TY - JOUR
T1 - User satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator
AU - Po (Harvey) Chin, Yen
AU - Hsin Huang, I.
AU - Yu Hou, Ze
AU - Yu Chen, Po
AU - Bassir, Fatima
AU - Han Wang, Hsiao
AU - Ting Lin, Yu
AU - Chuan (Jack) Li, Yu
N1 - Funding Information:
This work is partially funded by the Ministry of Science and Technology, Taiwan .
Publisher Copyright:
© 2020
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10
Y1 - 2020/10
N2 - Introduction: Melanoma is the most aggressive type of skin cancer, and it may arise from a cutaneous pigmented lesion. As artificial intelligence (AI)-based teledermatology services hold promise in redefining the melanoma screening paradigm, a study that evaluates user satisfaction with a smartphone-compatible, AI-based cutaneous pigmented lesion evaluator is lacking. Methods: Data was collected between April and May 2019 in Taiwan. To assess user satisfaction with MoleMe, an AI-based cutaneous pigmented lesion evaluator on a smartphone, users were asked to complete a questionnaire designed to evaluate four aspects, including interaction, impact on daily life, usability, and overall performance, after completing a MoleMe evaluation session. For each question, users could rank their satisfaction level from 1 to 5, with five showing strongly satisfied and one showing strongly unsatisfied. The Kruskal-Wallis and Wilcoxon rank-sum tests were used to compare user satisfaction among different age groups, genders, and risk predictions received. Result: A total of 1231 questionnaires were collected for analysis. Over 90% of the participants were satisfied (score = 4 or 5) and over 75% of the participants were strongly satisfied (score 5) with MoleMe, in terms of usability, interaction, and impact on daily life. The user satisfaction did not show a significant difference between genders, age groups, and risk predictions received. (all P > 0.05) Conclusion: With high user satisfaction regardless of age group, gender, and risk prediction received, AI-based teledermatology services on a smartphone such as MoleMe may potentially achieve widespread usage and be beneficial to both patients and physicians.
AB - Introduction: Melanoma is the most aggressive type of skin cancer, and it may arise from a cutaneous pigmented lesion. As artificial intelligence (AI)-based teledermatology services hold promise in redefining the melanoma screening paradigm, a study that evaluates user satisfaction with a smartphone-compatible, AI-based cutaneous pigmented lesion evaluator is lacking. Methods: Data was collected between April and May 2019 in Taiwan. To assess user satisfaction with MoleMe, an AI-based cutaneous pigmented lesion evaluator on a smartphone, users were asked to complete a questionnaire designed to evaluate four aspects, including interaction, impact on daily life, usability, and overall performance, after completing a MoleMe evaluation session. For each question, users could rank their satisfaction level from 1 to 5, with five showing strongly satisfied and one showing strongly unsatisfied. The Kruskal-Wallis and Wilcoxon rank-sum tests were used to compare user satisfaction among different age groups, genders, and risk predictions received. Result: A total of 1231 questionnaires were collected for analysis. Over 90% of the participants were satisfied (score = 4 or 5) and over 75% of the participants were strongly satisfied (score 5) with MoleMe, in terms of usability, interaction, and impact on daily life. The user satisfaction did not show a significant difference between genders, age groups, and risk predictions received. (all P > 0.05) Conclusion: With high user satisfaction regardless of age group, gender, and risk prediction received, AI-based teledermatology services on a smartphone such as MoleMe may potentially achieve widespread usage and be beneficial to both patients and physicians.
KW - Artificial intelligence
KW - Deep learning
KW - Melanoma
KW - Pigmented cutaneous lesion
KW - Teledermatology
KW - User satisfaction
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U2 - 10.1016/j.cmpb.2020.105649
DO - 10.1016/j.cmpb.2020.105649
M3 - Article
C2 - 32750631
AN - SCOPUS:85088893110
SN - 0169-2607
VL - 195
SP - 105649
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
M1 - 105649
ER -