Tchuente Foguem Germaine, Teguede Keleko Aurelien
Faculté des Sciences, Université de Yaoundé I, PO Box 812, Yaoundé, Cameroon.
Ecole Nationale d'Ingénieurs de Tarbes (ENIT), 47 Avenue Azereix, BP 1629, 65016 Tarbes, France.
AI Ethics. 2023 Mar 7:1-31. doi: 10.1007/s43681-023-00267-8.
Advances in Artificial Intelligence (AI) offer new Information Technology (IT) opportunities in various applications and fields (industry, health, etc.). The medical informatics scientific community expends tremendous effort on the management of diseases affecting vital organs making it a complex disease (lungs, heart, brain, kidneys, pancreas, and liver). Scientific research becomes more complex when several organs are simultaneously affected, as is the case with Pulmonary Hypertension (PH), which affects both the lungs and the heart. Therefore, early detection and diagnosis of PH are essential to monitor the disease's progression and prevent associated mortality.
The issue addressed relates to knowledge of recent developments in AI approaches applied to PH. The aim is to provide a systematic review through a quantitative analysis of the scientific production concerning PH and the analysis of the networks of this production. This bibliometric approach is based on various statistical, data mining, and data visualization methods to assess research performance using scientific publications and various indicators (e.g., direct indicators of scientific production and scientific impact).
The main sources used to obtain citation data are the Web of Science Core Collection and Google Scholar. The results indicate a diversity of journals (e.g., IEEE Access, Computers in Biology and Medicine, Biology Signal Processing and Control, Frontiers in Cardiovascular Medicine, Sensors) at the top of publications. The most relevant affiliations are universities from United States of America (Boston Univ, Harvard Med Sch, Univ Oxford, Stanford Univ) and United Kingdom (Imperial Coll London). The most cited keywords are "Classification", "Diagnosis", "Disease", "Prediction", and "Risk".
This bibliometric study is a crucial part of the review of the scientific literature on PH. It can be viewed as a guideline or tool that helps researchers and practitioners to understand the main scientific issues and challenges of AI modeling applied to PH. On the one hand, it makes it possible to increase the visibility of the progress made or the limits observed. Consequently, it promotes their wide dissemination. Furthermore, it offers valuable assistance in understanding the evolution of scientific AI activities applied to managing the diagnosis, treatment, and prognosis of PH. Finally, ethical considerations are described in each activity of data collection, treatment, and exploitation to preserve patients' legitimate rights.
人工智能（AI）的进步在各种应用和领域（工业、健康等）带来了新的信息技术（IT）机遇。医学信息科学界在影响重要器官的疾病管理方面投入了巨大努力，使其成为一种复杂疾病（肺部、心脏、大脑、肾脏、胰腺和肝脏）。当多个器官同时受到影响时，科学研究变得更加复杂，肺动脉高压（PH）就是如此，它会影响肺部和心脏。因此，早期检测和诊断PH对于监测疾病进展和预防相关死亡率至关重要。
所解决的问题涉及应用于PH的人工智能方法的最新发展知识。目的是通过对有关PH的科学产出进行定量分析以及对该产出的网络进行分析来提供系统综述。这种文献计量方法基于各种统计、数据挖掘和数据可视化方法，以使用科学出版物和各种指标（例如，科学产出和科学影响的直接指标）来评估研究绩效。
用于获取引用数据的主要来源是科学网核心合集和谷歌学术。结果表明，在出版物排名靠前的期刊种类多样（例如，《IEEE 接入》《生物医学中的计算机》《生物信号处理与控制》《心血管医学前沿》《传感器》）。最相关的机构是美国（波士顿大学、哈佛医学院、牛津大学、斯坦福大学）和英国（帝国理工学院）的大学。被引用最多的关键词是“分类”“诊断”“疾病”“预测”和“风险”。
这项文献计量研究是PH科学文献综述的关键部分。它可以被视为一种指南或工具，帮助研究人员和从业者理解应用于PH的人工智能建模的主要科学问题和挑战。一方面，它能够提高所取得进展或观察到的局限性的可见性。因此，它促进了这些进展的广泛传播。此外，它在理解应用于PH诊断、治疗和预后管理的科学人工智能活动的演变方面提供了有价值的帮助。最后，在数据收集、处理和利用的每一项活动中都描述了伦理考量，以维护患者的合法权利。
