> Just listen to your patient, he is telling you the diagnosis.Sir William Osler (1849–1919),Canadian physician and professor of medicineWe picture an 18-year-old young man from a village somewhere in Europe. His name is Omer. Omer has been suffering from disease episodes since his early childhood: bursts of fever, fatigue and abdominal pain. He visited many doctors, but none of them was able to establish a satisfactory treatment. He went through a diagnostic and therapeutic odyssey typical for rare disease patients. Fatigue has led him into social isolation. He is perceived as a malingerer.1 He begins to Google. Due to the unspecific nature of his approach, the search does not bring up specific results, though. Helpful medical knowledge is not accessible to him this way. Consequently, he looks for help through social media. He is targeted for an advertisement of a symptom checker app because of his previous searches and app downloads in his Ad-ID. He reads ‘start a symptom assessment!’ and decides to instal the app. Omer starts the symptom assessment in his mother tongue. The chatbot’s underlying ontology is designed to translate professional medical concepts into common language. It initially asks about his basic demographics, ethnicity, pre-existing conditions, current symptoms and their specific attributes. The dialogue reveals that Omer has lately also shown a red, hot, swollen, sharply bordered painful eruption in the calf. Based on its internal probabilistic disease models, the chatbot’s reasoning algorithms compile a first list of possible underlying diseases, among them several autoimmune diseases and periodic fever syndromes. The chatbot realises the complex situation and the need for a deeper anamnesis. It continues assessing the course and quality of Omer’s symptoms. It asks about the onset, intensity and duration of his fevers thereby identifying the presence of repeating disease episodes. In reaction, the chatbot encourages Omer to track his symptoms over time to increase the value of timing-related information. By asking detailed …

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罕见病2030：未来增强型人工智能将如何支持罕见病的诊断和治疗

> 听听你的病人，他会告诉你诊断结果。威廉·奥斯勒爵士（Sir William Osler，1849-1919 年），加拿大内科医生和医学教授我们描绘了一位来自欧洲某个村庄的 18 岁年轻人。他的名字是奥马尔。奥默从孩提时代起就一直患有疾病：发烧、疲劳和腹痛。他拜访了许多医生，但没有一个能够建立令人满意的治疗方法。他经历了罕见病患者典型的诊断和治疗奥德赛。疲劳使他陷入社会孤立。他被视为装病者。1 他开始使用 Google。但是，由于他的方法的不具体性质，搜索不会带来具体的结果。他无法通过这种方式获得有用的医学知识。因此，他通过社交媒体寻求帮助。由于之前在其 Ad-ID 中进行过搜索和应用下载，他成为了症状检查应用程序广告的目标。他写着“开始症状评估！” 并决定安装该应用程序。Omer 开始用他的母语进行症状评估。聊天机器人的底层本体旨在将专业医学概念翻译成通用语言。它最初询问他的基本人口统计、种族、既往状况、当前症状及其特定属性。对话显示，奥默最近还表现出小腿发红、发热、肿胀、边缘尖锐的疼痛爆发。基于其内部概率疾病模型，聊天机器人的推理算法编制了可能的潜在疾病的第一个列表，其中包括几种自身免疫性疾病和周期性发热综合征。聊天机器人意识到复杂的情况和更深层次的回忆的需要。它继续评估 Omer 症状的过程和质量。它询问他发烧的发作、强度和持续时间，从而确定是否存在重复的疾病发作。作为回应，聊天机器人鼓励 Omer 随着时间的推移跟踪他的症状，以增加与时间相关的信息的价值。通过询问详细...