Background Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot is an AI app that analyzes skin conditions and works on the principle of a convolutional neural network. Appropriate research analyzing the accuracy of such apps is necessary. Objective This study aims to analyze the predictability of the Tibot AI app in the identification of dermatological diseases as compared to a dermatologist. Methods This is a cross-sectional study. After taking informed consent, photographs of lesions of patients with different skin conditions were uploaded to the app. In every condition, the AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. The ability of the AI app to predict the actual diagnosis in the top one and top three anticipated diagnoses (prediction accuracy) was used to evaluate the app’s effectiveness. Sensitivity, specificity, and positive predictive value were also used to assess the app’s performance. Chi-square test was used to contrast categorical variables. P<.05 was considered statistically significant. Results A total of 600 patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumors, and infections. In the anticipated top three diagnoses, the app’s mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%), while for the exact diagnosis, it was 80.6% (95% CI 77.2%-83.7%). The prediction accuracy (top one) for alopecia, acne, pigmentary disorders, and fungal infections was 97.7%, 91.7%, 88.5%, and 82.9%, respectively. Prediction accuracy (top three) for alopecia, eczema, and tumors was 100%. The sensitivity and specificity of the app were 97% (95% CI 95%-98%) and 98% (95% CI 98%-99%), respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all conditions (P<.001). Conclusions The AI app has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.
BACKGROUND Artificial intelligence (AI) aims to create programs that reproduce human cognition and processes involved in interpreting complex data. Dermatology relies on morphological features and is ideal for applying AI image recognition for assisted diagnosis. Tibot® is an AI application that analyses skin conditions and works on the principle of convolutional neural network. Appropriate research analyzing the accuracy of such applications is necessary. OBJECTIVE To analyze the predictability of Tibot® AI application in the identification of dermatological diseases as compared to a dermatologist. METHODS This is a cross-sectional study. After taking informed consent, photographs of lesions of patients having different skin conditions were uploaded to the application. In every condition, AI predicted three diagnoses based on probability, and these were compared with that by a dermatologist. Accuracy, sensitivity, specificity and positive predictive value were used to assess the application's performance. Chi-square test was employed to contrast categorical variables. P<.05 was considered statistically significant. RESULTS Six hundred patients were included. Clinical conditions included alopecia, acne, eczema, immunological disorders, pigmentary disorders, psoriasis, infestation, tumours and infections. In the anticipated top three diagnoses, the application’s mean prediction accuracy was 96.1%, while for the exact diagnosis, it was 80.6%. Prediction accuracy for alopecia, eczema and tumours was 100%. The sensitivity and specificity of the application was 97% and 98%, respectively. There is a statistically significant association between clinical and AI-predicted diagnoses in all the conditions (P<.001). CONCLUSIONS AI application has shown promising results in diagnosing various dermatological conditions, and there is great potential for practical applicability.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.