Evidence map›Paper›PMID 39092200›Full record

ReviewFrontiers in oral health2024

Responsible artificial intelligence for addressing equity in oral healthcare.

Zaid H Khoury, Alexys Ferguson, Jeffery B Price, Ahmed S Sultan, Rong Wang

Abstract readReview
In one paragraph

Review in Frontiers in oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zaid H KhouryDepartment of Oral Diagnostic Sciences and Research, School of Dentistry, Meharry Medical College, Nashville, TN, United States.
Alexys FergusonDepartment of Oral Diagnostic Sciences and Research, School of Dentistry, Meharry Medical College, Nashville, TN, United States.
Jeffery B PriceDepartment of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, MD, United States.
Ahmed S SultanDepartment of Oncology and Diagnostic Sciences, University of Maryland School of Dentistry, Baltimore, MD, United States.
Rong WangDepartment of Oral and Craniofacial Sciences, School of Dentistry, University of Missouri-Kansas City, Kansas City, MO, United States.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

artificial intelligencebiasequityexplainable modelsinterpretable modelsoral healthcareresponsible models

Identifiers

PMID39092200
PMCPMC11291357

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.