Evidence map›Paper›PMID 38734438›Full record

ArticleSaudi medical journal2024

Role of artificial intelligence (Google bard) in morphological, histopathological, and radiological image identifications: Objective Structured Practical Examination (OSPE) type-based performance.

Sultan A Meo, Abdulelah A AbuKhalaf, Muhammad Zain S Meo, Muhammad Omair S Meo, Rashid Ayub, Riham A ElToukhy, Adnan M Usmani, Waseem M Hajjar

Abstract read
In one paragraph

Article in Saudi medical journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
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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

8 authors.

Sultan A MeoFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.ORCID https://orcid.org/0000-0001-9820-1852
Abdulelah A AbuKhalafFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Muhammad Zain S MeoFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Muhammad Omair S MeoFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Rashid AyubFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Riham A ElToukhyFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Adnan M UsmaniFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.
Waseem M HajjarFrom the Department of Physiology, College of Medicine, King Saud University (S. Meo); from the College of Medicine, Alfaisal University (AbuKhalaf, M.Z. Meo, M.O. Meo); from the Department of Science, College of Science (Ayub); from the Department of Family Medicine (ElToukhy); from the Department of Medicine (Usmani); from the Department of Surgery (Hajjar), College of Medicine, King Saud University. Riyadh, Kingdom of Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo evaluate the role of artificial intelligence (Google Bard) in figures, scans, and image identifications and interpretations in medical education and healthcare sciences through an Objective Structured Practical Examination (OSPE) type of performance.

methodsThe OSPE type of question bank was created with a pool of medical sciences figures, scans, and images. For assessment, 60 figures, scans and images were selected and entered into the given area of the Google Bard to evaluate the knowledge level.

resultsThe marks obtained by Google Bard in brain structures, morphological and radiological images 7/10 (70%); bone structures, radiological images 9/10 (90%); liver structure and morphological, pathological images 4/10 (40%); kidneys structure and morphological images 2/7 (28.57%); neuro-radiological images 4/7 (57.14%); and endocrine glands including the thyroid, pancreas, breast morphological and radiological images 8/16 (50%). The overall total marks obtained by Google Bard in various OSPE figures, scans, and image identification questions were 34/60 (56.7%).

conclusionGoogle Bard scored satisfactorily in morphological, histopathological, and radiological image identifications and their interpretations. Google Bard may assist medical students, faculty in medical education and physicians in healthcare settings.

Indexed as

Artificial IntelligenceEducational MeasurementEducation, MedicalHumansRadiographydiagnostic roleGoogle Bardimage identificationsknowledge

Identifiers

PMID38734438
PMCPMC11147552

What OpenQuestion holds

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LicenceCC BY-NC
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.