INTRODUCTION:

Artificial intelligence (AI) involves designing computer systems to mimic the cognitive functions of the human brain. In recent years, the healthcare industry has seen substantial growth of integrating cutting-edge technologies to enhance patient care and outcomes. AI has become an integral part of this evolution, offering significant advancements¹.

AI and machine learning algorithms encompasses across a range of advanced technologies that are progressively transforming the healthcare sector^2,3. Deep learning (DL) has increasingly found applications in the analysis of medical images, offering substantial improvements in diagnostic accuracy and processing efficiency⁴. Consequently, healthcare has become more dynamic, with real-time monitoring, comprehensive data collection, and tailored insights facilitated by AI-driven applications⁵. The healthcare industry is committed to imposing the latest AI advancements, applying sophisticated algorithms and machine learning methods to improve the diagnostics, customize specific treatment plans, and enhance administrative tasks⁶. As future healthcare professionals it is vital for undergraduate students to understand and engage with AI based technologies⁷. By gaining a deep understanding of how artificial intelligence can enhance diagnosis, improve patient outcomes, and streamline healthcare delivery, these future professionals will be well-equipped to utilize these innovations, ultimately advancing the field of medicine and advancing patient care more efficiently and effectively⁸.

AI can revolutionize mental health care by enabling early detection, personalized treatment, and predictive analytics⁹. Machine learning algorithms are essential for improving efficiency in data retrieval processes¹⁰. Neocortical information processing can be transferred into computers, contributing to neuroscience and psychology research¹¹.

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Regardless of how AI is significantly assisting the healthcare sector there are notable concerns that must be addressed especially regarding data privacy and the potential biases in AI-driven decision-making which could aggravate the existing disparities in patient care¹².

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Unlike, human practitioners, AI systems cannot offer empathy, emotional support, or understand the complex psychological needs of patients, which are essential for building trust and delivering compassionate care¹³. AI tools can significantly benefit patients such as optimization of dosage, facilitating prompt decision making¹⁴, but the challenge lies in anticipating potential misuse and protecting against potential harms¹⁵.

Considering these facts, our study aims to examine the perspectives of undergraduate students in UAE on the future role AI in healthcare sector. By analyzing their attitudes, we hope to gain valuable insights into how emerging healthcare professionals perceive AI’s potential impact in their future work environment and patient interactions.

MATERIALS AND METHODS:

Study Design:

This research employed a quantitative methodology, utilizing 21 closed-ended, faculty-validated questionnaire including both Likert and Dichotomous type questions, to explore the viewpoints of undergraduate students at RAK Medical and Health Sciences University (RAKMHSU) on the application of Artificial Intelligence (AI) in the healthcare sector. The study was carried on uundergraduate students enrolled in RAKMHSU's MD (Doctor of Medicine) or MBBS (Bachelor of Medicine and Bachelor of Surgery), BDS (Bachelor of Dental Surgery), B Pharm (Bachelor of Pharmacy), and BSN (Bachelor of Science in Nursing) programs were among the target population. Our selection of these disciplines was based on their direct relevance to healthcare practice and potential future interaction with artificial intelligence technologies.

Sampling Method:

We selected study participants from the study setting using nonprobability convenience sampling method. This approach comprised of selecting participants based on their availability and desire to take part. With a 95% confidence level, a 5% margin of error, and a 50% expected response rate, the projected sample size from the entire population of 1314 undergraduate students was 298. In the end, 307 replies were obtained, which is in excellent agreement with our research goals.

Data Collection and Analysis:

In order to increase the response rate, the questionnaire was presented to students as tangible printouts. A comprehensive statistical analysis using the IBM SPSS Statistics (Version 29) was performed on the obtained questionnaire data, including frequency and percentage as well as descriptive statistics like mean, mode, median, and standard deviation. The data was coded as 1= Strongly Agree, 2= Agree, 3= Strongly Agree and Disagree, 4= Disagree and 5= Strongly Disagree. In order to determine whether there were significant differences in the responses between two variables the Independent t test was used. The ANOVA test was employed to assess any differences across multiple independent groups. Further, a chi square test was used to compare between categorical variables. A significance level of p<0.05 was applied to establish statistical significance, ensuring robust conclusions regarding Health Science students' perceptions of AI in healthcare.

