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

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.

Clinical Photography in Dermatology: Perception and Behavior of Dermatologists - A Pilot Study.

BACKGROUND AND AIMS: Clinical photography is a vital component of patient care in dermatology. Casual sharing of images over social media is a common practice among the clinicians. We performed this study to assess the perception and behavior of dermatologists toward clinical photography. MATERIALS AND METHODS: A questionnaire having 10 questions was prepared using an online survey platform and the link to it was shared in a dermatologists group using the WhatsApp application. Among questions, nine were multiple choice type while the tenth was open ended. Some of the questions allowed multiple responses and answering all the questions was not mandatory. Resultant data was analyzed using Chi-square test for categorical variables and unpaired t-test to compare quantitative variables. RESULTS: A total of 119 dermatologists participated in the study. Most respondents used personal smartphone (72.9%, 86/118) for imaging followed by digital camera (27.9%, 33/118). Monitoring the disease process (83.9%, 99/118) and research publications (51.6%, 61/118) were the commonest reasons for photography. Except one all participants had shared images and most commonly in professional WhatsApp groups (86.3%, 101/117) and to personal WhatsApp number (31.6%, 37/117). Patient consent for photography was duly recorded by very few (14.4%, 17/118). More than half (52.3%, 56/107) responded monitoring of the disease as the single most important application of clinical photography in dermatology. CONCLUSION: Adequate awareness regarding appropriate usage and safekeeping of gadget meant for imaging and necessity of obtaining written consent for taking as well as sharing the images and secure storage of the images was found to be lacking.