Beyond the hype of big data and artificial intelligence: building foundations for knowledge and wisdom

被引:0
|
作者
Josip Car
Aziz Sheikh
Paul Wicks
Marc S. Williams
机构
[1] Nanyang Technological University Singapore,Centre for Population Health Sciences (CePHaS), Lee Kong Chian School of Medicine
[2] The University of Edinburgh,The Usher Institute
[3] PatientsLikeMe,undefined
[4] Genomic Medicine Institute,undefined
来源
BMC Medicine | / 17卷
关键词
Big data; Electronic health records; Artificial intelligence; Internet of things; Digital health; Genomics; Data sharing; Data privacy; Ethics;
D O I
暂无
中图分类号
学科分类号
摘要
Big data, coupled with the use of advanced analytical approaches, such as artificial intelligence (AI), have the potential to improve medical outcomes and population health. Data that are routinely generated from, for example, electronic medical records and smart devices have become progressively easier and cheaper to collect, process, and analyze. In recent decades, this has prompted a substantial increase in biomedical research efforts outside traditional clinical trial settings. Despite the apparent enthusiasm of researchers, funders, and the media, evidence is scarce for successful implementation of products, algorithms, and services arising that make a real difference to clinical care. This article collection provides concrete examples of how “big data” can be used to advance healthcare and discusses some of the limitations and challenges encountered with this type of research. It primarily focuses on real-world data, such as electronic medical records and genomic medicine, considers new developments in AI and digital health, and discusses ethical considerations and issues related to data sharing. Overall, we remain positive that big data studies and associated new technologies will continue to guide novel, exciting research that will ultimately improve healthcare and medicine—but we are also realistic that concerns remain about privacy, equity, security, and benefit to all.
引用
收藏
相关论文
共 50 条
  • [41] Editorial: Big data and artificial intelligence in ophthalmology
    Thakur, Sahil
    Rim, Tyler Hyungtaek
    Ting, Darren S. J.
    Hsieh, Yi-Ting
    Kim, Tae-im
    FRONTIERS IN MEDICINE, 2023, 10
  • [42] Big data and artificial intelligence in cancer research
    Wu, Xifeng
    Li, Wenyuan
    Tu, Huakang
    TRENDS IN CANCER, 2024, 10 (02) : 147 - 160
  • [43] Big data in medicine: The upcoming artificial intelligence
    Chang, Anthony C.
    PROGRESS IN PEDIATRIC CARDIOLOGY, 2016, 43 : 91 - 94
  • [44] Knowledge and Data in Artificial Intelligence Systems
    Gribova, V. V.
    Kobrinskii, B. A.
    PATTERN RECOGNITION AND IMAGE ANALYSIS, 2024, 34 (03) : 429 - 433
  • [45] Medical Big Data and Artificial Intelligence for Healthcare
    Zhang, Yudong
    Hong, Jin
    Chen, Shuwen
    APPLIED SCIENCES-BASEL, 2023, 13 (06):
  • [46] Teacher Intelligence Training Based on Big Data and Artificial Intelligence
    Dan, Songjian
    INTERNATIONAL JOURNAL OF E-COLLABORATION, 2022, 18 (03)
  • [47] Ontologies, Knowledge Representation, Artificial Intelligence - Hype or Prerequisites for International pHealth Interoperability?
    Blobel, Bernd
    E-HEALTH ACROSS BORDERS WITHOUT BOUNDARIES: E-SALUS TRANS CONFINIA SINE FINIBUS, 2011, 165 : 11 - 20
  • [48] Going beyond the "common suspects": to be presumed innocent in the era of algorithms, big data and artificial intelligence
    Sachoulidou, Athina
    ARTIFICIAL INTELLIGENCE AND LAW, 2023,
  • [49] Data, artificial intelligence and policy-making: hubris, hype and hope
    Holford, John
    Milana, Marcella
    Waller, Richard
    Webb, Sue
    Hodge, Steven
    INTERNATIONAL JOURNAL OF LIFELONG EDUCATION, 2019, 38 (06) : III - VII
  • [50] The Foundations for Building the National Idea of Russia: Intelligence Augmentation as an Alternative to Artificial General Intelligence
    Ivanchenko, Maria A.
    VOPROSY FILOSOFII, 2025, (02)