Ethical Considerations:

The Institutional Research and Ethics Committee approved this study request, with the reference RAKCOMS-REC-01-2023/24-UG.approval

RESULTS:

a. Sociodemographic Characteristics:

A total of 307 respondents were received. Approximately two-thirds of the respondents were female (208, 68%). The age ranged between 17 and 26, with a mean of 20.42. In the undergraduate departments, there were 133 Medicine students (43%), 63 Dentistry students (21%), 62 Pharmacy students (20%), and 49 Nursing students (16%). The distribution between pre-clinical (153, 50%) and clinical (154, 50%) students was almost equal. Participants were asked to disclose their ethnicity, and the majority were from the Arab region (175, 57%).

b. Knowledge about AI:

Data on individuals' knowledge of AI in the healthcare sector were compiled. Out of the 307 respondents, 252 (82%) indicated that they were familiar with AI. Among them, the majority (70%) had good knowledge, 14% had excellent knowledge and 13% had basic knowledge about AI in the healthcare sector. The majority of respondents, 268(87%), believed that AI would play an important role in the healthcare sector. Among them, 114 respondents (43%) mentioned that AI can help improve efficiency and effectiveness, 30% believed it would reduce medical errors, and the remaining 27% thought that AI can decrease work hours by speeding up the process in various procedures involved in health care. Conversely, 39 respondents expressed the opinion that AI does not have a crucial role in healthcare. The majority (54%) strongly believed that AI lacks 100% accuracy, followed by 44% who feared being replaced in their profession by AI.

Knowledge on AI:

The descriptive data analysis (Chi-square test) related to knowledge of AI within the healthcare sector is shown in Table 1. According to the analysis, a significant association between genders (Male Vs female, p= 0.001) and between study streams (Medicine vs Dentistry vs Pharmacy vs Nursing, p= 0.001) were observed regarding familiarity with AI in healthcare. Further, from the data it has been noted that males were more familiar with AI than females (p=0.002). Male and female respondents also had a significant difference in why they think AI might not have an important role in the healthcare sector (p <0.001).

However, data compiled with chi-square test on the various levels of knowledge tested by asking how familiar participants were with AI in the healthcare sector, all the domains, namely gender, study level (Pre clinical Vs clinical), and study streams, showed significant differences (p<0.05).

Interestingly, though 87% of participants believe that AI has an important role in health sector. As far as individual domains (gender, level of study and stream of study) did not show significant differences between the groups. Nevertheless, when tested if AI reduces medical errors or if it is more efficient and effective or if it speeds up the process, the study level (p<0.05) and study streams (p<0.001) showed statistically significant differences in these opinions.

Table 2: Gender wise, study level wise and study stream wise difference in dichotomous data about participants’ perception on AI

Perception parameters

p Value (Male vs Female)

p Value (Pre-Clinical vs Clinical)

p Value (Medicine vs Dentistry vs Pharmacy vs Nursing)

How do you perceive AI having no emotions?

0.54

0.459

<0.001*

Do you feel AI will increase or decrease the financial burden on the healthcare sector?

0.425

0.044*

0.002*

*Data denotes statistically significant by Chi-Square test (p<0.05)

Perception on AI:

A series of dichotomous and Likert-scale questions were asked to determine participants' perceptions of AI. The data obtained from the perception domain are illustrated in horizontal bar charts (Fig. 1 and 2). Most participants perceived AI having no emotions as a drawback (169, 55%), while 138(45%) considered it an advantage. A majority (69%) of medicine sector students perceived it as a drawback, which was a similar perception of dentistry sector students (59%). In contrast, pharmacy and nursing students viewed it as an advantage in their respective health sectors, with the frequency observed to be 60% and 69% respectively.

A statistically significant association on the perception that AI lacks emotions (p<0.001) was noticed by the student group of different study levels only (Table-2).

The study showed that 233 participants said AI would increase the financial burden. From these 233 participants, the majority were studying Medicine (69%). The remaining responses were from Dentistry (50%), Pharmacy (37%) and Nursing (44%) students.

However, a statistically significant observation was evident on the perceptions of the potential impact of AI on financial burden in the health care sector among pre-clinical and clinical students and among the various study streams (p<0.05) irrespective of gender. (Table-2)

Fig. 1: Participants responses to AI having no emotions (n=307)

Fig 2: Participants perception response to AI on financial burden to the healthcare sector (n=307)

The Likert scale responses that 57% of overall participants agreed that AI would help in reducing medical errors, and about 83% believed that AI would speed up processes in the health sector. Similarly, a total of 51% of participants were concerned about the potential threat to their profession posed by AI. Only 47% were worried that their jobs may be replaced by AI. 79% of the students disagreed that AI is flexible enough to be applied to every patient. Regarding legal liabilities of AI, 67% of students responded in favor of legal liabilities. Despite differing opinions among students from diverse health sector backgrounds, 73% of the student participant population advocated for the implementation of AI awareness campaigns at the university level.

