Abstract
Background and Aims
This systematic review examined healthcare students' attitudes, knowledge, and skill in Artificial Intelligence (AI).
Methods
On August 3, 2022, studies were retrieved from the PubMed, Embase, Scopus, and Web of Science databases. Preferred Reporting Items for Systematic Reviews and Meta‐Analyses recommendations were followed. We included cross‐sectional studies that examined healthcare students' knowledge, attitudes, skills, and perceptions of AI in this review. Using the eligibility requirements as a guide, titles and abstracts were screened. Complete texts were then retrieved and independently reviewed per the eligibility requirements. To collect data, a standardized form was used.
Results
Of the 38 included studies, 29 (76%) of healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in he future; however, in nine of the studies (24%), students considered AI a threat to healthcare fields and had a negative attitude towards it. Furthermore, 26 studies evaluated the knowledge of healthcare students about AI. Among these, 18 studies evaluated the level of student knowledge as low (50%). On the other hand, in six of the studies, students' high knowledge of AI was reported, and two of the studies reported average student general knowledge (almost 50%). Of the six studies, four (67%) of the students had very low skills, so they stated that they had never worked with AI.
Conclusion
Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. Face‐to‐face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works and raising students' knowledge of the advantages of AI.
Keywords: artificial intelligence, attitude, healthcare student, knowledge, medical student
1. INTRODUCTION
The main objective of artificial intelligence (AI) is to create intelligent machines that can comprehend calculations and carry out tasks that require human intelligence, such as accurate visual perception, speech recognition, timely decision‐making, and translation between different languages. Our daily lives now use AI to an exponentially greater extent. Therefore, AI is no longer a futuristic idea. AI is being incorporated into healthcare more and more. 1 , 2
The health industry is looking into two subsets of AI called machine learning (ML) and deep learning (DL). 3 The largest use of AI algorithms has been in radiology. But there are also examples of its use in other disciplines, including dermatology, ophthalmology, psychiatry, cardiology, oncology, neuroscience, pathology, and medicine. 4 , 5 , 6 , 7 , 8 , 9 A type of AI that uses a layered algorithmic architecture for data analysis is referred to as “DL,” a subfield of AI. 10 DL offers a variety of applications that help spot intricate yet subtle patterns in images. 11 Such a skill can be applied to image‐based automatic diagnosis in model‐oriented healthcare fields, such as pathology, dermatology, and radiology. 12 , 13
With AI's continued development, its applications will expand beyond image classification to include signal processing in cardiology and natural language processing in psychiatry. 14 , 15 Future AI‐assisted systems are anticipated to perform specific tasks like test referrals and patient screening, in addition to making recommendations for potential clinical actions and becoming more autonomous. 16
Shortly, it can be expected that physicians will encounter patients in very different healthcare settings compared with the present, and thus, restorative education will need to advance. 17 Evidence suggests that medical students are not afraid or concerned about being replaced by AI. 18 However, in Gong et al.'s study, it was stated that anxiety caused by displacement discouraged many medical students from pursuing the radiology specialty. 19 Positive and negative perspectives exist regarding how AI will change our daily lives. According to the pessimistic view of AI, humans will be replaced by AI in many fields. Additionally, according to upbeat perspectives, those who support AI will have more opportunities to benefit from future developments. 20 The recent increase in interest in teaching AI to medical students reflects how frequently AI applications are being used in clinical care, research, and education. The Association of American Medical Colleges, the Royal College of Physicians and Surgeons of Canada, and others have suggested that healthcare professionals receive training in AI, data sourcing and protection, AI ethics, and the critical evaluation and interpretation of AI applications in health. 14 , 21 , 22
Limited exposure to AI has been shown to cause anxiety in undergraduate medical students and influence their future career decisions 19 ; thus, examining public attitudes and current knowledge of healthcare students may be a powerful approach to highlight areas of need for curriculum decision‐makers regarding AI education. 23 As the roles of various healthcare providers in modern medicine are reexamined, the incorporation of AI necessitates the interdisciplinary collaboration of healthcare stakeholders, including physicians and other healthcare professionals. It is critical to collect information from a diverse group of healthcare students. 14 As the field of AI in healthcare gains traction, it is becoming clear that greater knowledge, as well as AI‐based training for physicians and medical students, is required.
Despite the growing contribution of AI to healthcare and the research on healthcare students' attitudes towards AI, few systematic reviews integrate the best evidence and provide overviews in this domain. Hence, this systematic review examined healthcare students' attitudes, knowledge, and skill in AI.
2. METHODS
2.1. Study design
Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) were used in this study to report the evidence obtained from the studies included in this systematic review. 24 Appendix A contains the PRISMA checklist. This study was approved by the ethical committee of Mashhad University of Medical Sciences (approval number IR.MUMS FHMPM.REC.1401.081).
2.2. Strategic search
On August 3, 2022, we conducted a literature search using the databases PubMed, Embase, Scopus, and Web of Science. Appendix B shows the search strategy for each database. The below Medical Subject Headings and Emtree keywords and terms were used to search the databases: (“artificial intelligence” or “deep learning” or “machine learning” or “machine intelligence” or “neural net” or “computational intelligence” or “intelligence, artificial” or “machine intelligence” or “computer reasoning” or “AI”) and (“trainee” or “student” or “resident” or “students, medical” or “medical students” or “student, medical” or “medical student” or “medical science student”) and (attitude or “sentiment” or “sentiments” or “opinions” or “opinion “or “perception” or “knowledge” or “awareness” or “skill”). The search strategy for each database is shown in Appendix B.
2.3. Eligibility criteria
The inclusion criteria for studies in this review were cross‐sectional studies that examined healthcare students' attitudes, knowledge, or skills related to AI. In contrast, articles whose target population was students other than healthcare students, the full text of which was not available, and those written in a language other than English, were excluded.
2.4. Data extraction and synthesis
Duplicate documents were removed from this review, and all documents were gathered from the systematic search. Independent screening was done on titles and abstracts according to eligibility standards. The review did not include any articles that did not adhere to the inclusion requirements. The complete texts were then retrieved and evaluated based on eligibility requirements by two different researchers. Discussions were held to settle disagreements, and in the event of a tie, the third author provided the decision. For data extraction, the same standard checklist was applied. Study reference, study country, publication year, participant characteristics (number of participants and field of study of the students), study objectives, attitude towards AI, knowledge of AI, skills of AI, and key study findings are among the data items included in this form.
2.5. Quality assessment
The Joanna Briggs Institute (JBI) critical appraisal checklist for analytical cross‐sectional studies was utilized to evaluate the caliber of the studies submitted for this review. 25 The research specifically collected 8 questions to assess the caliber of these studies. The subjects of the questions in this checklist included the following: sample inclusion criteria; study subjects; the setting; valid and reliable measurement tools (mentioning the validity and reliability of questionnaires); standard measurement criteria; identification of confounding factors; strategies to deal with confounding factors; valid measured outcomes, and statistical analysis was appropriate. Answers to the questions were divided into four categories: yes, no, unclear, and not applicable. The response to a question was either marked as 1 or 0, depending on whether it was yes or no, unclear, or not applicable. As a result, each included study could only receive a quality score of 8, and the exclusion only applies if the study's quality score is below 5.
3. RESULTS
3.1. Study selection
Figure 1 shows the 12,276 documents that were found after searching the databases. The titles and abstracts of 10,979 studies were examined after duplicates (1297) were eliminated. A total of 10,937 studies were excluded after the titles and abstracts of the studies were reviewed for relevance to the study's objectives. Fifty articles were then chosen for full‐text evaluation. Except for cross‐sectional studies, all of these studies' subjects were not healthcare students. Ultimately, 38 studies that satisfied the inclusion requirements were included in this review.
Figure 1.
Flowchart of the study selection.
