Generative AI in Pediatric Gastroenterology

被引:0
|
作者
John M. Rosen [1 ]
机构
[1] University of Arkansas for Medical Sciences, Little Rock, AR
[2] Arkansas Children’s, Little Rock, AR
关键词
AI chatbot; Artificial intelligence; Digital technology; Generative AI; Pediatric gastroenterology;
D O I
10.1007/s11894-024-00946-4
中图分类号
学科分类号
摘要
Purpose of Review: The integration of digital technology into medical practice is often thrust upon clinicians, with standards and routines developed long after initiation. Clinicians should endeavor towards a basic understanding even of emerging technologies so that they can direct its use. The intent of this review is to describe the current state of rapidly evolving generative artificial intelligence (GAI), and to explore both how pediatric gastroenterology practice may benefit as well as challenges that will be faced. Recent Findings: Although little research demonstrating the acceptance, practice, and outcomes associated with GAI in pediatric gastroenterology is published, there are relevant data adjacent to the specialty and overwhelming potential as professed in the media. Best practice guidelines are widely developed in academic publishing and resources to initiate and improve practical user skills are prevalent. Initial published evidence supports broad acceptance of the technology as part of medical practice by clinicians and patients, describes methods with which higher quality GAI can be developed, and identifies the potential for bias and disparities resulting from its use. Summary: GAI is broadly available as a digital tool for incorporation into medical practice and holds promise for improved quality and efficiency of care, but investigation into how GAI can best be used remains at an early stage despite rapid evolution of the technology. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.
引用
收藏
页码:342 / 348
页数:6
相关论文
共 50 条
  • [41] Generative AI in Medicine and Healthcare: Promises, Opportunities and Challenges
    Zhang, Peng
    Kamel Boulos, Maged N.
    FUTURE INTERNET, 2023, 15 (09)
  • [42] Exploring the Use of Generative AI in Education: Broadening the Scope
    Jahic, Irfan
    Ebner, Martin
    Schoen, Sandra
    Edelsbrunner, Sarah
    LEARNING AND COLLABORATION TECHNOLOGIES, PT III, LCT 2024, 2024, 14724 : 283 - 304
  • [43] Playgrounds and Prejudices: Exploring Biases in Generative AI For Children
    Baines, Alexander
    Gruia, Lidia
    Collyer-Hoar, Gail
    Rubegni, Elisa
    PROCEEDINGS OF ACM INTERACTION DESIGN AND CHILDREN CONFERENCE, IDC 2024, 2024, : 839 - 843
  • [44] Generative AI for Secure Physical Layer Communications: A Survey
    Zhao, Changyuan
    Du, Hongyang
    Niyato, Dusit
    Kang, Jiawen
    Xiong, Zehui
    Kim, Dong In
    Shen, Xuemin
    Letaief, Khaled B.
    IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING, 2025, 11 (01) : 3 - 26
  • [45] Developing Personalized Marketing Service Using Generative AI
    Lee, Gun Ho
    Lee, Kyoung Jun
    Jeong, Baek
    Kim, Taekyung
    IEEE ACCESS, 2024, 12 : 22394 - 22402
  • [46] Generative AI Prompt Engineering for Educators: Practical Strategies
    Park, Jiyeon
    Choo, Sam
    JOURNAL OF SPECIAL EDUCATION TECHNOLOGY, 2024,
  • [47] Harnessing the Power of Generative AI to Support ALL Learners
    Evmenova, Anya S.
    Borup, Jered
    Shin, Joan Kang
    TECHTRENDS, 2024, 68 (04) : 820 - 831
  • [48] Generative AI for the Maritime Environments
    Reddy, G. Pradeep
    Sinha, Shrutika
    Park, Soo-Hyun
    2024 FIFTEENTH INTERNATIONAL CONFERENCE ON UBIQUITOUS AND FUTURE NETWORKS, ICUFN 2024, 2024, : 618 - 623
  • [49] Issues and trends in generative AI technologies for decision making
    Phillips-Wren, Gloria
    Virvou, Maria
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2025,
  • [50] A Systematic Review of Generative AI for Teaching and Learning Practice
    Ogunleye, Bayode
    Zakariyyah, Kudirat Ibilola
    Ajao, Oluwaseun
    Olayinka, Olakunle
    Sharma, Hemlata
    EDUCATION SCIENCES, 2024, 14 (06):