A cross-sectional study led by Zagazig University and collaborators conducted a structured online survey of 423 medical students from ten Egyptian universities, assessing their understanding, attitudes, and practices regarding generative artificial intelligence. Findings indicate 61.5% satisfactory knowledge levels, higher scores among males and clinical-phase students, and widespread use of Chat-GPT tools for academic tasks.
Key points
An 8-question knowledge score, 13-item attitude Likert scale, and 7-item practice frequency scale evaluated generative AI competencies among 423 Egyptian medical students.
Binary logistic regression revealed male gender (OR=1.87), 6th October University affiliation (OR=3.55), and clinical-phase status (OR=0.54) as significant predictors of satisfactory AI knowledge (p<0.05).
Students primarily employed Chat-GPT 3.5 (37.1%) and 4 (35.2%) for grammar correction, assignment preparation, research, and idea generation, correlating with knowledge scores (r=0.303, p<0.001).
Why it matters:
Understanding medical students’ readiness for generative AI informs curriculum design for future healthcare education and practice.
Q&A
What is generative artificial intelligence?
How were knowledge, attitude, and practice measured?
Which factors influenced AI knowledge levels?
Why do students use generative AI in academics?
How can medical curricula integrate generative AI?
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Academy
Generative Artificial Intelligence in Healthcare Education
Generative artificial intelligence (AI) refers to computer systems that learn patterns from large datasets to generate new content—text, images, code, or other media formats. Models such as OpenAI’s Chat-GPT use neural network architectures called transformers, which predict the next word or token in a sequence based on massive pretraining on internet text. In healthcare education, generative AI offers tools to assist students with writing assignments, grammar correction, idea brainstorming, and rapid information retrieval.
For longevity enthusiasts without a deep biology background, understanding generative AI’s role in healthcare starts with recognizing its key components:
- Data Input: The model is trained on vast text corpora, including medical literature, textbooks, and online articles.
- Neural Architecture: Transformers use attention mechanisms to weigh the importance of different words and contexts, enabling coherent text generation.
- Fine-Tuning: Pretrained models can be fine-tuned on specialized medical datasets to improve relevance and accuracy for healthcare-related queries.
- Deployment: Accessible through web interfaces or APIs, students can interact with generative AI without coding expertise.
Applications in Medical Training:
- Writing Assistance: AI tools help draft case reports, essays, and reflective journals, improving clarity and saving time.
- Language Support: Non-native English speakers benefit from real-time grammar and style corrections.
- Study Aid: AI-generated summaries of research articles or textbook chapters support quick reviews before exams.
- Simulation Scenarios: Generative AI can script patient dialogues for role-playing exercises, enhancing clinical communication skills.
Ethical and Practical Considerations: While generative AI can boost efficiency, educators must guide students on verifying AI outputs, avoiding plagiarism, and understanding data privacy. Integrating dedicated modules into medical school curricula—covering AI fundamentals, hands-on workshops, and ethical debates—ensures students use these tools responsibly.
By embracing generative AI in healthcare education, institutions can prepare future clinicians to leverage cutting-edge technologies for improved patient care, research innovation, and lifelong learning.
