皮肤科是人工智能 (AI) 驱动的图像识别的理想专业，可提高诊断准确性和患者护理。世界许多地区缺乏皮肤科医生，以及皮肤疾病和恶性肿瘤的高发，凸显了对人工智能辅助诊断的需求不断增加。尽管基于人工智能的皮肤病识别应用程序已广泛使用，但缺乏评估其可靠性和准确性的研究。本研究的目的是分析 Aysa AI 应用程序作为各种皮肤病初步诊断工具的功效。这项观察性横断面研究纳入了到皮肤科诊所就诊的 2 岁以上患者。在获得知情同意后，患有各种皮肤病的个体的病变图像被上传到应用程序。该应用程序用于制作患者档案、识别病变形态、在人体模型上绘制位置，并回答有关持续时间和症状的问题。该应用程序提供了八种鉴别诊断，并与临床诊断进行比较。使用敏感性、特异性、准确性、阳性​​预测值、阴性预测值和 F1 分数来评估模型的性能。使用 χ2 检验进行分类变量的比较，并在 P<.05 时考虑统计显着性。共有 700 名患者参与了该研究。各种各样的皮肤状况被分为 12 类。 AI 模型的平均 top-1 敏感性为 71% (95% CI 61.5%-74.3%)，top-3 敏感性为 86.1% (95% CI 83.4%-88.6%)，all-8 敏感性为 95.1% （95% CI 93.3%-96.6%）。诊断皮肤感染、角化障碍、其他炎症和细菌感染的前 1 敏感度分别为 85.7%、85.7%、82.7% 和 81.8%。对于光照性皮肤病和恶性肿瘤，top-1 敏感性分别为 33.3% 和 10%。每个类别的临床诊断和可能诊断之间都具有很强的相关性 (P<.001)。Aysa 应用程序在识别大多数皮肤病方面显示出有希望的结果。©Shiva Shankar Marri、Warood Albadri、Mohammed Salman Hyder、Ajit B Janagond、Arun C伊纳玛达尔。最初发表于 JMIR Dermatology (http://derma.jmir.org)，2024 年 7 月 2 日。

Dermatology is an ideal specialty for artificial intelligence (AI)-driven image recognition to improve diagnostic accuracy and patient care. Lack of dermatologists in many parts of the world and the high frequency of cutaneous disorders and malignancies highlight the increasing need for AI-aided diagnosis. Although AI-based applications for the identification of dermatological conditions are widely available, research assessing their reliability and accuracy is lacking.The aim of this study was to analyze the efficacy of the Aysa AI app as a preliminary diagnostic tool for various dermatological conditions in a semiurban town in India.This observational cross-sectional study included patients over the age of 2 years who visited the dermatology clinic. Images of lesions from individuals with various skin disorders were uploaded to the app after obtaining informed consent. The app was used to make a patient profile, identify lesion morphology, plot the location on a human model, and answer questions regarding duration and symptoms. The app presented eight differential diagnoses, which were compared with the clinical diagnosis. The model's performance was evaluated using sensitivity, specificity, accuracy, positive predictive value, negative predictive value, and F1-score. Comparison of categorical variables was performed with the χ2 test and statistical significance was considered at P<.05.A total of 700 patients were part of the study. A wide variety of skin conditions were grouped into 12 categories. The AI model had a mean top-1 sensitivity of 71% (95% CI 61.5%-74.3%), top-3 sensitivity of 86.1% (95% CI 83.4%-88.6%), and all-8 sensitivity of 95.1% (95% CI 93.3%-96.6%). The top-1 sensitivities for diagnosis of skin infestations, disorders of keratinization, other inflammatory conditions, and bacterial infections were 85.7%, 85.7%, 82.7%, and 81.8%, respectively. In the case of photodermatoses and malignant tumors, the top-1 sensitivities were 33.3% and 10%, respectively. Each category had a strong correlation between the clinical diagnosis and the probable diagnoses (P<.001).The Aysa app showed promising results in identifying most dermatoses.©Shiva Shankar Marri, Warood Albadri, Mohammed Salman Hyder, Ajit B Janagond, Arun C Inamadar. Originally published in JMIR Dermatology (http://derma.jmir.org), 02.07.2024.