3.2. Quality assessment
All high‐quality studies were incorporated into our study according to the quality assessment results shown in Table 1, which demonstrate that there was no significant bias in the studies. One of the most common issues in these studies was the failure to identify confounding factors and develop strategies to address them.
Table 1.
Summary of the quality assessment of articles using the JBI critical appraisal checklist.
| Source | Questions | ||||||||
|---|---|---|---|---|---|---|---|---|---|
| Q 1 | Q 2 | Q 3 | Q 4 | Q 5 | Q 6 | Q 7 | Q 8 | Score | |
| Ahmed, 2022, Pakistan 24 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Al Saad, 2022, Jordan 25 | Y | Y | Y | N | Y | Y | Y | Y | 7 |
| AlAhmari, 2022, Saudi Arabia 26 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Alomary, 2021, Saudi Arabia 27 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Abouzeid, 2021, Saudi Arabia 28 | Y | Y | Y | N | Y | Y | Y | Y | 7 |
| Bisdas, 2021, 63 countries around the world 29 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Blease, 2022, Ireland 30 | N | Y | N | N | Y | N | Y | Y | 5 |
| Boillat, 2022, United Arab Emirates 31 | Y | Y | N | N | Y | Y | Y | Y | 5 |
| Brandes, 2020, Brazil 32 | N | Y | Y | N | Y | N | Y | Y | 5 |
| Banerjee, 2021, the UK 33 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Bin Dahmash, 2020, Saudi Arabia 34 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Pinto Dos Santos, 2019, Germany 18 | Y | Y | N | N | Y | Y | N | Y | 5 |
| Ejaz, 2022, the UK 35 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Gong, 2019, Canada 19 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Jussupow, 2022, Germany 36 | Y | Y | N | N | Y | Y | N | Y | 6 |
| Kansal, 2022, India 37 | Y | Y | N | N | Y | Y | N | Y | 5 |
| Kasetti, 2020, Britain 38 | Y | Y | N | N | Y | Y | N | Y | 5 |
| Khanagar, 2021, Saudi Arabia 39 | Y | Y | Y | N | Y | Y | Y | Y | 7 |
| Khafaji, 2022, Saudi Arabia 40 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Ooi, 2021, Singapore 41 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Pauwels, 2021, Brazil 42 | Y | Y | Y | N | Y | Y | Y | Y | 7 |
| Qurashi, 2021, Saudi Arabia 43 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Rainey, 2021, the UK 44 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Scheetz, 2021, Australia and New Zealand 45 | Y | Y | Y | N | Y | Y | N | Y | 6 |
| Santos, 2021, 94 countries around the world 46 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Sit, 2020, the UK 47 | Y | Y | Y | N | Y | Y | N | Y | 6 |
| Teng, 2022, Canada 14 | Y | Y | N | N | Y | Y | Y | N | 5 |
| Wood, 2021, the United States 48 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Yurdaisik, 2021, Turkey 49 | N | Y | Y | N | Y | N | Y | Y | 6 |
| Yüzbaşıoğlu, 2021, Turkey 50 | Y | Y | N | N | Y | Y | Y | Y | 6 |
| Reeder, 2022, the United States 51 | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Park, 2021, the United States 52 | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Tran, 2021, Vietnam 53 | Y | Y | Y | Y | Y | Y | Y | Y | 8 |
| Oh, 2019, South Korea 54 | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| van Hoek, 2019, Switzerland 55 | Y | Y | Y | Y | Y | N | Y | Y | 7 |
| Dumić‐Čule, 2020, Croatia 56 | Y | Y | Y | Y | N | N | Y | Y | 6 |
| Auloge, 2020, Europe 57 | Y | Y | Y | Y | N | N | Y | Y | 6 |
| Blacketer, 2021, Australia, New Zealand, and the United States 58 | Y | Y | Y | Y | N | N | Y | Y | 6 |
Abbreviations: JBI, Joanna Briggs Institute; Q, question, N, no; NA, not/applicable; U, unclear; Y, yes, Score: the quality assessment score ranged from 0 to 8 based on each question of the JBI checklist.
3.3. Study characteristics
Tables 2 and 3 list the characteristics of all the included studies. Seven (18%) of the 38 included studies were carried out in Saudi Arabia, 28 , 29 , 30 , 36 , 41 , 42 , 45 4 (11%) were carried out in the UK, 35 , 37 , 46 , 49 3 (8%) were carried out in the United States, 50 , 53 , 59 2 (5%) was carried out in Germany, 18 , 38 2 (5%) was carried out in Turkey, 51 , 52 2 (5%) was carried out in Australia and New Zealand, 47 , 59 2 (5%) was carried out in Canada, 14 , 19 and other studies were carried out in Pakistan, 26 Jordan, 27 Ireland, 32 United Arab Emirates, 33 India, 39 Singapore, 43 Brazil, 44 Vietnam, 55 South Korea, 56 Switzerland, 57 and Croatia. 58 Also, three studies were widely conducted in several countries around the world. 31 , 48 , 60 The study field for healthcare students included six fields: medicine, 19 , 26 , 27 , 29 , 31 , 32 , 33 , 35 , 36 , 37 , 38 , 39 , 40 , 48 , 49 , 50 , 53 , 54 , 55 , 56 , 59 , 60 dentistry, 28 , 30 , 31 , 41 , 44 , 52 radiology, 34 , 42 , 43 , 45 , 46 , 51 , 57 , 58 medical physics, 48 ophthalmology and dermatology, 47 and others. 14 The studies' total sample size was 19,378 participants, with sample sizes ranging from 101 34 to 3312 31 participants. According to “World Economic Situation and Prospects” by Belsey (2022), 60 22 included studies were conducted in high‐income countries (HICs) 14 , 18 , 19 , 32 , 33 , 34 , 35 , 37 , 38 , 40 , 43 , 44 , 46 , 47 , 49 , 50 , 53 , 54 , 56 , 58 , 59 , 60 and 14 studies were conducted in low‐ and middle‐income countries (LMICs). 26 , 27 , 28 , 29 , 30 , 36 , 39 , 41 , 42 , 45 , 51 , 52 , 55 , 57
Table 2.
Characteristics of participants in all included studies.
