Artificial Intelligence applications have shown promise in the management of pandemics and have been widely used to assist the identification, classification, and diagnosis of medical images. In response to the global outbreak of Monkeypox (Mpox), the HeHealth.ai team leveraged an existing tool to screen for sexually transmitted diseases to develop a digital screening test for symptomatic Mpox through AI approaches. Prior to the global outbreak of Mpox, the team developed a smartphone app, where app users can use their own smartphone cameras to take pictures of their own penises to screen for symptomatic STD. The AI model was initially developed using 5000 cases and use a modified convolutional neural network to output prediction scores across visually diagnosable penis pathologies including Syphilis, Herpes Simplex Virus, and Human Papilloma Virus. From June 2022 to October 2022, a total of about 22,000 users downloaded the HeHealth app, and about 21,000 images have been analyzed using HeHealth AI technology. We then engaged in formative research, stakeholder engagement, rapid consolidation images, a validation study, and implementation of the tool from July 2022. From July 2022 to October 2022, a total of 1000 Mpox related images had been used to train the Mpox symptom checker tool. Our digital symptom checker tool showed accuracy of 87% to rule in Mpox and 90% to rule out symptomatic Mpox. Several hurdles identified included issues of data privacy and security for app users, initial lack of data to train the AI tool, and the potential generalizability of input data. We offer several suggestions to help others get started on similar projects in emergency situations, including engaging a wide range of stakeholders, having a multidisciplinary team, prioritizing pragmatism, as well as the concept that big data in fact is made up of small data.

翻译：人工智能应用在大流行管理中展现出潜力，并被广泛用于辅助医学图像的识别、分类与诊断。为应对猴痘全球疫情，HeHealth.ai团队基于现有性传播疾病筛查工具，通过人工智能方法开发了针对症状性猴痘的数字化筛查测试。在猴痘全球疫情暴发前，该团队已开发了一款智能手机应用，用户可自行用手机摄像头拍摄阴茎照片以筛查症状性性传播疾病。该AI模型最初使用5000个案例进行训练，采用改进的卷积神经网络，针对包括梅毒、单纯疱疹病毒和人乳头瘤病毒在内的可视觉诊断的阴茎病理状况输出预测评分。2022年6月至2022年10月期间，共计约22000名用户下载了HeHealth应用，约21000张图像通过HeHealth AI技术完成分析。随后我们于2022年7月开展了形成性研究、利益相关方参与、图像快速整合、验证研究及工具部署。2022年7月至2022年10月期间，总计使用1000张猴痘相关图像训练了猴痘症状筛查工具。该数字化症状筛查工具对确诊猴痘的准确率为87%，对排除症状性猴痘的准确率为90%。识别出的若干障碍包括：应用用户数据隐私与安全问题、初期缺乏训练AI工具的数据、以及输入数据的潜在泛化性。我们提出多项建议以帮助其他团队在紧急情况下开展类似项目，包括广泛吸纳利益相关方、组建多学科团队、优先注重实用性，以及"大数据实由小数据构成"的理念。