Who Is Really Deciding? AI and Clinical Judgement in Dentistry.

Owais A Farooqi

Abstract readEditorial

In one paragraph

Article in International dental journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

1 author.

Owais A FarooqiDental Service VA Greater Los Angeles Healthcare System and Network Lead Dentist, VA Desert Pacific Network, US Department of Veterans Affairs, Los Angeles, California; School of Dentistry, University of California, Los Angeles, California; American Academy of Artificial Intelligence in Dentistry, Los Angeles, California. Electronic address: info@aaai-d.org.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The integration of AI in dental practice, primarily through AI-enabled software, influences clinical workflows by promoting rapid decision-making and potentially increasing bias stemming from its automation bias. While these systems demonstrate high accuracy in standardised evaluations, their real-world performance, particularly in atypical cases, remains less validated. This reliance on AI necessitates a broader AI literacy in clinicians, focusing on ethical awareness and critical judgement rather than solely technical proficiency. Ongoing evaluation of AI systems must encompass their impact on clinical reasoning, and dental education should prepare professionals to discern when to slow down their intuitive judgements in response to algorithmic insights to enhance clinical accountability and trust.

Indexed as

Artificial IntelligenceClinical Decision-MakingDentistryJudgmentDecision MakingHumansArtificial intelligenceAutomation biasClinical decision-makingClinical judgmentdental educationDentistry

Identifiers

PMID41825434

DOI10.1016/j.identj.2026.109476

PMCPMC12996637

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.