| Source (first author, year, country) | Number of participants | Type of research instrument | Study participant | Participant characteristics |
|---|---|---|---|---|
| Ahmed, 2022, Pakistan 24 | 470 | Online questionnaire | Doctors and medical students | 239 (50.9%) men and 231 (49.1%) female; 223 (47.45%) doctors and 247 (52.55%) medical students; 223 (47.4%) graduate/postgraduate and 247 (52.6%) undergraduate |
| Al Saad, 2022, Jordan 25 | 900 | Online questionnaire | Medical students | 900 medical students, a mean age of 21.34 years ± 2.43; 31.8% (n = 286) from the sixth year, 7.8% (n = 70) from the fifth year, and the rest were equally distributed between the remaining studying years |
| AlAhmari, 2022, Saudi Arabia 26 | 218 | Online questionnaire | Dental students | 116 (53.2) men and 102 (46.8) female; the grade of dental students was 41 (18.8) first, 46 (21.1) second, 43 (19.7) third, 44 (20.2) fourth, and 44(20.2) fifth |
| Alomary, 2021, Saudi Arabia 27 | 538 | e‐Questionnaire | Medical students | 380 (70.6%) males and 158 (29.4%) females; 12.6% aged 18–20 years, 40.7% 21–23 years, and 44.6% aged 24–26 years; 13.1% Interns, (48.3%) students in the fifth year of study; 27.1% in the preclinical years (first, second, and third years) and 72.8% clinical years (fourth year, fifth years, and interns) |
| Abouzeid, 2021, Saudi Arabia 28 | 570 | Online questionnaire | Dentists (dental students, dental school graduates/interns, dentists) | 313 (54.6%) males and 257 (45.4%) females; 58.8% dental students, 18.2% dentist school graduates/interns, and 23.0% postgraduate dentists |
| Bisdas, 2021, 63 countries around the world 29 | 3133 | Online questionnaire | Medical and dental students | Mean age 21.95 ± 2.77 years; 2083 (66.5%) females, 1050 (33.51) males; 26.43% in developed countries; 79.63% medical students and 20.37% dental students |
| Blease, 2022, Ireland 30 | 252 | Online survey | Medical students | 252 (100%) final year medical students; 157 (62.6%) female and 95 (37.4%) males; 223 of 246 (90.7%) were born in 1992 or later |
| Boillat, 2022, United Arab Emirates 31 | 207 | Online questionnaire | Medical students | 19 (9.17%) <20 years, 100 (48.30%) between 20 and 29 years, 28 (13.52%) between 30 and 39 years, 26 (24.8%) 40–49, 26 (24.8%) 59–50 and 8 (7.6%) 69–60; 105 (50.7%) physicians holding a medical degree and 102 (49.3%) MSs; 105 (50.72) men, and 102 (49.27%) females |
| Brandes, 2020, Brazil 32 | 101 | Online questionnaire | Radiology students | 60% in the sixth year, 17% in the fifth year, and 23% in the fourth year |
| Banerjee, 2021, the UK 33 | 210 | Online questionnaire | Postgraduate trainee doctors | 210 (100%) postgraduate trainee doctors; 47% female, 53% males |
| Bin Dahmash, 2020, Saudi Arabia 34 | 476 | Questionnaire | Medical students | 476 (100%) medical students in clinical years; 60.5% males and 39.5% females |
| Pinto Dos Santos, 2019, Germany 18 | 263 | Online questionnaire | Medical students | 263 undergraduate students; 166 (63.1%) female, 94 (36.9%) male; median age 23 years |
| Ejaz, 2022, the UK 35 | 128 | Online questionnaire | Medical students | 47 medical students from LMICs and 81 from HICs; 52 (41%) males, 72 (56%) females (56%), and 4 (3%) nonbinary |
| Gong, 2019, Canada 19 | 322 | Online survey | Medical students | 21.7% ranked radiology as the first specialty choice, 9% as the second choice, 10.6% as the third choice |
| Jussupow, 2022, Germany 36 | 206 | Web‐based survey | Medical students and doctors in different specialties | Mean age 27.689 ± 8.896 years; 164 (79.61%) medical students and 42 (20.38%) experienced physicians |
| Kansal, 2022, India 37 | 367 | Postevent online questionnaire | Doctors and medical students | 40.6% female of medical students, 41.9% female of doctors; 34.9% third‐year medical students |
| Kasetti, 2020, Britain 38 | 100 | Online survey | Medical students | Not reported |
| Khanagar, 2021, Saudi Arabia 39 | 423 | Online questionnaire | Dental students | Dental students in the first year 6.9% (n = 29), 9.7% (n = 41) second year, 18.4% (n = 78) third year, 13.2% (n = 56) fourth year, 25.1% (n = 106) fifth year, and 26.7% (n = 113) sixth year; 208 (49.2%) female and 215 (50.8%) male |
| Khafaji, 2022, Saudi Arabia 40 | 154 | Online survey | Radiology resident | 85 (55.2%) males and 69 (44.8%) females; 48.7% from the central region; 40 (25.9%) residents were in the first year of training, 34 (22.1%) were in the second year, 52 (33.8%) were in the third year, and 28 (18.2%) were in the fourth year |
| Ooi, 2021, Singapore 41 | 125 | Web‐based questionnaire | Radiology resident | 86 (68.8%) male, 39 (31.2%) female; 70 (56.0%) residents, 55 (44.0%) faculty radiologists; 78 (62.4%) 25–35, 31 (24.8%) 36–45, 16 (12.8) >45 years |
| Pauwels, 2021, Brazil 42 | 293 | Paper questionnaire after the lecture | Dentists and dental students | (57.0%) undergraduate students in dentistry, (20.2%) postgraduate/Ph.D. students, and (14.7%) professors; Mean age of the undergraduate students 22.6 ± 4.1 years and others 34.3 ± 11.9 years; 199 (69%) female and 89 (30.4%) males |
| Qurashi, 2021, Saudi Arabia 43 | 224 | Online survey | Radiography students and radiologists | 75.9% aged <34 years; 38.4% female; 53.6% radiographers, 20.5% internship radiography students; 94.6% bachelor's degree or higher |
| Rainey, 2021, the UK 44 | 411 | Online survey | All UK radiographers including students and retired freelance radiographers | 24.2% males and 74.9% females; 273 (66.4%) practising diagnostic radiography, 59 (14.4%) diagnostic radiography students, 66 (16.1%) practising therapeutic radiography, and 11 (2.7%) therapeutic radiography students; 81 (19.7%) 18–25, 113 (27.4%) 26–35, 108 (26.2%) 36–45, 55 (13.3%) 46–55, 42 (10.2%) 56–65, and 4 (0.9%) >65 years old |
| Scheetz, 2021, Australia and New Zealand 45 | 632 | Online survey | Students and interns of three specialized colleges (ophthalmology, radiology/radiation oncology, dermatology) | 20.4% of Royal Australian and New Zealand College of Radiologists (RANZCO), 5.1% of RANZCO and 13.2% of Australasian College of Dermatologists (ACD); 72.8% in metropolitan areas; 47.9% in practice for 20 years or more |
| Santos, 2021, 94 countries around the world 46 | 1019 | Online survey | Specialists, trainees, and students in the field of medical physics | 35% females and 65% males; 59% 24–39‐year old; 28% 40–56 and 11% >56‐year old age range; 91% held one or more postgraduate degrees, including 34% who held a Ph.D. degree; 5% being board‐certified; (79%) currently working as academic and/or clinical physicists, while 10% carried multiple roles; (11%) undertaking a postgraduate course |
| Sit, 2020, the UK 47 | 484 | Multicenter online survey | Medical students | Not reported |
| Teng, 2022, Canada 14 | 2167 | Web‐based survey | Students in 10 different health professions | 56.16% aged 21–25 years; 62.53% female; 31.52% from medical doctorate program, 23.72% from nursing program; 53.53% bachelor's degree |
| Wood, 2021, the United States 48 | 161 | Online survey | Medical students and faculty members | Students: 52% aged ≤24 years; 45% female; 30% first‐year, 29% second‐year |
| Yurdaisik, 2021, Turkey 49 | 204 | Online survey | Doctors, assistants, and technicians working in the radiology department and medical students | 81.8% aged 18–39 years; 59.8% female; 22.1% radiologists, 27.5% residents, 31.9% medical faculty students |
| Yüzbaşıoğlu, 2021, Turkey 50 | 1103 | Online survey | Dental students | 650 females and 453 males; mean age was 21.36 ± 1.93 years; 304 (27.6%) first grade, 407 (36.90%) second grade, 161 (14.60%) third grade, 134 (12.10%) were fourth grade, and 97 (8.80%) fifth‐grade dental students |
| Reeder, 2022, the United States 51 | 463 | Online survey | Medical school students | 43.2% female; 64.6% in the first and second year; 20.5% ranking radiology as fourth or lower choice; 22.5% and 29.2% interested in diagnostic and interventional radiology, respectively |
| Park, 2021, the United States 52 | 156 | Online survey | Medical students | 25.8% in the first year of medical school, 27.1% in the second year |
| Tran, 2021, Vietnam 53 | 211 | Online survey | Medical students | Mean age 20.6 years (SD 1.5); 73.5% female; 89.1% in urban areas; 59.7% in Ho Chi Minh city; 57.8% general physicians |
| Oh, 2019, South Korea 54 | 669 | Online survey | Medical students and graduates | 22.4% aged <30 years; 22.1% female; 121 medical students, 162 training physicians, and 386 physicians |
| van Hoek, 2019, Switzerland 55 | 170 | Online survey | Radiologists, medical students, and surgeons | 40% female; 59 radiologists, 56 surgeons, and 55 students |
| Dumić‐Čule, 2020, Croatia 56 | 144 | Anonymous electronic survey | Radiologists and radiology residents | 90 (62.5%) radiologists, 54 (37.5%) radiology residents |
| Auloge, 2020, Europe 57 | 1459 | e‐Questionnaire | Medical students | 713 (15.3%) students from Strasbourg (64.7% female, 35.3% male), 525 from Nancy (65% female, 35% male), and 221 from Louvain (62.4% female, 37.6% male); 26% students in the first year, 11% in the second year, 13% in the third year, 14% in the fourth year, 16% in the fifth year and 20% in the sixth year |
| Blacketer, 2021, Australia, New Zealand, and the United States 58 | 245 | Multicenter international survey | Medical students and doctors | Not reported |
Abbreviations: HIC, high‐income country; LMIC, low‐ and middle‐income countries.
