Evidence map›Paper›PMID 42367557›Full record

ArticleCureus2026

Knowledge, Utilization, and Perceptions of Artificial Intelligence in Medical Education Among Preclinical Osteopathic Medical Students and Faculty: A Cross-Sectional Study.

Raju Panta, Laura Francois, Hassan Cordash, Jake Orent

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

4 authors.

Raju PantaPhysiology and Pathology, Burrell College of Osteopathic Medicine, Melbourne, USA.
Laura FrancoisCollege of Medicine, Burrell College of Osteopathic Medicine, Melbourne, USA.
Hassan CordashCollege of Medicine, Burrell College of Osteopathic Medicine, Las Cruces, USA.
Jake OrentCollege of Medicine, Burrell College of Osteopathic Medicine, Melbourne, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction Artificial intelligence (AI), including generative large language models such as ChatGPT and Microsoft Copilot, is increasingly incorporated into medical education. However, empirical data describing AI knowledge, utilization, and perceptions within osteopathic medical education remain limited. This study aimed to assess AI literacy, usage patterns, perceived benefits and concerns, and future readiness among preclinical osteopathic medical students and faculty. Methods A cross-sectional, anonymous, web-based survey was administered to first- and second-year osteopathic medical students and preclinical faculty at Burrell College of Osteopathic Medicine across two campuses. Survey items assessed demographics, AI knowledge and training, educational and scholarly AI use, perceptions, attitudes toward verification and policy, and future perspectives. Descriptive statistics summarized responses, and chi-square tests compared faculty and student groups. Results Ninety‑four responses were received (26.0% response rate), with 58 complete responses (20 faculty and 38 students) included in analyses. Formal AI training was uncommon overall, but faculty were significantly more likely than students to have received any formal AI training (35.0% vs. 2.6%; p = 0.003). Students demonstrated significantly higher use of AI for studying or lecture preparation (76.3% vs. 45.0%; p = 0.036), generating exam questions (63.2% vs. 20.0%; p = 0.002), reviewing exam questions (71.1% vs. 10.0%; p < 0.001), completing written assignments (36.8% vs. 0%; p = 0.005), and writing patient notes (92.1% vs. 5.0%; p < 0.001). Students were also more likely to use AI‑driven digital anatomy platforms (81.6% vs. 35.0%; p = 0.001). Students more frequently identified treatment planning as an area that would benefit from AI integration (31.6% vs. 5.0%; p = 0.048). Conclusions This study provides preliminary institutional insights into AI knowledge, utilization, and perceptions among preclinical osteopathic medical students and faculty at a single osteopathic medical school. Respondents demonstrated moderate baseline AI knowledge and strong interest in future integration, with students showing substantially higher use of AI tools than faculty. These early patterns underscore a need for structured AI literacy education, targeted faculty development, and the establishment of institutional governance to guide responsible and pedagogically sound AI adoption within this educational context.

Indexed as

ai knowledge and useartificial intelligenceattitudes toward use of aifuture perspectives of use of aimedical educationosteopathic medical studentsperception of use of ai

Identifiers

PMID42367557
PMCPMC13296715

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.