User satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator. (October 2020)

• Record Type: Journal Article
• Authors: Po (Harvey) Chin, Yen; Hsin Huang, I; Yu Hou, Ze; Yu Chen, Po; Bassir, Fatima; Han Wang, Hsiao; Ting Lin, Yu; Chuan (Jack) Li, Yu
• Is Part Of: Computer methods and programs in biomedicine. Volume 195(2020)
• Publication Date: 2020-10
• Subjects: Deep learning -- Melanoma -- Teledermatology -- User satisfaction -- Pigmented cutaneous lesion -- Artificial intelligence; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105649
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: We present the first study that focuses on user satisfaction with a smartphone-compatible, artificial intelligence-based cutaneous pigmented lesion evaluator. Over 90% of the participants were satisfied and over 75% of the participants were strongly satisfied with this program, in terms of usability, interaction, and impact on daily life. User satisfaction did not show a significant difference between genders, age groups, and risk predictions received. Abstract: 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.