Table 3.
Summary of study characteristics from all included studies.
| Source (first author, year, country) | Study goal | Attitude towards AI | Knowledge of AI | Skill of AI | Results |
|---|---|---|---|---|---|
| Ahmed, 2022, Pakistan 24 | Determining knowledge, attitude, and practice of AI among doctors and medical students in Pakistan | One hundred twenty‐nine participants (27.4%) strongly agree, and 221 people (47%) agree that AI is necessary for the field of medicine, while only 5 people (1.1%) strongly disagree and only 10 participants (2.1%) disagree | Men have more knowledge about AI than women. Three hundred thirty‐five people (71.28%) had a basic concept of AI, but only 166 people (35.3%) knew about ML and DL, and only 109 people (23.2%) knew about its applications | Only 53 (11.3%), including 20 medical students and 33 doctors, had applied to AI | Most doctors and medical students do not have enough knowledge about AI and its applications, but they have a positive view of it in the medical field and are willing to accept it |
| Al Saad, 2022, Jordan 25 | Estimating the level of knowledge about AI and DL among medical students in Jordanian universities | Most participants (77.4%) believed that AI plays an important role in healthcare | In the last 5 years, most of them never participated in any course (78.4%) | Not mentioned | Medical students appreciate the importance of AI and ML in medical advancements |
| AlAhmari, 2022, Saudi Arabia 26 | Investigating the views of Saudi Arabian dental students on the impact of AI in dentistry | 74% of participants agreed that AI would lead to major advances in dentistry, but 64% disagreed that AI could replace them in the future | 22% had basic knowledge about AI technologies, and almost 37% knew about the application of AI in dentistry | Not mentioned | Most dental students are enthusiastic about the application of AI in dentistry and believe that AI can be effectively used for disease diagnosis |
| Alomary, 2021, Saudi Arabia 27 | Determining medical students' understanding of ML in otolaryngology, head and neck surgery, and its applications in diagnosis and management | One‐third of the respondents believed that using ML in otolaryngology is important due to the anatomical complexity (35.1%) | Most of the students, in general, 308 (57.3%), were familiar with machine learning | Not mentioned | In general, students had a good knowledge of ML, although many were not familiar with the applications of machine learning in this field |
| Abouzeid, 2021, Saudi Arabia 28 | Assessing dentists' (dental students, dental school graduates/interns, and postgraduate dentists) knowledge, attitudes, and understanding of the role of robotics (R) and AI in dental health | Participants agreed that R and AI in dentistry are useful and provide better results | Most dentists were not familiar with AI | Not mentioned | Most dentists were not familiar with R and AI. Dentists had a positive attitude towards R/AI, but its use and applications were limited due to insufficient knowledge and understanding |
| Bisdas, 2021, 63 countries around the world 29 | Assessing the attitude of medical and dental students towards AI | Most students agree that AI advances will make medicine and dentistry more exciting (69.9%). They stated that AI will be a part of medical education (85.6%) and are eager to incorporate AI into their future activities (99%) | Not mentioned | Not mentioned | Students have a basic understanding of the principles of AI, have a positive attitude towards AI, and are willing to incorporate it into their education |
| Blease, 2022, Ireland 30 | Assessing the experiences and opinions of final‐year medical students across Ireland regarding their exposure to AI/ML during their course of study | Medical students reported limited knowledge and training about AI/ML | Not mentioned | Not mentioned | To help address educational gaps, we suggest medical schools consider short‐term, interdisciplinary courses in digital health, including understanding and augmented intelligence, to empower students to keep up with technological advances |
| Boillat, 2022, United Arab Emirates 31 | Identifying the level of familiarity of medical students and doctors with AI in medicine, as well as the challenges, obstacles, and possible risks related to the democratization of this new paradigm | Medical students perceived AI in medicine as leading to higher risks for patients and the medical field | We also identified a relatively low level of familiarity with AI (medical students = 5.211; physicians = 5.06) and low attendance at education or training | Not mentioned | The low level of familiarity with AI identified in this study calls for the implementation of training in medical schools and hospitals to ensure that medical professionals can use this new paradigm and improve health outcomes |
| Brandes, 2020, Brazil 32 | Evaluating the effect of AI on the choice of radiology major by medical students | More than half (52.5%) said they believe AI is a threat to the radiology job market | 64.3% claimed they did not know enough about these new technologies, and 31.7% said they wanted more information about how they work | Not mentioned | A significant proportion of surveyed students perceive AI as a threat to radiological practice that affects their career choice |
| Banerjee, 2021, the UK 33 | Investigating the effect of AI technologies on the clinical training of doctors in postgraduate studies | The majority (58%) perceived an overall positive impact of AI technologies on their education and training. Most respondents also agreed that AI would reduce clinical workload (62%) and improve research and audit training (68%) | The majority reported adequate AI training in their current curricula (92%) and supported having more formal AI training (81%) | Not mentioned | Practitioners have a generally positive perception of the impact of AI technologies on clinical education |
| Bin Dahmash, 2020, Saudi Arabia 34 | Investigating medical students' perceptions of the impact of AI on radiology and the impact of this perception on choosing the field of radiology as a lifelong career | 50% of the participants believed that they had a good understanding of AI | When AI knowledge was tested using five questions, only 22% of the questions were answered correctly | Not mentioned | Concerns that AI might replace radiologists in the future had a negative impact on medical students' perception of radiology as a career |
| Pinto Dos Santos, 2019, Germany 18 | Evaluation of medical students' attitudes towards AI in radiology and medicine | Medical students are not worried about AI replacing human radiologists | About 52% were aware of current discussions about AI in radiology | Not mentioned | Contrary to media anecdotes, undergraduate medical students are not worried about AI replacing human radiologists and are aware of the potential applications and implications of AI in radiology and medicine |
| Ejaz, 2022, the UK 35 | A report on the state of AI in medical education worldwide, examining the perspectives of medical students | There was support for the inclusion of AI education in mainstream curricula around the world | Few students had received AI training | Not mentioned | Medical students from all countries should be offered AI training as part of their curriculum to develop skills and knowledge about AI and ensure a patient‐centric digital future in medicine |
| Gong, 2019, Canada 19 | Examining Canadian medical students' perceptions of the impact of AI on radiology and their impact on students' preference for radiology specialization | Only a minority (29.3%) of respondents agreed that AI would displace radiologists in the foreseeable future, but a majority (67.7%) agreed that AI would reduce the demand for radiologists | Not mentioned | Not mentioned | Anxiety about the “displacement” (not “replacement”) of radiologists by AI has dissuaded many medical students from considering a radiology specialty |
| Jussupow, 2022, Germany 36 | Investigating how medical professionals perceive resistance to AI due to threats to professional identity and time perception of AI systems | Novice doctors showed relatively high resistance to and threat from AI, while experienced doctors showed slightly lower resistance and threat | Not mentioned | Not mentioned | AI systems can be seen as a threat to the identity of the medical profession |
| Kansal, 2022, India 37 | Assessing knowledge of the basic principles, limitations, and applications of AI in healthcare among medical students and physicians in developing countries | Most participants felt that AI would play an important role in the delivery of healthcare services in the future (74.4%) | They were unaware of the applications (79.6%) and limitations of AI (82.8%) | Not mentioned | Formal training courses for teaching about AI should be focused on medical schools and hospitals to facilitate the coherent and scientific dissemination of knowledge |
| Kasetti, 2020, Britain 38 | Assessing medical students' understanding of AI in medicine | 83% of students believed that AI plays an important role in medicine | Most participants were aware of the role of AI in medicine and radiology | Not mentioned | AI is growing rapidly, so doctors need to be prepared and aware of it |
| Khanagar, 2021, Saudi Arabia 39 | Assessment of knowledge, attitude, and perception of dental students in Riyadh, Saudi Arabia, towards AI | 46.8% strongly agree that AI will lead to major advances in dentistry and medicine | 50.1% had no basic knowledge about the working principles of AI. Also, the majority did not know about the use of AI in dentistry (55.8% no) | Not mentioned | Dental students were eager to learn more about new technologies related to dentistry. To improve the knowledge of dental students about AI, lectures, training courses, and scientific meetings should be given much attention |
