Menegatti Danilo, Giuseppi Alessandro, Delli Priscoli Francesco, Pietrabissa Antonio, Di Giorgio Alessandro, Baldisseri Federico, Mattioni Mattia, Monaco Salvatore, Lanari Leonardo, Panfili Martina, Suraci Vincenzo
Department of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy.
Healthcare (Basel). 2023 Aug 4;11(15):2199. doi: 10.3390/healthcare11152199.
Data-driven algorithms have proven to be effective for a variety of medical tasks, including disease categorization and prediction, personalized medicine design, and imaging diagnostics. Although their performance is frequently on par with that of clinicians, their widespread use is constrained by a number of obstacles, including the requirement for high-quality data that are typical of the population, the difficulty of explaining how they operate, and ethical and regulatory concerns. The use of data augmentation and synthetic data generation methodologies, such as federated learning and explainable artificial intelligence ones, could provide a viable solution to the current issues, facilitating the widespread application of artificial intelligence algorithms in the clinical application domain and reducing the time needed for prevention, diagnosis, and prognosis by up to 70%. To this end, a novel AI-based functional framework is conceived and presented in this paper.
数据驱动的算法已被证明在各种医疗任务中有效，包括疾病分类与预测、个性化医疗设计以及影像诊断。尽管它们的表现常常与临床医生相当，但其广泛应用受到诸多障碍的限制，包括需要典型人群的高质量数据、难以解释其运作方式以及伦理和监管方面的担忧。使用数据增强和合成数据生成方法，如联邦学习和可解释人工智能方法，可能为当前问题提供可行的解决方案，促进人工智能算法在临床应用领域的广泛应用，并将预防、诊断和预后所需时间最多减少70%。为此，本文构思并提出了一个新颖的基于人工智能的功能框架。
