   

Abstract

BACKGROUND AND OBJECTIVES: Automated computer-guided diagnostic procedures are increasingly being integrated into patient care. However, in contrast to the increasing application of automation, patient acceptance and trust in such technologies has rarely been studied. Automated diagnosis of melanoma with dermatofluoroscopy was recently approved by regulatory agencies. The objective of this study is to assess patient acceptance and trust in automated melanoma diagnosis with dermatofluoroscopy. PATIENTS AND METHODS: We examined 140 pigmented skin lesions with dermatofluoroscopy as part of a prospective clinical study. Four weeks after their examination with dermatofluoroscopy, we contacted 100 patients with a 10-item questionnaire addressing their acceptance and trust in this technology on a five-point visual analogue scale.
RESULTS: A "high" to "very high" level of patient acceptance and trust in dermatofluoroscopy was found in 74 % of responders. Most patients agreed that computer-assisted diagnoses are trustworthy and may generally improve the diagnostic performance of physicians. However, all responders insisted on the interpretation of computer-assisted diagnoses by a physician and frequently rejected the idea of computers completely replacing physicians.
CONCLUSION: Patient acceptance and trust in dermatofluoroscopy was high. Patients clearly supported the use of automated, computer-assisted diagnostics as an adjunct to the physicians' examination.
© 2018 Deutsche Dermatologische Gesellschaft (DDG). Published by John Wiley & Sons Ltd.

Entities:  

Mesh:

Year:  2018        PMID: 29927518     DOI: 10.1111/ddg.13562

Source DB:  PubMed          Journal:  J Dtsch Dermatol Ges        ISSN: 1610-0379            Impact factor:   5.584


  6 in total

1.  Patient Perceptions of New Robotic Technologies in Clinical Restorative Dentistry.

Authors:  Mattie N Milner; Emily C Anania; Karla Candelaria-Oquendo; Stephen Rice; Scott R Winter; Nadine K Ragbir
Journal:  J Med Syst       Date:  2019-12-17       Impact factor: 4.460

Review 2.  [New optical examination procedures for the diagnosis of skin diseases].

Authors:  K Sies; J K Winkler; M Zieger; M Kaatz; H A Haenssle
Journal:  Hautarzt       Date:  2020-02       Impact factor: 0.751

3.  Diagnostic Performance of a Support Vector Machine for Dermatofluoroscopic Melanoma Recognition: The Results of the Retrospective Clinical Study on 214 Pigmented Skin Lesions.

Authors:  Łukasz Szyc; Uwe Hillen; Constantin Scharlach; Friederike Kauer; Claus Garbe
Journal:  Diagnostics (Basel)       Date:  2019-08-25

Review 4.  Artificial intelligence in medicine and dermatology.

Authors:  Anna Woźniacka; Sebastian Patrzyk; Maksym Mikołajczyk
Journal:  Postepy Dermatol Alergol       Date:  2022-01-07       Impact factor: 1.837

5.  Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

Authors:  Titus J Brinker; Roman C Maron; Jochen S 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
Journal:  J Med Internet Res       Date:  2020-09-11       Impact factor: 5.428

Review 6.  From Melanocytes to Melanoma Cells: Characterization of the Malignant Transformation by Four Distinctly Different Melanin Fluorescence Spectra (Review).

Authors:  Dieter Leupold; Lutz Pfeifer; Maja Hofmann; Andrea Forschner; Gerd Wessler; Holger Haenssle
Journal:  Int J Mol Sci       Date:  2021-05-17       Impact factor: 5.923

  6 in total

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