Evidence map›Paper›PMID 41643188›Full record

ArticleJMIR medical education2026

Effectiveness of Informed AI Use on Clinical Competence of General Practitioners and Internists: Pre-Post Intervention Study.

Eyad A Qunaibi, Ayman M Al-Qaaneh, Baraa F Ismail, Hussam I Muhidat, Farhia S Rageh, Najwa A Musallam, Alaa K Fawzy

Abstract read
In one paragraph

Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

7 authors.

Eyad A QunaibiDepartment of International Research, MedOne Academy, Dover, DE, United States.ORCID 0000-0003-0648-0757
Ayman M Al-QaanehFaculty of Allied Medical Sciences, Al-Balqa Applied University, Al-Salt, Jordan.ORCID 0000-0002-6342-6573
Baraa F IsmailFaculty of Public Health, Jinan University, Tripoli, Lebanon.ORCID 0009-0005-5899-951X
Hussam I MuhidatPriceWaterhouseCoopers Middle East, Doha, Qatar.ORCID 0000-0001-9179-451X
Farhia S RagehFakeeh University Hospital, Dubai, United Arab Emirates.ORCID 0009-0009-5420-1258
Najwa A MusallamKing Hussein Cancer Center, Amman, Jordan.ORCID 0009-0000-3705-713X
Alaa K FawzyMedical Biochemistry and Molecular Biology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt.ORCID 0000-0001-6063-4185

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) shows promise in clinical diagnosis, treatment support, and health care efficiency. However, its adoption in real-world practice remains limited due to insufficient clinical validation and an unclear impact on practitioners' competence. Addressing these gaps is essential for effective, confident, and ethical integration of AI into modern health care settings.

objectiveThis study aimed to evaluate the effectiveness of informed AI use, following a tailored AI training course, on the performance of general practitioners (GPs) and internists in test-based clinical competence assessments and their attitudes toward clinical AI applications.

methodsA pre-post intervention study was conducted with 326 physicians from 39 countries. Participants completed a baseline test of clinical decision-making skills, covering diagnosis, treatment planning, and patient counseling; attended a 1.5-hour online training on effective AI use; and then took a similar postcourse test with AI assistance permitted (GPT-4.0). Test performance and time per question were compared before and after the training. Participants also rated AI accuracy, efficiency, perceived need for structured AI training, and their willingness to use AI in clinical practice before and after the course.

resultsThe average test scores improved from 56.9% (SD 15.7%) to 77.6% (SD 12.7%; P<.001), and the pass rate increased from 6.4% (21/326) to 58.6% (191/326), with larger gains observed among GPs and younger physicians. All skill domains (diagnosis, treatment planning, and patient counseling) improved significantly (all P<.001), while time taken to complete the test increased slightly from before to after the course (mean 40.25, SD 16.14 min vs 42.29, SD 14.02 min; P=.03). By the end of the intervention, physicians viewed AI more favorably, reporting increased confidence in its accuracy and time efficiency, greater appreciation for the need for structured AI training, and increased confidence and willingness to integrate AI into patient care.

conclusionsInformed use of AI, based on tailored training, was associated with higher performance in test-based clinical decision-making assessments and greater confidence in using AI among GPs and internists. Building on previous research that often lacked structured training, focused primarily on model performance, or was limited in clinical scope, this study provides empirical evidence of both competence and perceptual improvement following informed AI use in a large, multinational cohort, enhancing the generalizability. These findings support the integration of structured AI training into medical education and continuing professional development to improve clinical performance and promote competent use of AI in clinical practice.

Indexed as

Artificial IntelligenceClinical CompetenceGeneral PractitionersAdultFemaleHumansMaleMiddle AgedAIAI trainingartificial intelligenceartificial intelligence trainingclinical competenceclinical decision supportdigital health toolsmedical education

Identifiers

PMID41643188
PMCPMC12921430

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Registered trials

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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.