Evidence map›Paper›PMID 39068482›Full record

ArticleBMC medical education2024

Artificial intelligence in medical education - perception among medical students.

Preetha Jackson, Gayathri Ponath Sukumaran, Chikku Babu, M Christa Tony, Deen Stephano Jack, V R Reshma, Dency Davis, Nisha Kurian, Anjum John

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In one paragraph

Article in BMC medical education, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 90 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
90citing papers in PubMed, 6 pooled it
–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

90 citing papers in PubMed, 6 syntheses or guidelines pooled it.

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  5. [Medical education and artificial intelligence: perspectives and ethical challenges].Revista medica del Instituto Mexicano del Seguro Social · 2025
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30 more citing papers are in PubMed but not listed here.

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

9 authors.

Preetha JacksonPushpagiri Medical College, Tiruvalla, Kerala, India.
Gayathri Ponath SukumaranPushpagiri Medical College, Tiruvalla, Kerala, India.
Chikku BabuPushpagiri Medical College, Tiruvalla, Kerala, India.
M Christa TonyPushpagiri Medical College, Tiruvalla, Kerala, India.
Deen Stephano JackPushpagiri Medical College, Tiruvalla, Kerala, India.
V R ReshmaPushpagiri Medical College, Tiruvalla, Kerala, India.
Dency DavisPushpagiri Medical College, Tiruvalla, Kerala, India.
Nisha KurianPushpagiri Medical College, Tiruvalla, Kerala, India.
Anjum JohnPushpagiri Medical College, Tiruvalla, Kerala, India. dranjumsjohn@pimsrc.edu.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAs Artificial Intelligence (AI) becomes pervasive in healthcare, including applications like robotic surgery and image analysis, the World Medical Association emphasises integrating AI education into medical curricula. This study evaluates medical students' perceptions of 'AI in medicine', their preferences for AI training in education, and their grasp of AI's ethical implications in healthcare. MATERIALS &

methodsA cross-sectional study was conducted among 325 medical students in Kerala using a pre-validated, semi structured questionnaire. The survey collected demographic data, any past educational experience about AI, participants' self-evaluation of their knowledge and evaluated self-perceived understanding of applications of AI in medicine. Participants responded to twelve Likert-scale questions targeting perceptions and ethical aspects and their opinions on suggested topics on AI to be included in their curriculum. RESULTS & DISCUSSION: AI was viewed as an assistive technology for reducing medical errors by 57.2% students and 54.2% believed AI could enhance medical decision accuracy. About 49% agreed that AI could potentially improve accessibility to healthcare. Concerns about AI replacing physicians were reported by 37.6% and 69.2% feared a reduction in the humanistic aspect of medicine. Students were worried about challenges to trust (52.9%), patient-physician relationships (54.5%) and breach of professional confidentiality (53.5%). Only 3.7% felttotally competent in informing patients about features and risks associated with AI applications. Strong demand for structured AI training was expressed, particularly on reducing medical errors (76.9%) and ethical issues (79.4%).

conclusionThis study highlights medical students' demand for structured AI training in undergraduate curricula, emphasising its importance in addressing evolving healthcare needs and ethical considerations. Despite widespread ethical concerns, the majority perceive AI as an assistive technology in healthcare. These findings provide valuable insights for curriculum development and defining learning outcomes in AI education for medical students.

Indexed as

Artificial IntelligenceCurriculumStudents, MedicalAdultAttitude of Health PersonnelCross-Sectional StudiesEducation, MedicalEducation, Medical, UndergraduateFemaleHumansIndiaMaleSurveys and QuestionnairesYoung AdultArtificial intelligenceHealthcareMedical curriculumMedical educationMedical ethics

Identifiers

PMID39068482
PMCPMC11283685

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.