| Khafaji, 2022, Saudi Arabia 40 | Assessing knowledge and understanding of AI among radiology residents across Saudi Arabia and assessing their interest in learning AI | Approximately 43.5% of participants did not expect AI to affect jobs, while 42% predicted that jobs would decrease. Approximately 53% expected a decrease in reporting workload, while 28% expected an increase in workload | Sixty‐four people (41.6%) of the residents reported familiarity with AI | Not mentioned | Radiologists' exposure to AI is insufficient. AI must be introduced to radiology trainees |
| Ooi, 2021, Singapore 41 | Evaluating the attitudes and learning needs of radiology residents and radiologists regarding AI and ML in the field of radiology | The majority agreed that AI/ML would drastically change radiology (88.8%) | 64.8% considered their understanding of AI/ML to be beginner level | Not mentioned | Growing optimism about technologically changing radiology and the implementation of AI/ML has led to strong demand for AI/ML curriculum in residency training |
| Pauwels, 2021, Brazil 42 | Investigating the attitudes of Brazilian dentists and dental students regarding the impact of AI in oral radiology and investigating the impact of an AI introductory lecture on their attitudes | Mixed responses were found regarding concerns about the development of AI (33.5% agree) and the replacement of oral radiologists with AI programs in the next 15 years (22.9% agree) | 63% of participants were unfamiliar with the application of AI in radiology, and a significant amount (24.7%) assessed that they already had a basic understanding of the technology | Not mentioned | An overall positive attitude towards AI was found. An introductory lecture benefited this attitude and alleviated concerns about the impact of AI on the oral radiology profession |
| Qurashi, 2021, Saudi Arabia 43 | Studying the familiarity of Saudi Arabian radiology personnel with AI applications and its usefulness in clinical practice | Most participants (n = 214, 95.5%) expressed a strong interest in AI training and would like to incorporate it into clinical radiology practice. Almost half of the radiography students (22/46, 47.8%) believe that their careers may be at risk due to the use of AI (p = 0.038) | Most respondents (n = 160, 71.4%) reported no formal training on AI‐based applications | 82% of participants (184) had never used AI in their departments | Participants showed a positive attitude towards AI, a reasonable understanding, and a high motivation to learn and incorporate it into clinical practice. Some participants felt their jobs were threatened by adapting to AI, but this belief may change with the right training programs |
| Rainey, 2021, the UK 44 | It was to determine knowledge, skills, and confidence in AI among UK radiographers and highlight priorities for training providers to support the digital healthcare ecosystem | Diagnostic radiographers stated that they felt confident or very confident in using AI technologies in radiography compared with radiotherapy responses (28.2% and 33.8%, respectively) | Many respondents stated that they understood the concept of AI in general (78.7% for diagnostic respondents and 52.1% for therapeutic radiography respondents, respectively). 57% of diagnostic and 49% of radiotherapy respondents feel they are not sufficiently trained to implement AI in the clinical setting | 52% and 64% said they had not developed any AI skills, while 62% and 55% said there was insufficient AI training for radiographers | The results of this survey not only highlight the lack of knowledge, skills, and confidence of radiographers in using AI solutions, but also the need for formal training in AI to prepare the current and future workforce for the future clinical integration of AI in healthcare |
| Scheetz, 2021, Australia and New Zealand 45 | To investigate the perceptions of ophthalmologists, radiologists/radiation oncologists, and dermatologists about AI | The majority (449 = 71.0%) believed that AI will improve the field of medicine and that the needs of the medical workforce will be affected by this technology in the next decade (n = 542, 85.8%) | Almost half of the respondents (47.6%; n = 301) rated their knowledge of AI as average compared with their peers | Most respondents indicated that they had never used AI applications in their work (511, 80.9%) | Most respondents in the survey perceived the introduction of AI technology in their respective fields as a positive development |
| Santos, 2021, 94 countries around the world | Investigating the knowledge of medical physicists about AI and their understanding of the relevance and impact of AI in the practice of medical physics | An overwhelming majority of survey respondents (91%) agree that AI will play a central role in the practice of medical physicists | Only 34% can confidently say they have a working knowledge of AI | 22% think they have the relevant expertise in AI, and 53% do not have the right skills | AI can help automate and speed up processes, allowing medical physicists to focus on areas that need improvement. Most people agree that AI knowledge should be taught to new generations |
| Sit, 2020, the UK 47 | Examining the attitude of medical students in England (UK) about AI, their understanding, and career intention towards radiology and investigating the state of education related to AI among this group | Most respondents believe that AI will play an important role in healthcare in the future (88%, n = 432) | Not mentioned | Not mentioned | UK medical students understand the importance of AI and are eager to get involved |
| Teng, 2022, Canada 14 | Investigating and identifying gaps in Canadian healthcare students' knowledge of AI | Most participants reported a positive outlook on the development of AI in their respective healthcare fields and believed that AI would impact their jobs. However, concern about job loss was a common theme among healthcare students | More than half of the respondents either did not know what AI is (51.08%) or had the wrong understanding of it (631.2%) | Not mentioned | The lack of understanding of AI indicates an urgent need for education, as healthcare providers may increasingly need to use AI applications in their activities |
| Wood, 2021, the United States 48 | An integrated medical education curriculum assesses the attitude of medical students and professors towards AI to prepare for teaching AI basics and data science applications in clinical practice | Students and faculty stated that AI would revolutionize medical practice, improve some aspects of healthcare, and should be part of medical education, and disagreed with the statement that AI technology threatened their jobs | 30% of students and 50% of professors answered that they know about AI issues in medicine | Almost half (45%) of students use some type of AI application | Professors and students are very interested in teaching AI in various subjects, and there is a strong need to prepare instructors to teach various aspects of AI technologies |
| Yurdaisik, 2021, Turkey 49 | Investigating the knowledge and attitude of radiology department employees about AI | 35.3% thought AI applications would have a negative impact on radiologists' careers, while 30.3% thought these applications would have a positive impact | 47.1% of participants reported having sufficient knowledge about AI applications in general, while only 25% stated that they had sufficient knowledge about AI applications in radiology | Not mentioned | Healthcare workers in radiology departments are concerned that AI will replace them soon. Raising the knowledge of radiology staff is important to help develop AI applications in radiology |
| Yüzbaşıoğlu, 2021, Turkey 50 | Evaluation of dental students' knowledge and attitude towards AI and possible applications in dentistry | While most participants agreed that dentistry would be revolutionized by AI (85.70%), half of the participants did not agree that AI could be replaced shortly | 48.40% of the participants (n = 534) had basic knowledge about AI technologies | Not mentioned | Although the participants do not have sufficient knowledge of AI, they are willing to improve their knowledge in this field. The results of this survey showed that students feel the use of AI in dentistry is useful |
| Reeder, 2022, the United States 51 | Investigating the impact of AI on US medical students' choice of radiology as a career and their opinions | AI significantly reduced students' preference for radiology ratings (p < 0.001). One‐sixth of students who chose radiology as their first choice did not do so because of AI, and almost half of those who considered radiology in their top three choices were concerned about AI | Not mentioned | Not mentioned | AI significantly negatively affected US medical students' choice of radiology as a career |
| Park, 2021, the United States 52 | A survey of US medical students' views of radiology and other medical specialties regarding AI | More than 75% of respondents agreed that AI would play an important role in the future of medicine. Most of them (66%) agreed that diagnostic radiology was the most affected specialty. Nearly half (44%) reported that AI made them less enthusiastic about radiology | Not mentioned | Not mentioned | US medical students believe AI will play an important role in medicine, especially radiology. However, almost half are less interested in radiology because of AI |