Gender Disparities Among Medical Students (Table 3):

The only significant difference (p=0.009) of agreement between the genders on perception of AI was observed regarding AI’s caliber that is potentially enough to accelerate healthcare processes.

Comparisons Between Pre-clinical and Clinical Students (Table-3):

The distinctions (Independent t-test result) between preclinical and clinical students emerged in most of the variables testing the perception of AI's role in the healthcare sector. This includes their perception that AI can reduce medical errors (p<0.05) and AI can also speed up the health care process (p<0.05). However, they are also concerned about AI being a threat to their jobs in the future (p<0.001) and also perceive that AI will increase the legal liabilities in the healthcare sector (p<0.05).

Comparisons Between Different Medical Study Streams (Table-3):

The ANOVA test compared perceptions across different healthcare study streams (Medicine, Dentistry, Pharmacy, and Nursing) regarding various aspects of AI integration in healthcare and showed statistically significant differences of opinions among the tested categories.

This includes positive remarks such as AI can reduce medical errors, speed up the process, and provide flexibility to be applied to every patient, as well as concerns like the introduction of AI may be a threat by replacing jobs, increasing legal liabilities, and causing patients to fear it (p<0.01). Nevertheless, they also agreed to incorporate AI awareness campaigns in medical or health science universities (p<0.05)

DISCUSSION:

AI is transforming healthcare and medical practice by combining computer and physician strengths, improving patient outcomes and making clinical tasks faster and easier, becoming increasingly important in the health care and pharmaceutical sector^16,17. AI-driven systems improve healthcare efficiency by analysing medical data, enhancing diagnostics, and optimizing treatment plans. Research suggests AI's significant impact on patient safety, guiding the development of global healthcare programs¹⁸.

In our study, a significant majority of respondents (82%) reported being familiar with AI in the healthcare sector, with 84% of those categorized as having either "excellent" or "good" knowledge. This high level of familiarity aligns closely with the findings of Mosleh et al. (2023), who reported that as many as 81.3% of medicine and pharmacy students from Palestine and Jordan were aware of AI programs¹⁹. However, this contrasts with research done by Kansal et al. (2022), from India, which indicated that 73.6% of participants felt unfamiliar with the basic principles of AI and its applications in healthcare²⁰.

Our study revealed that 87% of respondents believed AI plays a significant role in the healthcare sector, with many recognizing its potential to improve efficiency and reduce medical errors. This positive perception is consistent with the findings of Kansal et al. (2022), who also noted that the majority (85.4%) felt that AI will play an integral role in delivering healthcare services in the future. Also, Hasan et al reported that majority (96%) agreed on improvement in health and pharmacy services due to AI, whereas a small percentage of participants (18%) revealed to have received training on AI²¹.

Regarding student perceptions on the possible influences of AI in medicine (Fig. 1), in our study most believed it would help be more efficient and effective (43%) and 30% believed it could reduce medical errors. Whereas in the study of Jackson et al. (2024), the highest agreement (72.3%) was observed on it reducing errors²². The study showed that 55% of participants viewed AI’s lack of emotions as a drawback, which mirrors the concerns documented by Montemayor et al. (2021), where the absence of emotional capability was seen as a significant limitation of AI²³. Concerns about AI potentially replacing jobs are widely documented. Some people see the advancement of AI as a threat to their profession²⁴. Kansal et al. (2022) found that most of the respondents (162, 76.4%) believed AI might replace physicians in some specialties in the future, which aligns with our study’s results²⁰. However, in AlZaabi et al. (2023) study, the majority of the participants didn’t agree nor disagree with the questions related to AI replacing medical doctors. Instead, almost half of the responses reported that AI may increase the job market by implementing new opportunities for job placements²⁵. Additionally, the issue of AI increasing legal liabilities in healthcare, noted in the study, is consistent with Cestonaro et al. (2023) who also reported such concerns ²⁶. Lastly, the call for AI awareness campaigns in medical education, as highlighted in our findings, are supported by a study done by Wood et al., in US that emphasized the need for integrating AI education into the medical curriculum to address the knowledge gaps and concerns effectively²⁷.

