Musacchio Nicoletta, Giancaterini Annalisa, Guaita Giacomo, Ozzello Alessandro, Pellegrini Maria A, Ponzani Paola, Russo Giuseppina T, Zilich Rita, de Micheli Alberto
Italian Association of Diabetologists, Rome, Italy.
Diabetology Service, Muggiò Polyambulatory, Azienda Socio Sanitaria Territoriale, Monza, Italy.
J Med Internet Res. 2020 Jun 22;22(6):e16922. doi: 10.2196/16922.
Since the last decade, most of our daily activities have become digital. Digital health takes into account the ever-increasing synergy between advanced medical technologies, innovation, and digital communication. Thanks to machine learning, we are not limited anymore to a descriptive analysis of the data, as we can obtain greater value by identifying and predicting patterns resulting from inductive reasoning. Machine learning software programs that disclose the reasoning behind a prediction allow for "what-if" models by which it is possible to understand if and how, by changing certain factors, one may improve the outcomes, thereby identifying the optimal behavior. Currently, diabetes care is facing several challenges: the decreasing number of diabetologists, the increasing number of patients, the reduced time allowed for medical visits, the growing complexity of the disease both from the standpoints of clinical and patient care, the difficulty of achieving the relevant clinical targets, the growing burden of disease management for both the health care professional and the patient, and the health care accessibility and sustainability. In this context, new digital technologies and the use of artificial intelligence are certainly a great opportunity. Herein, we report the results of a careful analysis of the current literature and represent the vision of the Italian Association of Medical Diabetologists (AMD) on this controversial topic that, if well used, may be the key for a great scientific innovation. AMD believes that the use of artificial intelligence will enable the conversion of data (descriptive) into knowledge of the factors that "affect" the behavior and correlations (predictive), thereby identifying the key aspects that may establish an improvement of the expected results (prescriptive). Artificial intelligence can therefore become a tool of great technical support to help diabetologists become fully responsible of the individual patient, thereby assuring customized and precise medicine. This, in turn, will allow for comprehensive therapies to be built in accordance with the evidence criteria that should always be the ground for any therapeutic choice.
在过去十年中，我们的大部分日常活动都已数字化。数字健康考虑到了先进医疗技术、创新与数字通信之间日益增强的协同作用。得益于机器学习，我们不再局限于对数据进行描述性分析，因为通过识别和预测归纳推理产生的模式，我们能够获得更大的价值。能够揭示预测背后推理过程的机器学习软件程序可实现“假设分析”模型，通过该模型可以了解改变某些因素是否以及如何能够改善结果，从而确定最佳行为。目前，糖尿病护理面临着诸多挑战：糖尿病专家数量减少、患者数量增加、就诊时间缩短、从临床和患者护理角度来看疾病的复杂性日益增加、实现相关临床目标困难、疾病管理给医护人员和患者带来的负担不断加重，以及医疗保健的可及性和可持续性问题。在这种背景下，新的数字技术和人工智能的应用无疑是一个巨大的机遇。在此，我们报告对当前文献进行仔细分析的结果，并阐述意大利糖尿病医学专家协会（AMD）在这个有争议话题上的观点，即如果运用得当，这可能是重大科学创新的关键。AMD认为，人工智能的应用将使数据（描述性）转化为对“影响”行为和相关性（预测性）因素的认识，从而确定可能带来预期结果改善的关键方面（规范性）。因此，人工智能可以成为强大的技术支持工具，帮助糖尿病专家全面负责个体患者，从而确保个性化精准医疗。反过来，这将使我们能够依据始终应作为任何治疗选择基础的证据标准构建综合治疗方案。