| Tran, 2021, Vietnam 53 | Development of a theoretical model to explore the behavioral intentions of medical students to adopt an AI‐based diagnosis support system | Effort hope (β = 0.201, p < 0.05) and social influence (β = 0.574, p < 0.05) had a positive relationship with initial trust, while there was no relationship between performance expectation and initial trust (p > 0.05) was not found. Only social influence (β = 0.527, p < 0.05) was related to behavioral intention in a positive way | Not mentioned | Not mentioned | This study highlights the positive behavioral intentions towards using an AI‐based diagnosis support system among prospective Vietnamese physicians and the influence of social influence on this choice |
| Oh, 2019, South Korea 54 | Examining the knowledge of medical students and Korean doctors about AI and evaluating their attitude towards the medical application of AI | Most participants found AI useful in the medical field (558/669, 83.4% agreement). Respondents agreed that the area of medicine where AI is most useful is disease diagnosis (558/669, 83.4% agreement) | Only 40 people (5.9%) answered that they were familiar with AI. One possible problem mentioned by the participants was that the AI could not help in unexpected situations due to insufficient information (196/669, 29.3%) | Not mentioned | Korean doctors and medical students have a favorable attitude towards AI in the medical field. Most of the doctors surveyed believed that AI would not replace their role in the future |
| van Hoek, 2019, Switzerland 55 | Assessing the views of radiologists, surgeons, and medical students on several important topics regarding the future of radiology, such as AI, teleradiology, and 3D printing | While most participants agreed that AI should be included as a support system in radiology (Likert scale 0–10: median value 8), surgeons were less supportive than radiologists (p = 0.001). Students saw the potential risk of AI more than radiologists (p = 0.041) | Not mentioned | Not mentioned | Given AI, radiologists expect their workflow to become more efficient and tend to support the use of AI, while medical students and surgeons are more skeptical of the technology. Medical students see AI as a potential threat to diagnostic radiologists, while radiologists themselves are relatively fearful |
| Dumić‐Čule, 2020, Croatia 56 | Assessing attitudes about the importance of introducing AI education into medical school curricula among physicians whose daily work is significantly affected by AI | The responses showed very high support across age groups regardless of subspecialty area. A large majority of participants—89.6% (95% Agresti—CI 0.83–0.94) agreed on the need to include AI education in medical curricula | Not mentioned | Not mentioned | The results of the study showed strong agreement among radiologists and radiology residents about the need for AI training to be part of medical school curricula |
| Auloge, 2020, Europe 57 | Assessment of awareness and knowledge of interventional radiology (IR) in a large population of medical students in 2019 | 34.8% of participants answered that AI is a threat to radiologists. | Not mentioned | Not mentioned | The development of new technology supporting AI advances will likely continue to change the radiology landscape. Most medical students want more information about IR in their medical curriculum. About a quarter of students are interested in a career in IR |
| Blacketer, 2021, Australia, New Zealand, and the United States 58 | Assessing current levels of understanding for the effective use of ML tools among clinical medical students and practitioners at three centers in Australia, New Zealand, and the United States | Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty identifying weaknesses in the model's performance analysis | Not mentioned | Not mentioned | Of the 245 individuals who participated in the study (response rate = 45.4%), the majority had difficulty identifying weaknesses in the model's performance analysis. Further studies are necessary to investigate educational interventions on such ML topics |
Abbreviations: 3D, three‐dimensional; AI, Artificial Intelligence; DL, deep learning; ML, machine learning.
3.4. Students' attitudes towards AI
Of the 38 included studies, 29 (76%) healthcare students had a positive and promising attitude towards AI in the clinical profession and its use in the future. 14 , 18 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 35 , 37 , 39 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 54 , 55 , 56 , 58 Among these, 17 studies were conducted in HICs, 14 , 18 , 32 , 33 , 35 , 37 , 40 , 42 , 44 , 46 , 47 , 48 , 49 , 50 , 54 , 56 , 58 11 studies were conducted in LMICs, 26 , 27 , 28 , 29 , 30 , 39 , 41 , 45 , 51 , 52 , 55 and a study was conducted in countries around the world. 31 In Ahmed et al.'s study, 129 (27.4%) of the participants strongly agreed, 221 (47%) agreed that AI is necessary for the field of medicine, while only 5 (1.1%) of the participants completely disagreed, and only 10 (2.1%) disagreed. 26 Additionally, according to Al Saad et al., the majority of respondents (77.4%, n = 697) thought AI had a significant impact on healthcare, with only a small proportion disagreeing or being neutral. 27 According to AlAhmari et al., the majority of participants (52.8% and 21.1%) agreed and strongly agreed with the statements “I think AI will lead to major advancements in dentistry and medicine” and “AI is exciting medicine and dentistry,” respectively. 28 According to Alomary et al., the majority of 343 students (54.8%) thought that ML had a bright future in the field of otolaryngology in the Kingdom of Saudi Arabia, and about 235 students (69.2%) thought that ML was significant in medical practice. Few students felt that the otorhinolaryngology curriculum's 162 (30.1%) ML was not important for learning. 29 Additionally, according to Abouzeid et al., the majority of students (69.9%) believe that advances in AI will make medicine and dentistry more exciting and that AI will be a part of medical education (85.6%). They also expressed a desire to incorporate AI into future activities. (99%) Consider yourself. 30 Additionally, according to Banerjee et al., most participants (58%) believed that AI technologies would have a generally positive impact on education. They also concurred that AI would reduce clinical workload (62%), as well as the amount of research and auditing required of educators. 35 In a separate study by Santos et al., most participants agreed that AI would revolutionize and improve radiology (77% and 86%), but they disagreed (83%) with the claim that it would replace human radiologists. 48
However, in nine of the studies (24%), students considered AI a threat to healthcare fields, especially radiology, and had a negative attitude towards it. 19 , 34 , 36 , 38 , 42 , 51 , 53 , 59 , 60 Among these, six studies were conducted in HICs 19 , 34 , 38 , 53 , 59 , 60 and three studies were conducted in LMICs. 36 , 42 , 51 In the study by Boillat et al., medical students believed that the use of AI in medicine increased risks for patients and the medical community. 33 Additionally, more than half (52.5%) of respondents to Brandes et al.'s stated that they thought AI posed a threat to the radiology job market. 34 In a different study by Bin Dahmash et al., 36 concern that AI could eventually replace radiologists had a negative impact on medical students' perceptions of radiology as a career (58.8%). In their study, Gong et al. found that while the majority of respondents (67.7%) agreed that AI would decrease the need for radiologists, only a minority of respondents (29.3%) thought that AI would eventually replace radiologists. Additionally, 48.6% of participants concurred that AI makes them nervous when thinking about the radiology specialty. 19 Jussupow et al. discovered in a different study that, compared with medical professionals, medical students were more resistant to AI and experienced stronger identity threats. 38
3.5. Students' knowledge of AI
Of the 38 included studies, 26 evaluated the knowledge of healthcare students about AI. 14 , 26 , 27 , 28 , 29 , 30 , 33 , 34 , 35 , 36 , 37 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 51 , 52 , 56 Among these, 18 studies evaluated the level of students' knowledge as low (50%). 14 , 26 , 27 , 28 , 30 , 33 , 34 , 36 , 37 , 39 , 41 , 42 , 44 , 45 , 46 , 48 , 52 , 56 Of the 18 included studies, 10 were conducted in LMICs 26 , 27 , 28 , 30 , 36 , 39 , 41 , 42 , 45 , 52 and eight were conducted in HICs. 14 , 33 , 34 , 37 , 44 , 46 , 48 , 56 Only 35.3% of the 470 participants in Ahmed et al.'s study had a basic understanding of AI (ML and DL). Men made up the majority of the study participants who were knowledgeable about AI, and nearly 77% of them were unaware that AI was being used in medicine. It demonstrated that, despite having a basic understanding of AI, Pakistani doctors, and medical students are unaware of its applications. 26 In their study, Al Saad et al. also reported that most participants (about 85%, n = 765) had heard of AI or ML, and about half of the participants (51.8%, n = 466) had read articles about AI in the past 2 years. Regarding participation in AI, ML, and data science courses in the past 5 years, the majority never attended any course (78.4%, n = 706), and after that, participants only attended one course (11.6%, n = 104). 27 In AlAhmari et al.'s study, they also reported that the majority of participants (77.1%) did not have basic knowledge about the working principles of AI, and 63.3% did not know about the application of AI in dentistry. 28 According to Blease et al., 80.6% of participants had not read any academic articles or journals on AI or ML. 32 Furthermore, Brandes et al. reported in their study that 52.5% of the 53 participants thought AI was a threat to the radiology job market, and 37 (36.6%) thought it even threatened radiologists' auxiliary functions. 34 Another study by Santos et al. found that approximately 52% of participants were aware of the current debate about AI in radiology, but 68% were unaware of the technologies involved. 48
