AI Awareness Among Medical & Dental Students

AI in Healthcare Education

Artificial intelligence (AI) is changing healthcare through its ability to plan, learn, understand, and act. AI is being used for diagnosis, drug research, improving communication between doctors and patients, and remote treatment. One exciting application involves improving diagnoses and detecting diseases faster.

The use of AI has grown in dentistry. AI's influence can be seen in dermatology, neurology, psychiatry, and medical research.

Integrating AI raises concerns, including patient privacy risks, algorithm training bias, and cybersecurity threats. Educating future professionals in both technical skills and ethical considerations is necessary for AI to be integrated effectively in clinical settings while keeping a patient-centered approach.

AI Education in the Middle East

Including AI in medical and dental curricula is still limited, especially in the Middle East. Studies show gaps in AI education among healthcare students. While students see AI's potential, most have not had official AI training.

Organized AI education needs to be integrated into healthcare fields. Students have positive attitudes toward AI’s potential but have limited ethical knowledge. Assessing students’ readiness to learn and adopt AI is important before designing educational programs. Programs tailored to students’ needs and career paths can ensure AI is integrated ethically and effectively into future practice.

University of Jordan Study

A study expanded to include medical and dental students at the University of Jordan, exploring differences in AI awareness and understanding across disciplines. It examined ethical concerns, perceived utility, and institutional support that affect how ready students are to use AI in clinical practice. By connecting basic knowledge to adoption drivers, the study aims to inform educational strategies. This cross-sectional study collected data from medical and dental students at the University of Jordan between November 20, 2022, and March 1, 2023.

Of the 820 students invited, 800 participated. The survey covered sociodemographics, technological familiarity, AI understanding, awareness of AI’s role in medicine and dentistry, and attitudes toward AI. A pilot study confirmed the questionnaire’s effectiveness, maintaining internal consistency (Cronbach’s alpha between 0.715 and 0.867).

The sociodemographic details collected included sex, age, field of study, training phase, social status, monthly income, and governorates. The study examined factors such as family involvement in technology, AI courses, programming experience, AI research, AI concerns, and AI applications.

The survey used statements to assess participants’ AI knowledge, medical applications, and uses in daily life. Additional statements measured participants’ awareness regarding AI’s role in medicine and dentistry.
Nine items explored participants’ attitudes toward AI, including opinions about AI as a helper, its impact on healthcare, concerns about job replacement, and support for AI integration in schools.

Study Results

The University of Jordan Institutional Review Board approved this study, which followed ethical guidelines. After collecting data through Google Forms, SPSS version 26 was used for analysis.

The survey was completed by 517 medical students and 283 dental students. Most participants were in the pre-clinical phase. More medical students had taken AI-related courses. AI-related courses and programming experience were positive factors for medical students’ AI awareness and understanding. Programming experience was also a positive factor for dental students’ AI awareness and understanding.

More medical students were concerned about AI decreasing patient communication. More dental students focused on cybersecurity and hacking risks. Both groups agreed that AI could improve diagnoses.

Medical and dental students had similar median attitude scores. Among dental students, AI research experience correlated with a lower attitude score. Spearman correlation analysis showed no significant relationship between attitude scores and awareness or understanding scores.

For medical students, factors such as age, family in technology, AI coursework, and programming experience were linked to higher awareness scores. AI coursework and programming experience were significant positive predictors. There were no significant predictors for awareness among dental students. Awareness and understanding scores were positively correlated among both medical and dental students.

For medical students, age, preclinical training phase, AI coursework, and programming experience were positive predictors of higher understanding scores. Among dental students, those residing in northern governorates had lower understanding scores, while programming experience remained a positive factor.

Conclusions

Medical and dental students at the University of Jordan showed basic AI awareness but lacked in-depth knowledge. More medical students had taken AI-related courses than dental students. Having family in the technology sector and programming courses improved AI understanding. Dental students wanted more AI education, with concerns about cyberattacks. Research experience impacted students’ perceptions of AI.

A gap in ethical understanding exists, especially regarding patient depersonalization and cybersecurity. Including technical and ethical AI instruction is needed, especially in dentistry. Awareness and understanding of AI were more strongly related in medical students. Students with AI research experience were more cautious about AI.

Demographic factors had little impact on students’ attitudes and AI understanding. Dental students in the north had lower comprehension scores due to less access to AI tools. Programming experience was key for increased AI knowledge among dental students. The study highlights how AI courses and research projects shape students’ knowledge.

An integrated approach to AI training should be adopted across healthcare fields. Factors like family background and socioeconomic status affect AI understanding. Ethical training is needed due to cybersecurity concerns. AI education should balance technical skills with ethical challenges.

The study's findings are limited by its cross-sectional design, reliance on self-reported data, and focus on a single institution. Future research should address these limitations to provide a more comprehensive understanding of AI readiness among healthcare students.