Gardes Joël, Maldivi Christophe, Boisset Denis, Aubourg Timothée, Vuillerme Nicolas, Demongeot Jacques
Orange Labs, Meylan, France.
Université Grenoble Alpes, AGEIS, EA 7407, La Tronche 38700, France.
Stud Health Technol Inform. 2019 Aug 21;264:1464-1465. doi: 10.3233/SHTI190486.
In the 5P medicine (Personalized, Preventive, Participative, Predictive and Pluri-expert), the general trend is to process data by displacing the barycenter of the information from hospital centered systems to the patient centered ones through his personal medical records. Today, the use of artificial intelligence for supporting this transition shows real limitations in its implementation in operational practice, both at the level of patient care, but also in the general daily life of the health professional, because of the medico-legal imperatives induced by the promises of the '5P medicine'. In this paper, we propose to fill this gap by introducing an original artificial intelligence platform, named Maxwell, which follows an unsupervised learning approach in line with the medico-legal imperatives of the '5P medicine'. We describe the functional platform characteristics and illustrate them by two examples of clustering in genomics and magnetic resonance imaging.
在5P医学（个性化、预防性、参与性、预测性和多专家性）中，总体趋势是通过将信息重心从以医院为中心的系统转移到以患者为中心的系统（通过其个人病历）来处理数据。如今，由于“5P医学”承诺所引发的医疗法律要求，在实际操作层面，无论是在患者护理方面，还是在医疗专业人员的日常工作中，利用人工智能来支持这一转变都显示出实际局限性。在本文中，我们提议通过引入一个名为麦克斯韦的原创人工智能平台来填补这一空白，该平台遵循一种无监督学习方法，符合“5P医学”的医疗法律要求。我们描述了该功能平台的特点，并通过基因组学和磁共振成像中的两个聚类示例进行说明。