On the other hand, in six of the studies, students' high knowledge of AI was reported, 29 , 35 , 40 , 43 , 48 , 50 and two of the studies reported average students' general knowledge (almost 50%). 47 , 51 Among these, six studies were conducted in HICs 35 , 40 , 43 , 46 , 48 , 50 and two studies were conducted in LMICs. 29 , 51 Alomary et al. reported that, in general, 308 (57.3%) students were familiar with ML. 29 The majority of participants in the study by Banerjee et al. also agreed that their current curricula provided adequate AI training (92%), and they supported the idea of more formal AI training (81%). 35 In Ooi et al.'s study, 64.8% of participants had a fundamental understanding of AI/ML, but 76.0% planned to increase their knowledge of the field, and 67.2% were eager to take part in an AI/ML research project. 43 The perception of students and interns at three specialty colleges (ophthalmology, radiology/radiation oncology, and dermatology) regarding AI was examined in a different study by Scheetz et al. The researchers discovered that almost half of the respondents (47.6%) rated their knowledge of AI as average compared with their peers, and a small number rated their knowledge as excellent (5.5%) or very poor (4.9%). The three professional groups' responses were similar (p = 0.542). 47
3.6. Students' skills towards AI
Of the 38 included studies, six evaluated the skill of healthcare science students in AI. 26 , 45 , 46 , 47 , 48 , 50 Of the six studies, four (67%) students had very low skills, so they stated they had never worked with AI. 45 , 46 , 47 , 48 Of the four included studies, three studies were conducted in HICs 46 , 47 , 48 , 49 and one study was conducted in LMICs. 45 Only 14% of 1019 medical physics professionals, interns, and students confidently claimed to be skilled in designing, coding, and managing an AI program, according to Santos et al. 48 In a different study, Abouzeid et al. discovered that the majority of dentists were ignorant of robotics (R) and AI. Dentists had a favorable attitude towards R/AI, but its use and applications were constrained because of a lack of understanding and knowledge. 30 The majority of participants (511, 80.9%), according to Scheetz et al., had never used AI applications in their professional capacity as doctors. But compared with radiologists/dermatologists or radiation oncologists (15.7%, 6.1%, and 5.2%, respectively, p = 0.001), ophthalmologists were more than twice as likely to use AI in their routine clinical practice. 47 Additionally, 62% and 55% of UK radiologists said there was insufficient AI training for radiologists, while 52% and 64% of all radiologists, including students and retired radiologists, said they had no AI skills. 46 The opposite was also true, according to Qurashi et al., who found that 82% of participants (184) had never used AI in their departments. 45
However, students' skill in AI was reported as average in two studies (33%), both conducted in LMICs. 26 , 50 Only 53 (11.3%) of the doctors in Ahmed et al.'s study used AI, and they all agreed that it made the relevant tasks simpler. This included 20 (8.1%) medical students and 33 (14.8%) doctors. The remaining 417 participants (88.7%), including 190 doctors and 227 medical students (91.9% and 85.2%, respectively), had never used AI in their work. Forty participants (8.5%) had used AI in radiology for diagnostic and research purposes using X‐ray, computed tomography (CT) scan, and MRI techniques. The practical use of AI in pathology for culture and sensitivity, as well as histopathological testing for diagnostic and research purposes, was experienced by 32 participants (6.8%). 26 Additionally, according to Wood et al., of the 117 medical students and 44 clinical faculty, about half (45%) of the students used an AI application. 50
4. DISCUSSION
4.1. Principal findings
Evidence from this review indicates that healthcare students had a positive and encouraging attitude towards the use of AI in medicine, but the majority of students had little knowledge of and limited experience with using AI. 38 studies complied with all of the requirements for inclusion in this review. The attitudes, knowledge, and skills of healthcare students towards AI were evaluated in all of the included cross‐sectional studies. The JBI checklist criteria found that almost all studies had moderate‐to‐high‐quality evidence.
Recent broad surveys report a range of positive 14 , 18 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 36 , 38 , 39 , 40 , 41 , 43 , 44 , 45 , 46 , 47 , 48 , 50 , 51 , 53 , 54 , 55 , 57 , 59 and negative 19 , 34 , 36 , 38 , 42 , 51 , 53 , 59 , 60 attitudes towards AI. Although attitudes towards AI among healthcare students were generally positive. But the majority of negative attitudes were in HIC countries. In contrast, the majority of attitudes in LMICs were positive. However, Santos et al. conducted an international survey in 94 countries among medical physics professionals and students worldwide. The majority of the respondents to this study were in developed countries, such as the UK (10%), Malaysia (8%), the United States (8%), Australia (6%), and Japan (5%). More than 85% of respondents had a positive view of AI and agreed that AI will play a central role in the practice of medical physicists. The majority of them also stated that AI should be taught in medical physics graduate programs and that more applications, such as quality control, and treatment planning, will be provided by AI. As a result, the hypothesis that positive or negative attitudes are more desirable in developed countries may not be correct, and most of the students' negative attitudes in their field of study, particularly in radiology, 34 , 42 , 51 and their career futures should be pushed in one direction. One of the downsides of AI is potential job displacement. Frey created computer aptitude scores for 702 occupations, many of which had highly developed computer aptitudes. 61 Makridakis et al. conducted an analysis with a similar objective but a different methodology, and also identified a range of occupations at risk of automation. 62 Naturally, this may lead to negative feelings towards AI. However, White et al. found that although people have negative feelings if they imagine that robots will replace other people's jobs, they will feel less negative if robots replace their jobs compared with their own. These findings suggest that jobs with highly predictable tasks may be automated, so people's concerns about their future employment may be justified. 63 This issue can be positive from another point of view. For example, from the point of view of managers, reducing manpower can have a positive aspect. 64 , 65 As noted, AI can also raise ethical concerns as Fenech et al. (2018) 66 reported that there are differing views on the use of AI in medical diagnoses, such as a recent study in the UK where the majority opposed the use of AI. Vayena et al. 67 investigated whether, in response to feeling uncomfortable, the majority of the UK public can make use of AI and ML in medical settings in tasks such as answering medical questions and offering treatment (17% agree, 63% disagree, and 20% do not know). They concluded that data protection, aside from preventing bias in decision‐making, should bolster trust in these programs. Overall, many important positive and negative views about AI have been identified in previous studies. But most students attribute this concern to a lack of knowledge and limited understanding of AI. 19 , 34 , 36 , 38 , 42 , 50 , 52 , 58 , 60
AI knowledge was generally low among healthcare students. Although the majority of studies included in this review were conducted in HICs. However, the majority of low knowledge was reported among students in LMICs. On the other hand, the level of AI skill was only measured in a small number of studies. Students stated that they have very little skill using AI. There were also limited reported cases of AI being used in diagnostic aids, and it was rarely used for complex medical and therapeutic procedures, even in HICs. All these studies attributed the low knowledge and skill of healthcare students to not completing the training course and the lack of relevant course units in this field. Given that previous research has shown that the use of AI will improve countries' economies by saving time and resources. Therefore, if countries have a plan to develop and use AI, it is necessary to plan for its training now. 68 Also, to properly use AI technology, it is very important to provide education to healthcare students as key players in the health of society. 31 , 32 , 33 , 35 , 59 Additionally, to accomplish this goal, healthcare organizations, hospitals, employers, and the media must collaborate to address this shortage. 37 , 69 , 70 , 71 , 72 , 73 , 74 The same topic was the subject of a national study in 2019 that emphasized the significance of equipping the current and future clinical workforce with the abilities required to use new digital tools critically, including those supported by AI. 75 Therefore, to implement and comprehend how AI technology functions, face‐to‐face training, training manuals, and step‐by‐step instructions are crucial for students. Future research should look into the best way to integrate training with AI‐related methods.
