Evidence map›Paper›PMID 40786464›Full record

ArticleJMA journal2025

Exploring Undergraduate Medical Students' Perspectives towards Artificial Intelligence in Healthcare: A Qualitative Study from India.

Sonali Sharma, Neha Saboo, Vaseem Naheed Baig

Abstract read
In one paragraph

Article in JMA journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 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

4 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Article
  4. 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

3 authors.

Sonali SharmaDepartment of Biochemistry, RUHS College of Medical Sciences, Jaipur, India.
Neha SabooDepartment of Physiology, RUHS College of Medical Sciences, Jaipur, India.
Vaseem Naheed BaigDepartment of Community Medicine, RUHS College of Medical Sciences, Jaipur, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: The term "artificial intelligence (AI)" refers to the development of systems that possess intellectual processes characteristic of humans, such as the ability to reason, discern meaning, generalize, and learn from experience. This study aimed to provide an overview of medical students' knowledge, perceptions, and concerns about AI in healthcare. Methods: This study utilized a qualitative approach, employing semi-structured interviews in focus groups of medical undergraduates to explore their perceptions, knowledge, and concerns about AI in healthcare. The interviews complied with the standards for reporting qualitative research set by the Consolidated Criteria for Reporting Qualitative Research (COREQ). Seven focus groups were formed, with an average size of 8-10 participants per group. Each group was diverse in terms of sex and year of study. Results: 156 participants responded to the survey, of whom 124 completed the questionnaires. Sixty-six undergraduate medical students consented to attend in-person focus groups for discussions. Four major themes are the advantages of AI in healthcare, education, and training in AI, future implications of AI, and ethical concerns, and sixteen subthemes emerged from this study's data analysis. Most students (57.7%) believed that artificial intelligence (AI) should be integrated into medical education. A substantial proportion (42.2%) of medical students demonstrated varying knowledge regarding the advantages of AI in healthcare. A significant number of students, 37.8%, articulated concerns regarding the future implications of AI; a minority of students, 22.7%, expressed ethical concerns regarding biases, privacy issues, security risks, and unequal access. Conclusions: Medical students generally view AI positively, recognizing its benefits in diagnosis and treatment. Many emphasized the need to integrate AI into medical education to prepare for future changes in healthcare. Future research should focus on developing evidence-based training programs and strategies to tackle these issues.

Indexed as

artificial Intelligenceconcernhealthcareknowledgemachine learningmedical educationperceptionqualitative

Identifiers

PMID40786464
PMCPMC12329140

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.