Analyzing the Predictability of an Artificial Intelligence App (Tibot) in the Diagnosis of Dermatological Conditions: A Cross-sectional Study

Abstract

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.

Efficacy of an Artificial Intelligence App (Aysa) in Dermatological Diagnosis: Cross-Sectional Analysis

“…Marri et al [ 2 ] assessed the Tibot AI app in diagnosing skin conditions in 600 patients. For the predicted top-3 diagnoses given by the app, the mean prediction accuracy was 96.1% (95% CI 94.3%-97.5%) and for the exact diagnosis it was 80.6% (95% CI 77.2%-83.7%).…”

“…Table 4 provides a comparison of the sensitivities, specificity, and PPV of AI algorithms of this study and the studies by Marri et al [ 2 ] and Mathur et al [ 19 ] in diagnosing various skin disorders. The sensitivity in the majority of the conditions was comparable in all the studies except for lichen planus and malignant tumors.…”

“…Traditionally, dermatological diagnoses are established by integrating the patient’s medical history, clinical examination, and, in some instances, dermoscopic and histopathologic analyses [ 1 ]. As it is predominantly a morphological feature–dependent specialty, dermatology is a field best suited for incorporating artificial intelligence (AI) image detection and recognition capabilities for aided diagnosis [ 2 - 5 ].…”

“…Some of its merits include automating redundant assignments, performing constrained tasks, addressing spectator dependability issues, and ability to think outside the box. Conversely, there are unresolved legal, ethical, privacy, and liability issues associated with AI, and the inability to understand the decision-making process (ie, the “blackbox” nature) may limit its acceptability [ 2 ].…”

Artificial Intelligence in Diagnostic Dermatology: Challenges and the Way Forward

“…However, most models only served an academic purpose; with few developing into real-life applications. [ 6 7 8 9 ] At the time of writing this review, as per TRACXN data, there are around 44 active startups around the world that utilize AI for skin disorder diagnosis and incorporate that technology into their business model. [ 10 ] However, most commercially successful applications do not have a corresponding publication, indicating a disparity between academia and the industry.…”