Responsible artificial intelligence for addressing equity in oral healthcare

被引:2
|
作者
Khoury, Zaid H. [1 ]
Ferguson, Alexys [1 ]
Price, Jeffery B. [2 ,3 ]
Sultan, Ahmed S. [2 ,3 ,4 ]
Wang, Rong [5 ]
机构
[1] Meharry Med Coll, Sch Dent, Dept Oral Diagnost Sci & Res, Nashville, TN USA
[2] Univ Maryland, Sch Dent, Dept Oncol & Diagnost Sci, Baltimore, MD 21201 USA
[3] Univ Maryland, Sch Dent, Div Artificial Intelligence Res, Baltimore, MD 21201 USA
[4] Univ Maryland, Marlene & Stewart Greenebaum Comprehens Canc Ctr, Baltimore, MD 21201 USA
[5] Univ Missouri Kansas City, Sch Dent, Dept Oral & Craniofacial Sci, Kansas City, MO 64110 USA
来源
关键词
artificial intelligence; interpretable models; explainable models; responsible models; equity; bias; oral healthcare; ASSOCIATION; CARIES;
D O I
10.3389/froh.2024.1408867
中图分类号
R78 [口腔科学];
学科分类号
1003 ;
摘要
Oral diseases pose a significant burden on global healthcare. While many oral conditions are preventable and manageable through regular dental office visits, a substantial portion of the population faces obstacles in accessing essential and affordable quality oral healthcare. In this mini review, we describe the issue of inequity and bias in oral healthcare and discuss various strategies to address these challenges, with an emphasis on the application of artificial intelligence (AI). Recent advances in AI technologies have led to significant performance improvements in oral healthcare. AI also holds tremendous potential for advancing equity in oral healthcare, yet its application must be approached with caution to prevent the exacerbation of inequities. The "black box" approaches of some advanced AI models raise uncertainty about their operations and decision-making processes. To this end, we discuss the use of interpretable and explainable AI techniques in enhancing transparency and trustworthiness. Those techniques, aimed at augmenting rather than replacing oral health practitioners' judgment and skills, have the potential to achieve personalized dental and oral care that is unbiased, equitable, and transparent. Overall, achieving equity in oral healthcare through the responsible use of AI requires collective efforts from all stakeholders involved in the design, implementation, regulation, and utilization of AI systems. We use the United States as an example due to its uniquely diverse population, making it an excellent model for our discussion. However, the general and responsible AI strategies suggested in this article can be applied to address equity in oral healthcare on a global level.
引用
收藏
页数:6
相关论文
共 50 条
  • [31] Addressing the challenges of artificial intelligence in medicine
    Smith, Marcus
    Jeffery, Rachael C. Heath
    INTERNAL MEDICINE JOURNAL, 2020, 50 (10) : 1278 - 1281
  • [32] Artificial intelligence: Who is responsible for the diagnosis?
    Neri, Emanuele
    Coppola, Francesca
    Miele, Vittorio
    Bibbolino, Corrado
    Grassi, Roberto
    RADIOLOGIA MEDICA, 2020, 125 (06): : 517 - 521
  • [33] Aspects and Views on Responsible Artificial Intelligence
    Brumen, Bostjan
    Goellner, Sabrina
    Tropmann-Frick, Marina
    MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE, LOD 2022, PT I, 2023, 13810 : 384 - 398
  • [34] Artificial intelligence: Who is responsible for the diagnosis?
    Emanuele Neri
    Francesca Coppola
    Vittorio Miele
    Corrado Bibbolino
    Roberto Grassi
    La radiologia medica, 2020, 125 : 517 - 521
  • [35] Artificial Intelligence Pathologist: The use of Artificial Intelligence in Digital Healthcare
    Kaddour, Asmaa Ben Ali
    Abdulaziz, Nidhal
    2021 IEEE GLOBAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND INTERNET OF THINGS (GCAIOT), 2021, : 31 - 36
  • [36] Responsible Artificial Intelligence in Radiation Oncology
    Teng, C. L.
    Bhullar, A. S.
    Jermain, P.
    Jordon, D.
    Nawfel, R.
    Patel, P.
    Sean, R.
    Shang, M.
    Wu, D. H.
    INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS, 2024, 120 (02): : E659 - E659
  • [37] Responsible artificial intelligence and civic science
    Terrones Rodriguez, Antonio Luis
    REVISTA DE FILOSOFIA AURORA, 2020, 32 (57): : 827 - 847
  • [38] Health Equity and Artificial Intelligence in Nephrology
    Hercz, Adam Engel
    Nicholas, Susanne B.
    Bui, Alex A. T.
    Norris, Keith C.
    CLINICAL JOURNAL OF THE AMERICAN SOCIETY OF NEPHROLOGY, 2025,
  • [39] Equity and Artificial Intelligence in Surgical Care
    Johnson-Mann, Crystal N.
    Loftus, Tyler J.
    Bihorac, Azra
    JAMA SURGERY, 2021, 156 (06) : 509 - 510
  • [40] Advancing health equity with artificial intelligence
    Nicole M. Thomasian
    Carsten Eickhoff
    Eli Y. Adashi
    Journal of Public Health Policy, 2021, 42 : 602 - 611