The analysis indicated a significant association between genders and familiarity with AI, where males demonstrating greater familiarity than females contradicting Jaber Amin et al. (2024) study where females were found to have more knowledge about AI than males with a p value of 0.023²⁸. In contrast Serbaya et al., reported that males health care workers had better knowledge scoring as compared to females²⁹.

The study also found significant differences between pre-clinical and clinical students regarding their perceptions of AI’s potential. Pre-clinical students were more optimistic and aware of AI’s potential in the healthcare sector. This finding is validated by Bisdas et al. (2021), who reported that pre-clinical students often exhibit a more positive outlook towards AI compared to their clinical counterparts. Clinical students, on the other hand, tend to be more cautious possibly due to their practical experience and active involvement in clinical settings, leading them to prioritize existing methods and practices over keeping updates of the latest research advancements³⁰.

When comparing students from different medical streams (Medicine, Dentistry, Pharmacy, Nursing), the study found notable variations in the knowledge of the students. For example, Pharmacy and Nursing students expressed more concerns about AI’s potential to replace jobs, reflecting concerns that align with Hadithy et al. (2023) study who thought that certain medical specialties are more susceptible to job loss due to AI, with a majority agreeing (n=97, 43.9%) or strongly agreeing (n=75, 33.9%)³¹. In the same line Syed et al., reported that 12.8% of community pharmacists agreed on losing jobs due to AI, whereas the vast majority (63.4) felt that AI would facilitates health care workers.³²

Similarly, this perception aligns with the results of a study conducted by Ahmed et al. (2022), where healthcare professionals believe that radiology and pathology will be the medical specialties most affected by AI. This is because these fields involve interpreting medical images, a task that AI can perform quickly and accurately³³.

Our study findings indicated that healthcare students are generally familiar with AI and optimistic about its role in healthcare field, reflecting a growing acceptance and enthusiasm for AI technologies. Participants acknowledged AI's ability to enhance diagnostic accuracy, speed up the healthcare process, and increase efficiency in administrative tasks.

LIMITATIONS:

Limitations and challenges included inappropriate responses from participants and misinterpretation of the questionnaire due to language barriers. Further, the use of convenience sampling could have caused limitations in terms of selection bias and lack of generalizability.

CONCLUSION:

Incorporation of Artificial Intelligence in the medical field is a significant advancement that has the potential to improve patient care and healthcare processes. Although Artificial Intelligence might be more efficient than humans, in certain areas it lacks the emotionn capability that a healthcare professional present. One of the main responsibility of the healthcare professional is to create a safe space for the patients and their families at the hospital.

AUTHOR’S CONTRIBUTION:

Data gathering and idea owner of this study: Anoosha and Drishti.

Study design and data collection: Naveen, Anoosha, Drishti, Suresh, Mohamedasnas approval of final draft: Naveen, Suresh.

Writing assistance and submitting manuscript: Naveen Kumar.

REFERENCES:

1. Simmons A., and Chappell S. Artificial intelligence-definition and practice. IEEE Journal of Oceanic Engineering. 1988; 13(2): 14–42. https://doi.org/10.1109/48.551

2. Smith, J., and Doe A. Overview of artificial intelligence in medicine. Journal of Family Medicine and Primary Care. 2020; 9(4): 123-130. https://doi.org/10.4103/jfmpc.jfmpc_440_19

3. Park C. J., Yi P. H., and Siegel E. L. Medical Student Perspectives on the impact of artificial intelligence on the practice of medicine. Current Problems in Diagnostic Radiology. 2021; 50(5): 614–619. https://doi.org/10.1067/j.cpradiol.2020.06.011

4. Chan HP, Samala RK, Hadjiiski LM, Zhou C. Deep learning in medical image analysis. Advances in Experimental Medicine and Biology. 2020: 3-21. doi:10.1007/978-3-030-33128-3_1

5. Ejaz H., McGrath H., Wong B. L., Guise A., Vercauteren T., and Shapey J. Artificial intelligence and medical education: A global mixed-methods study of medical students’ perspectives. Digital Health. 2022; 8: 205520762210890. https://doi.org/10.1177/20552076221089099

6. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: the role of artificial intelligence in clinical practice. BMC Medical Education. 2023; 23(1): doi:10.1186/s12909-023-04698-z

7. Gordon M, Daniel M, Ajiboye A, et al. A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher. 2024; 46(4): 446-470. doi:10.1080/0142159x.2024.2314198

8. Karalis VD. The Integration of Artificial Intelligence into Clinical Practice. Applied Biosciences. 2024; 3(1): 14-44. doi:10.3390/applbiosci3010002

