Emerging trends of artificial intelligence in healthcare: a bibliometric outlook

被引:1
作者
Almeida, Maria Ines [1 ]
Rosa, Alvaro [2 ]
Pestana, Helena Castelao Figueira Carlos [3 ]
机构
[1] Inst Univ Lisboa ISCTE IUL, Lisbon, Portugal
[2] Inst Univ Lisboa ISCTE IUL, Business Res Unit BRU IUL, Lisbon, Portugal
[3] Inst Univ Lisboa ISCTE IUL, ISCTE Execut Educ, Lisbon, Portugal
关键词
artificial intelligence; machine learning; deep learning; health; bibliometric analysis; LEARNING ALGORITHM; SCIENCE; WEB; CLASSIFICATION; VALIDATION; EVOLUTION; FUTURE; SCOPUS;
D O I
10.1504/IJHTM.2024.136548
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Emerging technologies are reshaping the landscape of healthcare, with artificial intelligence (AI) spearheading this transformative wave. The exploration of AI within the realm of healthcare is rapidly growing across multiple domains of medicine, with the aim of enhancing the healthcare sector by enabling tailored approaches to diagnosis, prognosis, and patient interventions. This study aims to understand the emerging applications of AI to aid the emergence and implementation of precision medicine. A descriptive bibliometric analysis and a conceptual structure analysis were carried out for this purpose. Our findings suggest that machine and deep learning models are primarily employed for disease diagnosis and prognosis, with a stronger emphasis on clinical specialties like cardiovascular and pulmonary conditions, as well as oncology and radiology. The current and upcoming focus of research revolves around the prominent subject of big data analysis, encompassing the following fundamental data science techniques: segmentation, classification, and processing of medical imaging.
引用
收藏
页数:31
相关论文
共 121 条
  • [21] Bornmann L, 2014, BEYOND BIBLIOMETRICS: HARNESSING MULTIDIMENSIONAL INDICATORS OF SCHOLARLY IMPACT, P201
  • [22] TOWARD A DEFINITION OF BIBLIOMETRICS
    BROADUS, RN
    [J]. SCIENTOMETRICS, 1987, 12 (5-6) : 373 - 379
  • [23] A review of the application of deep learning in medical image classification and segmentation
    Cai, Lei
    Gao, Jingyang
    Zhao, Di
    [J]. ANNALS OF TRANSLATIONAL MEDICINE, 2020, 8 (11)
  • [24] Carrega Miguel Santos H.M. N., 2021, Em, P609, DOI DOI 10.1007/978-3-030-86230-5
  • [25] 3D Convolutional Neural Networks for Brain Tumor Segmentation: A Comparison of Multi-resolution Architectures
    Casamitjana, Adria
    Puch, Santi
    Aduriz, Asier
    Vilaplana, Veronica
    [J]. BRAINLESION: GLIOMA, MULTIPLE SCLEROSIS, STROKE AND TRAUMATIC BRAIN INJURIES, 2016, 2016, 10154 : 150 - 161
  • [26] Cascella M., 2020, STATPEARLS
  • [27] Chadegani A.A., 2013, ASIAN SOC SCI, V9, P18, DOI [https://doi.org/10.5539/ass.v9n5p18, DOI 10.5539/ASS.V9N5P18, 10.5539/ass.v9n5p18]
  • [28] The Times they Are a-Changin' - Healthcare 4.0 Is Coming!
    Chen, Chiehfeng
    Loh, El-Wui
    Kuo, Ken N.
    Tam, Ka-Wai
    [J]. JOURNAL OF MEDICAL SYSTEMS, 2020, 44 (02)
  • [29] A bibliometric and visual analysis of artificial intelligence technologies-enhanced brain MRI research
    Chen, Xieling
    Zhang, Xinxin
    Xie, Haoran
    Tao, Xiaohui
    Wang, Fu Lee
    Xie, Nengfu
    Hao, Tianyong
    [J]. MULTIMEDIA TOOLS AND APPLICATIONS, 2021, 80 (11) : 17335 - 17363
  • [30] Understanding Bibliometric Parameters and Analysis
    Choudhri, Asim F.
    Siddiqui, Adeel
    Khan, Nickalus R.
    Cohen, Harris L.
    [J]. RADIOGRAPHICS, 2015, 35 (03) : 736 - 746