Knowledge And Attitude of Artificial Intelligence Among Medical And Dental Students of a Public Sector University of Karachi: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Artificial Intelligence (AI) is transforming healthcare, revolutionizing treatment, diagnosis and patient care. It has the potential to not only predict clinical events but also provide prognosis and aid drug discovery. As AI advances, it is crucial to train medical experts for an AI-enhanced healthcare system. This can be achieved by educating future medical and dental students. The first step in doing so is to assess the baseline knowledge at which they currently stand as well as exploring their insights on the famous stigma of AI replacing their future career choices and the current status of AI use in their country. Additionally, exploring ways through which students prefer AI to be incorporated; either curricular or extra-curricular. Methods: A questionnaire-based survey was conducted from August-November 2023, following IRB approval (Reference No: JSMU/IRB/2023/752) from the Institutional Review Board of JSMU, which was granted on June 24, 2023. The questionnaire, adapted from a similar Canadian study, was distributed via social media to medical and dental students over 18 years old at JSMU. A convenience sampling technique collected 324 completed and consented responses via forms. Statistical analysis was performed with SPSS version 20, using Chi-square test to compare factor variables (gender and year of study) with outcome variable (knowledge about AI). Results: Most students (74.38%) obtained information about AI from social media.Majority (79.9%) reported being somewhat familiar with AI, but only 22.53% chose accurate definition. Students' program and year of study impacted their knowledge of AI (P < 0.05), while gender did not (P > 0.05). Over half (56.17%) of the population believed students need to learn AI basics, and 53.09% agreed AI will revolutionize healthcare. Top benefits selected were early accurate diagnosis, improved accessibility, and automation of routine tasks. Most students (48.15%) favored integrating AI into their curriculum. A majority of MBBS (47.7%) and BDS (41.33%) students believed AI might replace their careers in the future. Notably, 60.19% of students believed AI will improve patient care, while 45.68% thought it would raise healthcare costs. Conclusion: Students acknowledge AI’s potential, but lack the necessary knowledge. Recognition of this gap highlights the need for robust educational planning either curricular or extracurricular with the learning objectives as highlighted by the preferences chosen by the students. This study helped address students’ notion about AI replacing careers and identifies the need to educate students about the potential of AI and how it can efficiently supplement but not replace healthcare. We believe that these findings offer valuable guidance for education and health policymakers to plan curricula and integrate modulations for future advancements. The limitations include the study being limited to a single university and two departments, and the use of convenience sampling, which may have affected the generalizability of the findings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".