Evidence map›Paper›PMID 42422616›Full record

ArticleCureus2026

Unveiling the Artificial Intelligence (AI) Shift: Attitudes Toward and Utilization of AI Models Among Undergraduate Medical Students.

Hitarthi M Joshi, Apexa B Shukla, Pavan J Panchal

Abstract read
In one paragraph

Article in Cureus, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Hitarthi M JoshiPharmacology, GMERS Medical College and General Hospital, Himmatnagar, IND.
Apexa B ShuklaPharmacology, GMERS Medical College and General Hospital, Himmatnagar, IND.
Pavan J PanchalPharmacology, GMERS Medical College and General Hospital, Himmatnagar, IND.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionArtificial intelligence (AI) tools like ChatGPT (OpenAI, San Francisco, USA) are increasingly influencing medical education, yet their use among undergraduate students in Western India remains underexplored. This study aimed to assess students' acceptance, usage patterns, and key factors influencing AI adoption to support effective curriculum integration. METHODOLOGY: This cross-sectional, questionnaire-based study evaluated attitudes toward and use of AI among undergraduate medical students from first year to final year. A structured 37-item English questionnaire was developed via Google Forms (Google LLC, Mountain View, USA) and distributed online using electronic channels, with informed consent obtained. Data were managed in Microsoft Excel 2013 (Microsoft Corp., Redmond, USA) and analyzed using GraphPad Prism version 10.6 (GraphPad Software, Boston, USA; www.graphpad.com), yielding key insights into perceptions and use of AI.

resultsThis study was conducted among 791 medical students, with a mean age of 19.92 ± 1.64 years. Of these, 701 (88.62%) participants engaged with AI, relying predominantly on social media (425 (53.72%)) and faculty lectures (199 (25.15%)) for information. Moreover, 784 (99.11%) participants showed high AI awareness. Multivariable logistic regression demonstrated that AI use was strongly associated with behavior (odds ratio (OR)=356.9; 95% confidence interval (CI): 77.83-6329), perceived usefulness (OR=18.5; 95% CI: 40.68-327), and inversely with perceived risk (OR=0.06; 95% CI: 0.003-0.31). Students more than 20 years of age, male participants, and consistent AI users showed significantly higher usefulness and strong positive attitudes. Notably, first-year students reported a higher perception of risk compared to their peers, underscoring significant variations in AI adoption and perception.

conclusionsThe use of AI models among Western Indian undergraduate medical students and their attitudes were investigated in this study. Although there was variation in students' comprehension of AI-related concepts, the majority of students reported being aware of and having previously used AI tools, mostly through social media platforms. The use of AI and opinions of AI models were correlated with age, gender, academic year, perceived utility, and behavior. The results demonstrate the potential value of responsible and ethical AI guidance in medical education and may offer initial institutional-level insights for future multicentric research and curriculum development.

Indexed as

artificial intelligence toolsattitudemedical undergraduate studentsperceptionutilization

Identifiers

PMID42422616
PMCPMC13344258

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.