Patients with rare diseases are a major challenge for healthcare systems. These patients face three major obstacles: late diagnosis and misdiagnosis, lack of proper response to therapies, and absence of valid monitoring tools. We reviewed the relevant literature on first-generation artificial intelligence (AI) algorithms which were designed to improve the management of chronic diseases. The shortage of big data resources and the inability to provide patients with clinical value limit the use of these AI platforms by patients and physicians. In the present study, we reviewed the relevant literature on the obstacles encountered in the management of patients with rare diseases. Examples of currently available AI platforms are presented. The use of second-generation AI-based systems that are patient-tailored is presented. The system provides a means for early diagnosis and a method for improving the response to therapies based on clinically meaningful outcome parameters. The system may offer a patient-tailored monitoring tool that is based on parameters that are relevant to patients and caregivers and provides a clinically meaningful tool for follow-up. The system can provide an inclusive solution for patients with rare diseases and ensures adherence based on clinical responses. It has the potential advantage of not being dependent on large datasets and is a dynamic system that adapts to ongoing changes in patients’ disease and response to therapy.

罕见病患者是医疗保健系统面临的重大挑战。这些患者面临三大障碍：晚期诊断和误诊、对治疗缺乏适当反应以及缺乏有效的监测工具。我们回顾了旨在改善慢性病管理的第一代人工智能 (AI) 算法的相关文献。大数据资源的短缺和无法为患者提供临床价值限制了患者和医生对这些人工智能平台的使用。在本研究中，我们回顾了有关罕见病患者管理中遇到的障碍的相关文献。介绍了当前可用的 AI 平台的示例。介绍了为患者量身定制的第二代基于人工智能的系统的使用。该系统提供了一种早期诊断的手段和一种基于临床有意义的结果参数来改善对治疗的反应的方法。该系统可以提供基于与患者和护理人员相关的参数的患者定制监测工具，并提供临床上有意义的随访工具。该系统可以为罕见病患者提供包容性解决方案，并根据临床反应确保依从性。它具有不依赖于大型数据集的潜在优势，并且是一个动态系统，可以适应患者疾病的持续变化和对治疗的反应。该系统可以提供基于与患者和护理人员相关的参数的患者定制监测工具，并提供临床上有意义的随访工具。该系统可以为罕见病患者提供包容性解决方案，并根据临床反应确保依从性。它具有不依赖于大型数据集的潜在优势，并且是一个动态系统，可以适应患者疾病的持续变化和对治疗的反应。该系统可以提供基于与患者和护理人员相关的参数的患者定制监测工具，并提供临床上有意义的随访工具。该系统可以为罕见病患者提供包容性解决方案，并根据临床反应确保依从性。它具有不依赖于大型数据集的潜在优势，并且是一个动态系统，可以适应患者疾病的持续变化和对治疗的反应。