Evidence map›Paper›PMID 40012697›Full record

ArticleCureus2025

ChatGPT-4 as an Assistant for Evidence-Based Decision-Making Among General Dentists: An Observational Feasibility Study.

Bugude Shiva Shankar, Sasankoti Mohan

Abstract read
In one paragraph

Article in Cureus, 2025. 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

2 authors.

Bugude Shiva ShankarPeriodontology, Qassim University, Buraydah, SAU.
Sasankoti MohanOral Medicine and Radiology, Hope Health Inc, Florence, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background Evidence-based decision-making (EBDM) is essential in contemporary dentistry. However, navigating the extensive and constantly evolving scientific literature can be challenging. Large language models (LLMs), such as ChatGPT-4, have the potential to transform EBDM by analyzing vast datasets and extracting critical information, thereby significantly reducing the time required to find evidence. This observational feasibility study investigates ChatGPT-4's potential in dental EBDM, focusing on its capabilities, strengths, and limitations. Materials and methods In this observational feasibility study, two independent examiners conducted interactive sessions with ChatGPT-4. Five clinical scenarios were explored using the Google Chrome web browser, accessing publicly available scientific evidence from Cochrane, ADA, and PubMed. This approach ensured compliance with the Cochrane guidelines for EBDM. Two independent dentists engaged with ChatGPT-4 in simulated real-life clinical scenarios to seek scientific information. The output from ChatGPT-4 for each scenario was assessed based on predetermined criteria. Its responses were evaluated for accuracy, relevance, efficiency, actionability, and ethical considerations using the ChatGPT-4 Response Scoring System (CRSS) and the ChatGPT-4 Generative Ability Matrix (C-GAM). Results ChatGPT-4 demonstrated consistent performance across all five clinical scenarios, achieving a C-GAM score of 46.4% and a CRSS score of 12 out of 28. It effectively identified relevant sources of evidence and provided concise summaries, potentially saving valuable time and enhancing access to information. No significant differences in scores were found when the responses to all clinical scenarios were analyzed independently by the two researchers. However, a notable limitation was its inability to provide specific web links directing users to relevant scientific articles. Additionally, while ChatGPT-4 offered suggestions for incorporating the latest scientific publications into decision-making, it could not generate direct links to these articles. Conclusion Despite its current limitations, ChatGPT-4, as a generative AI, can assist clinicians in making evidence-based decisions. It can save time compared to conventional search engines. Ethical considerations must be prioritized in training these models to ensure that clinicians make responsible, evidence-based decisions rather than relying solely on specific evidence statements provided by ChatGPT-4. This model shows its potential as an AI tool for EBDM in dentistry. Further development and training could address existing limitations and enhance its effectiveness; however, clinicians must retain ultimate responsibility for informed decisions, necessitating expertise and critical evaluation of the evidence presented.

Indexed as

aichatgptdentistryebdmlarge language models

Identifiers

PMID40012697
PMCPMC11859412

What OpenQuestion holds

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LicenceCC BY
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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.