ENTERPRISE AI ANALYSIS
Readiness towards artificial intelligence among medical and dental undergraduate students in Peshawar, Pakistan: a cross-sectional survey
A study assessing the readiness of medical and dental students in Peshawar, Pakistan, for Artificial Intelligence (AI) revealed moderate overall readiness. Key findings include significant gender disparities favoring males in AI readiness scores, while no significant differences were observed between medical and dental fields or across academic years. The study highlights the necessity for curriculum modifications to better prepare future healthcare professionals for AI integration.
Executive Impact: Key Metrics & Opportunities
Integrating AI into healthcare promises transformative improvements in diagnostic accuracy and operational efficiency. However, a lack of readiness among future professionals can impede adoption. This analysis reveals specific areas for intervention to ensure a seamless transition to AI-driven healthcare, emphasizing targeted education and addressing gender disparities.
62.2%
Moderate AI Readiness
29.7%
Students with Low Readiness
8.1%
Students with High Readiness
0.002
Gender Disparity Significance (p-value)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
The study found that the majority of medical and dental students in Peshawar, Pakistan, exhibit a moderate level of readiness for Artificial Intelligence (AI). This indicates a foundational awareness but suggests significant room for improvement in deeper knowledge and practical application skills. A substantial portion also demonstrated low readiness, highlighting the need for targeted educational interventions.
A significant gender disparity in AI readiness was observed, with male students demonstrating higher levels of readiness compared to their female counterparts. This finding is consistent with global trends and points to potential societal, educational, and access-related factors that may influence women's engagement with technology. Addressing these disparities is crucial for equitable AI integration in healthcare.
Interestingly, the study found no significant differences in AI readiness between medical and dental students, suggesting similar levels of exposure or curriculum gaps across both fields. Similarly, AI readiness levels remained relatively consistent across academic years (1st to 5th year), indicating a potential lack of progressive AI education embedded throughout the undergraduate curriculum.
While students showed a solid grasp of AI concepts and their applications (Ability factor), there were identified limitations in visualizing and interpreting AI outputs (Vision factor). Ethical considerations, though reasonably understood, also presented room for improvement. This suggests a need for more focused training in practical AI interpretation and ethical frameworks.
62.2%
of students showed moderate AI readiness, indicating a baseline but requiring further development.
Enterprise Process Flow
Ethical Approval & Consent
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Questionnaire Distribution (Google Form)
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Data Collection (Excel Spreadsheet)
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Statistical Analysis (SPSS v26)
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Reporting & Interpretation
| Gender | Low Readiness (%) | Moderate Readiness (%) | High Readiness (%) | Significance (p-value) |
|---|---|---|---|---|
| Male | 23.5 | 63.3 | 13.3 | 0.002 |
| Female | 34.0 | 61.4 | 4.6 |
Addressing Gender Disparities in AI Education
Previous studies and our findings consistently highlight that male students often exhibit higher AI readiness and confidence. This necessitates targeted interventions. For instance, implementing mentorship programs specifically for female students in AI and technology, creating inclusive curriculum materials, and promoting role models can help bridge the gap. Additionally, offering hands-on workshops that demystify AI concepts and focus on practical applications can boost confidence and engagement among all students, particularly those who might feel less prepared.
| Specialty | Low Readiness (%) | Moderate Readiness (%) | High Readiness (%) | Significance (p-value) |
|---|---|---|---|---|
| Dentistry | 29.7 | 59.4 | 10.9 | 0.655 (Not significant) |
| Medicine | 29.7 | 62.7 | 7.6 |
Enterprise Process Flow
Assess Current Curriculum
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Identify AI Integration Points
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Develop AI Modules & Electives
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Train Faculty on AI Pedagogy
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Pilot & Evaluate New Curriculum
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Scale & Refine Program
p-value 0.526
for college affiliation vs. AI readiness, indicating no significant association.
| Factor | Mean Score | Standard Deviation |
|---|---|---|
| Cognition | 22.95 | 5.815 |
| Ability | 26.48 | 5.917 |
| Vision | 9.99 | 2.412 |
| Ethics | 10.03 | 2.546 |
Enhancing Practical AI Skills and Ethical Understanding
The research identified gaps in 'Vision factor' (interpreting AI outputs) and 'Ethics factor'. To address this, medical and dental curricula should incorporate hands-on labs focused on AI-driven diagnostics and image interpretation, using real-world case studies. Furthermore, dedicated modules on AI ethics, bias, and patient data privacy are essential. Inviting AI specialists for guest lectures and fostering interdisciplinary projects can further enrich students' understanding and practical application of AI in a responsible manner.
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Projected Annual Savings
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Implementation Roadmap
Our phased approach ensures a smooth, effective AI integration.
Phase 1: Readiness Assessment & Gap Analysis (Weeks 1-4)
Conduct a detailed assessment of current curriculum, faculty capabilities, and student baseline AI knowledge. Identify specific gaps in AI literacy, ethical understanding, and practical application skills across medical and dental programs. Form an expert committee to review findings and propose initial recommendations.
Phase 2: Curriculum Development & Faculty Training (Months 2-6)
Design and integrate AI modules into existing medical and dental curricula, focusing on practical applications, ethical considerations, and data interpretation. Develop specialized training programs for faculty to ensure they are proficient in teaching AI concepts and utilizing AI tools. Foster interdisciplinary collaboration.
Phase 3: Pilot Implementation & Feedback (Months 7-12)
Pilot the new AI-integrated curriculum with a cohort of students. Gather continuous feedback from students and faculty through surveys, workshops, and direct observation. Refine curriculum content and teaching methodologies based on pilot results. Conduct initial assessments of student AI readiness post-intervention.
Phase 4: Full-Scale Rollout & Continuous Improvement (Year 2 onwards)
Implement the revised AI curriculum across all academic years and specialties. Establish ongoing evaluation mechanisms to monitor student AI readiness and curriculum effectiveness. Regularly update curriculum content to reflect advancements in AI technology and healthcare applications. Promote research and innovation in AI within the institutions.
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