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Exploring the Ethical Implications of ChatGPT in Medical Education: Privacy, Accuracy, and Professional Integrity in
Hafiz Muhammad Amad Abdullah1, Noor-I-Kiran Naeem2,3,1, Ghufran Ali Malkana1
1Medical Education, ABWA Medical College, Faisalabad, PAK.
Background:
The inclusion of artificial intelligence in medical education, specifically through the use of ChatGPT (OpenAI, San Francisco, CA), has transformed learning and generated many ethical questions. This study aims to analyze the medical students' ethical concerns about using ChatGPT in medical education, focusing on privacy, accuracy, and professional integrity.
Methods:
The study format was a cross-sectional survey distributed to 219 medical students at ABWA Medical College, Pakistan. A pre-validated, pre-structured questionnaire was created with Google Forms, including questions regarding the accuracy of ChatGPT, confidentiality, and its impact on the students' critical thinking faculties. This information was collected after obtaining informed consent. The data collected were descriptively and inferentially analyzed using SPSS software version 26.0 (IBM Corp., Armonk, NY).
Results:
In total, 95% of respondents (n = 190) confirmed that the information provided by ChatGPT was accurate, and 80% (n = 160) stated that they trusted the medical information from the tool. However, 83% (n = 166 students) indicated concerns about privacy and data security. While 69% of participants (n = 138) discovered that ChatGPT supplemented their critical thinking skills, the rest (31%; n = 62) believed it led to decreased autonomy over time. However, since health science-related courses often involve sensitive patient information, 22% (n = 44) of students raised concerns about using ChatGPT in future medical education due to the potential issues with privacy and the risk of inaccuracies in recorded information.
Conclusion:
ChatGPT offers promising educational benefits in medical training but raises significant ethical concerns, particularly regarding data privacy and the potential for over-reliance. The results suggest the need for responsible integration of AI in medical education, ensuring it supplements rather than replaces traditional learning methods.
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