9. Samruddhi Nelson Chauhan, Ashwini K Vaidya. Artificial Intelligence in Mental Health: Challenges and Opportunities. International Journal of Nursing Education and Research. 2024; 14(4): 274-6

10. R. M. Balajee, K. Venkatesh. A Survey on Machine Learning Algorithms and finding the best out there for the considered seven Medical Data Sets Scenario. Research J. Pharm. and Tech. 2019; 12(6): 3059-3062

11. RNSK Kartheek, V Rama Raju, R Md. Shafi. Blue Brain – A New Subway to Artificial Intelligence and Human Machine: A Proposal. Int. J. Tech. 2014; 4(2): 287-290

12. Ueda D, Kakinuma T, Fujita S, et al. Fairness of artificial intelligence in healthcare: review and recommendations. Japanese Journal of Radiology. 2023; 42(1): 3-15. doi:10.1007/s11604-023-01474-3

13. Mohammed K Al-Medfa, Ahmed M.S. Al-Ansari Abdullah H D, Tasabeeh A Q, Haitham J. Physicians’ attitudes and knowledge toward artificial intelligence in medicine: Benefits and Drawbacks. Heliyon. 2023; 9(4): e14744. https://doi.org/10.1016/j.heliyon.2023.e14744

14. Vaidehi Sunil Holey, Ajay W. Baitule. A Wide Application of Artificial Intelligence in Pharma Field. Asian Journal of Pharmaceutical Research. 2024; 14(4): 403-0. doi: 10.52711/2231-5691.2024.00064

15. Ajay I. Patel, Pooja K. Khunti, Amit J. Vyas, Ashok B. Patel. Explicating Artificial Intelligence: Applications in Medicine and Pharmacy. Asian Journal of Pharmacy and Technology. 12(4): 401-6. doi: 10.52711/2231-5713.2022.00061

16. Lakshmidevi Sigatapu, S. Sundar, K. Padmalatha, Sravya. K, D. Ooha, P. Uha Devi. Artificial Intelligence in Healthcare- An Overview. Asian Journal of Pharmacy and Technology. 2023; 13(3): 218-2. doi: 10.52711/2231-5713.2023.00039

17. Shraddha Jain, Sanket Jain, Dr. Sujit Pillai, Rampal Singh Mandloi. Review article on Role of Artificial Intelligence in Radiology. Research Journal of Pharmacognosy and Phytochemistry. 2023; 15(3): 264-0. doi: 10.52711/0975-4385.2023.00041

18. Randa Khirfan, Heba Kotb, Huda Atiyeh. Utilizing Artificial Intelligence to Improve Patient Safety: Innovations, Obstacles, and Future Paths. Research Journal of Pharmacy and Technology. 2024; 17(9): 4630-6

19. Mosleh R, Jarrar Q, Jarrar Y, Tazkarji M, Hawash M. Medicine and Pharmacy Students’ Knowledge, Attitudes, and Practice regarding Artificial Intelligence Programs: Jordan and West Bank of Palestine. Advances in Medical Education and Practice. 2023; 14: 1391-1400. doi:10.2147/amep.s433255

20. Kansal R, Bawa A, Bansal A, et al. Differences in Knowledge and Perspectives on the Usage of Artificial Intelligence Among Doctors and Medical Students of a Developing Country: A Cross-Sectional Study. Cureus. 2022; 14(1): e21434. Published 2022 Jan 19. doi:10.7759/cureus.21434

21. Hasan HE, Jaber D, Al Tabbah S, Lawand N, Habib HA, Farahat NM. Knowledge, attitude and practice among pharmacy students and faculty members towards artificial intelligence in pharmacy practice: A multinational cross-sectional study. PLoS One. 2024; 19(3): e0296884. doi:10.1371/journal.pone.0296884

22. Jackson P, Ponath Sukumaran G, Babu C, et al. Artificial intelligence in medical education - perception among medical students. BMC Med Educ. 2024; 24(1): 804. doi:10.1186/s12909-024-05760-0

23. Montemayor C, Halpern J, Fairweather A. In principle obstacles for empathic AI: why we can't replace human empathy in healthcare. AI Soc. 2022; 37(4): 1353-1359. doi:10.1007/s00146-021-01230-z

24. O’Neill C. Is AI a threat or benefit to health workers?. Canadian Medical Association Journal. 2017; 189(20): E732. doi:10.1503/cmaj.1095428

