Evidence map›Paper›PMID 35412463›Full record

ArticleJMIR medical education2022

Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study.

Thomas Boillat, Faisal A Nawaz, Homero Rivas

Open access · goldAbstract read
In one paragraph

Article in JMIR medical education, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 2 pooled it
6.3field-weighted citation impact, top 3% of its field
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

53 citing papers in PubMed, 2 syntheses or guidelines pooled it, 107 citations in OpenAlex.

  1. Pooled it
  2. AI in Medical Questionnaires: Scoping ReviewJournal of medical Internet research · 2025
    Pooled it
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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

3 authors at 1 institution in 1 country.

Thomas Boillat *Design Lab, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.ORCID https://orcid.org/0000-0002-4684-8969
Faisal A NawazCollege of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.ORCID https://orcid.org/0000-0002-0701-7628
Homero RivasDesign Lab, College of Medicine, Mohammed Bin Rashid University of Medicine and Health Sciences, Dubai, United Arab Emirates.ORCID https://orcid.org/0000-0003-0413-9328
Mohammed Bin Rashid University of Medicine and Health Sciences · AE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSimilar to understanding how blood pressure is measured by a sphygmomanometer, physicians will soon have to understand how an artificial intelligence-based application has come to the conclusion that a patient has hypertension, diabetes, or cancer. Although there are an increasing number of use cases where artificial intelligence is or can be applied to improve medical outcomes, the extent to which medical doctors and students are ready to work and leverage this paradigm is unclear.

objectiveThis research aims to capture medical students' and doctors' level of familiarity toward artificial intelligence in medicine as well as their challenges, barriers, and potential risks linked to the democratization of this new paradigm.

methodsA web-based questionnaire comprising five dimensions-demographics, concepts and definitions, training and education, implementation, and risks-was systematically designed from a literature search. It was completed by 207 participants in total, of which 105 (50.7%) medical doctors and 102 (49.3%) medical students trained in all continents, with most of them in Europe, the Middle East, Asia, and North America.

resultsThe results revealed no significant difference in the familiarity of artificial intelligence between medical doctors and students (P=.91), except that medical students perceived artificial intelligence in medicine to lead to higher risks for patients and the field of medicine in general (P<.001). We also identified a rather low level of familiarity with artificial intelligence (medical students=2.11/5; medical doctors=2.06/5) as well as a low attendance to education or training. Only 2.9% (3/105) of medical doctors attended a course on artificial intelligence within the previous year, compared with 9.8% (10/102) of medical students. The complexity of the field of medicine was considered one of the biggest challenges (medical doctors=3.5/5; medical students=3.8/5), whereas the reduction of physicians' skills was the most important risk (medical doctors=3.3; medical students=3.6; P=.03).

conclusionsThe question is not whether artificial intelligence will be used in medicine, but when it will become a standard practice for optimizing health care. The low level of familiarity with artificial intelligence identified in this study calls for the implementation of specific education and training in medical schools and hospitals to ensure that medical professionals can leverage this new paradigm and improve health outcomes.

Indexed as

artificial intelligence in medicinehealth caremedical doctorsmedical studentsquestionnaire

Identifiers

PMID35412463
PMCPMC9044144
OpenAlexW4212993683

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.