Evidence map›Paper›PMID 42255338›Full record

ArticlePublic health challenges2026

Knowledge, Attitude, and Perception of Medical Students Toward AI-Based Learning: A Cross-Sectional Study.

Danyal Khan, Muhammad Dawood, Ihtiram Ullah, Husna Irfan Thalib, Sadia Yasir Khan, Farasat Maheen, Mable Pereira

Abstract read
In one paragraph

Article in Public health challenges, 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

7 authors.

Danyal KhanMedicine and Surgery Gomal Medical College Dera Ismail Khan Pakistan.ORCID https://orcid.org/0009-0003-4777-0168
Muhammad DawoodMedicine and Surgery Gomal Medical College Dera Ismail Khan Pakistan.ORCID https://orcid.org/0009-0005-6567-3403
Ihtiram UllahMedicine and Surgery Gomal Medical College Dera Ismail Khan Pakistan.ORCID https://orcid.org/0009-0008-3603-5818
Husna Irfan ThalibGeneral Medicine Practice Program Batterjee Medical College Jeddah Saudi Arabia.ORCID https://orcid.org/0009-0009-6361-6586
Sadia Yasir KhanMedicine and Surgery Gomal Medical College Dera Ismail Khan Pakistan.ORCID https://orcid.org/0009-0009-7269-7576
Farasat MaheenMedicine and Surgery Gomal Medical College Dera Ismail Khan Pakistan.ORCID https://orcid.org/0009-0001-0663-0260
Mable PereiraSchool of Medicine Lincoln American University Georgetown Guyana.ORCID https://orcid.org/0009-0005-6678-7349

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Artificial intelligence (AI) is rapidly transforming healthcare delivery and medical education. Despite its growing global presence, little is known about the knowledge, attitudes, and perceptions (KAP) of medical students in Pakistan regarding AI-based learning. Understanding these factors is essential for guiding curriculum development and preparing future physicians for an AI-driven health system. Objective: To assess the KAP of medical students in Khyber Pakhtunkhwa (KP), Pakistan, toward AI-based learning. Methods: A cross-sectional survey was conducted among 392 medical students from multiple medical colleges in KP. A structured, self-administered questionnaire was used to assess KAP toward AI-based learning. Data were analyzed using SPSS version 26, employing descriptive statistics and chi-square tests to examine group differences. Ordinal logistic regression was performed to identify independent predictors, with statistical significance set at Results: Of the 392 participants, most demonstrated moderate knowledge (62.8%) regarding AI-based learning, whereas only 30.4% achieved high knowledge scores. Significant variation was observed across academic years, with senior students demonstrating greater knowledge than preclinical students ( Conclusion: Medical students in KP exhibit strong enthusiasm and positive perceptions toward AI-based learning, yet their knowledge remains modest and uneven across training levels. This mismatch highlights the urgent need for structured AI education in undergraduate curricula. Incorporating formal training, workshops, and faculty development programs can bridge this gap and better prepare future physicians to responsibly integrate AI into clinical practice.

Indexed as

artificial intelligenceattitudeKhyber Pakhtunkhwaknowledgemedical educationmedical studentsperception

Identifiers

PMID42255338
PMCPMC13241693

What Socratic 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 Socratic graph.