25. AlZaabi A, AlMaskari S, AalAbdulsalam A. Are physicians and medical students ready for artificial intelligence applications in healthcare?. Digit Health. 2023; 9: 20552076231152167. doi:10.1177/20552076231152167

26. Cestonaro C, Delicati A, Marcante B, Caenazzo L, Tozzo P. Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Front Med (Lausanne). 2023; 10:1305756. doi:10.3389/fmed.2023.1305756

27. Wood EA, Ange BL, Miller DD. Are We Ready to Integrate Artificial Intelligence Literacy into Medical School Curriculum: Students and Faculty Survey. J Med Educ Curric Dev. 2021; 8: 23821205211024078. doi:10.1177/23821205211024078

28. Jaber Amin MH, Mohamed Elhassan Elmahi MA, Abdelmonim GA, et al. Knowledge, attitude, and practice of artificial intelligence among medical students in Sudan: a cross-sectional study. Ann Med Surg. 2024; 86(7): 3917-3923. doi:10.1097/MS9.0000000000002070

29. Serbaya SH, Khan AA, Surbaya SH, Alzahrani SM. Knowledge, Attitude and Practice Toward Artificial Intelligence Among Healthcare Workers in Private Polyclinics in Jeddah, Saudi Arabia. Adv Med Educ Pract. 2024; 15: 269-280. doi:10.2147/AMEP.S448422

30. Bisdas S, Topriceanu CC, Zakrzewska Z, et al. Artificial Intelligence in Medicine: A Multinational Multi-Center Survey on the Medical and Dental Students’ Perception. Frontiers in Public Health. 2021;9. doi:10.3389/fpubh.2021.795284

31. Al Hadithy ZA, Al Lawati A, Al-Zadjali R, Al Sinawi H. Knowledge, Attitudes, and Perceptions of Artificial Intelligence in Healthcare Among Medical Students at Sultan Qaboos University. Cureus. 2023; 15(9): e44887. doi:10.7759/cureus.44887

32. Syed W, Al-Rawi MBA. Community pharmacists awareness, perceptions, and opinions of artificial intelligence: A cross-sectional study in Riyadh, Saudi Arabia. Technol Health Care. 2024; 32(1): 481-493. doi:10.3233/THC-230784

33. Ahmed Z, Bhinder KK, Tariq A, et al. Knowledge, attitude, and practice of artificial intelligence among doctors and medical students in Pakistan: A cross-sectional online survey. Ann Med Surg. 2022; 76: 103493. doi:10.1016/j.amsu.2022.103493

Received on 25.02.2025 Revised on 11.06.2025

Accepted on 14.08.2025 Published on 01.12.2025

Available online from December 06, 2025

Research J. Pharmacy and Technology. 2025;18(12):5928-5934.

DOI: 10.52711/0974-360X.2025.00857

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

Knowledge parameters	p value (Male vs Female)	p value (Pre-Clinical vs Clinical)	p value (Medicine vs Dentistry vs Pharmacy vs Nursing)
Are you familiar with AI in the healthcare sector ?	0.001*	0.31	0.001*
How familiar are you with AI in the health care sector?	0.002*	0.001*	<0.001*
Do you feel AI has an important role in the healthcare sector ?	0.602	0.118	0.101
If yes, how do you think AI has an Important role in the health care sector?	0.915	0.022*	0.001*
If no, why do you think AI does not have an important role in the health care sector?	<0.001*	0.352	<0.001*

	Mean ±SD	p value (Male vs Female)^a	p value (Pre-Clinical vs Clinical)^a	P value (Medicine vs Dentistry vs Pharmacy vs Nursing)^b
Do you think AI can help reduce medical errors?	2.38 ±0.89	0.17	0.031	<0.001
Should AI awareness campaigns be done in medical universities?	2.06 ±0.74	0.643	0.166	0.005
Can AI speed up the process in health care ?	1.91 ±0.71	0.009	0.03	<0.001
Do you feel the increased use of AI is a threat to your profession?	2.41 ±0.95	0.929	0.263	<0.001
In the future do you think AI can replace your job?	2.85 ±1.21	0.625	<0.001	<0.001
Do you think AI is flexible enough to be applied to every patient?	2.93 ±0.98	0.14	0.062	<0.001
Do you believe AI will increase legal liabilities in the health care sector?	2.23 ±0.83	0.895	0.002	<0.001
Do you believe patients would fear AI?	2.13 ±0.90	0.491	0.078	<0.001