The healthcare industry is increasingly looking to technological solutions to enhance the efficiency and accuracy of patient interaction and medical data collection. This study investigates the development of a self-anamnesis platform designed to streamline the medical history-taking process. The importance of this topic lies in its potential to alleviate the administrative burdens on healthcare providers, improve patient care by enabling more informed consultations, and enhance the overall patient experience through acknowledgement and reassurance. Despite advancements in healthcare technology, there remains a gap in meeting patients’ desires for longer consultations, more extensive feedback, greater empathy, and the option of receiving care from home. This often results in inefficiencies and increased workloads for healthcare providers. The Erasmus Medical Center (EMC) has expressed interest in implementing an AI avatar to address these needs but is uncertain about the design and functionality it should have. The primary research question of this study is: How can an AI avatar-driven platform, designed to gather patients’ medical histories, be developed to ensure patient acceptance? The research employs a combination of literature review and practical implementation to develop and test the self-anamnesis platform. Key methods include conducting user interviews and tests to investigate patient preferences for the appearance and functionality of AI-driven avatars, designing the platform to be desirable for patients, viable for healthcare professionals, and feasible for software experts. Additionally, the research examines ethical considerations and integrates comprehensive data security measures. The core message of this research is that a well-designed self-anamnesis platform has the potential to significantly improve the efficiency of medical data collection. By addressing the challenges of integration, data security, and user accessibility, such a platform can reduce administrative workloads and facilitate better patient care. The study’s findings suggest that with the right technological and user-centered design considerations, the implementation of a self-anamnesis platform is both feasible and beneficial for modern healthcare systems.