4.2. Strengths and limitations
There are many advantages to this systematic review. First, we adhered to guidelines for demanding systematic review techniques. 24 Second, the JBI assessment checklist was used to evaluate the strength of the evidence from each study that was included. This improved the transparency of the included studies' quality. Third, this review provides valuable insights to educational policymakers in the field of AI in medicine and healthcare.
On the other hand, this study had potential limitations. First, due to the diversity of the questionnaire studies, it can be mentioned that in one questionnaire, the knowledge of the basic approaches to AI may have been addressed, whereas in another study the knowledge of the advanced approaches to AI has been addressed. As a result, the reported results may be the same because the research tools should be changed. Also, due to the wide differences in factors such as age, gender, and semester of study among the students, it was not possible to accurately explain the definitive results and conduct a meta‐analysis study. Second, this study included only peer‐reviewed studies published in scientific journals and conferences; therefore, articles published in the gray literature are not included in the present study. Third, this review was not registered in PROSPERO.
5. CONCLUSION
Evidence from this review shows that healthcare students had a positive and promising attitude towards AI in medicine; however, most students had low knowledge and limited skills in working with AI. All the included studies in our review attributed the low knowledge of healthcare students to not completing the training course and the lack of relevant course units in this field. Face‐to‐face instruction, training manuals, and detailed instructions are therefore crucial for implementing and comprehending how AI technology works to raise students' knowledge of the advantages of AI.
AUTHOR CONTRIBUTIONS
SFMB contributed to the search strategy, writing the protocol, writing the first draft of the article. MS participated in controlling the search strategy and writing the first draft of the article. KGH and MGH participated in data extraction and quality assessment of articles. ASM participated in the control of study selection, data extraction and quality assessment of the articles. KK contributed to study design and writing the study protocol. All authors have read and approved the final version of the manuscript.
CONFLICT OF INTEREST STATEMENT
The authors declare no conflict of interest.
ETHICS STATEMENT
This study was approved by the ethical committee of Mashhad University of Medical Sciences (approval number IR.MUMS FHMPM.REC.1401.081).
TRANSPARENCY STATEMENT
The lead author Khalil Kimiafar affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned (and, if relevant, registered) have been explained.
ACKNOWLEDGMENTS
We hereby express our gratitude to the Student Research Committee of Mashhad University of Medical Sciences who helped us in conducting this research.
APPENDIX A. PRISMA (PREFERRED REPORTING ITEMS FOR SYSTEMATIC REVIEWS AND META‐ANALYSES) CHECKLIST
| Section/topic | # | Checklist item | Reported on page # |
|---|---|---|---|
| Title | |||
| Title | 1 | Identify the report as a systematic review, meta‐analysis, or both. | 1 |
| Abstract | |||
| Structured summary | 2 | Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. | 2 |
| Introduction | |||
| Rationale | 3 | Describe the rationale for the review in the context of what is already known. | 2 |
| Objectives | 4 | Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS). | 3 |
| Methods | |||
| Protocol and registration | 5 | Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number. | N/A |
| Eligibility criteria | 6 | Specify study characteristics (e.g., PICOS, length of follow‐up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. | 3 |
| Information sources | 7 | Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. | 3, 10, 11 |
| Search | 8 | Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. | 3 |
| Study selection | 9 | State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta‐analysis). | 4 |
| Data collection process | 10 | Describe the method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. | 4 |
| Data items | 11 | List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. | 4 |
| Risk of bias in individual studies | 12 | Describe methods used for assessing the risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. | 4 |
| Summary measures | 13 | State the principal summary measures (e.g., risk ratio and difference in means). | N/A |
| Synthesis of results | 14 | Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I 2) for each meta‐analysis. | N/A |
| Section/topic | # | Checklist item | Reported on page # |
|---|---|---|---|
| Risk of bias across studies | 15 | Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias and selective reporting within studies). | N/A |
| Additional analyses | 16 | Describe methods of additional analyses (e.g., sensitivity or subgroup analyses and meta‐regression), if done, indicating which were pre‐specified. | N/A |
| Results | |||
| Study selection | 17 | Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. | 4 |
| Study characteristics | 18 | For each study, present characteristics for which data were extracted (e.g., study size, PICOS, and follow‐up period) and provide the citations. | 4–6 |
| Risk of bias within studies | 19 | Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). | 4 |
| Results of individual studies | 20 | For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. | 4, 5 |
| Synthesis of results | 21 | Present results of each meta‐analysis done, including confidence intervals and measures of consistency. | N/A |
| Risk of bias across studies | 22 | Present results of any assessment of risk of bias across studies (see Item 15). | N/A |
| Additional analysis | 23 | Give results of additional analyses, if done (e.g., sensitivity or subgroup analyses, meta‐regression [see Item 16]). | N/A |
| Discussion | |||
| Summary of evidence | 24 | Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policymakers). | 6–8 |
| Limitations | 25 | Discuss limitations at the study and outcome level (e.g., risk of bias), and at review‐level (e.g., incomplete retrieval of identified research, reporting bias). | 8 |
| Conclusions | 26 | Provide a general interpretation of the results in the context of other evidence, and implications for future research. | 8 |
| Funding | |||
| Funding | 27 | Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. | 1 |
APPENDIX B. SEARCH STRATEGY FOR EACH DATABASE
| # | Search strategy in PubMed database | Results |
|---|---|---|
| 1 | ((“Artificial intelligence” [MeSH Terms]) AND (“students, medical” [MeSH Terms])) AND (“Attitude” [MeSH Terms]) | N = 15 |
| 2 | (“Artificial intelligence” [Title/Abstract] OR “Deep learning” [Title/Abstract] OR “Machine learning” [Title/Abstract] OR “Machine intelligence” [Title/Abstract] OR “Neural net” [Title/Abstract] OR “Computational Intelligence” [Title/Abstract] OR “Intelligence, Artificial” [Title/Abstract] OR “Machine Intelligence” [Title/Abstract] OR “Computer Reasoning” [Title/Abstract] OR “AI” [Title/Abstract]) | N = 143,785 |
| 3 | (“Trainee” [Title/Abstract] OR “student” [Title/Abstract] OR “Resident” [Title/Abstract] OR “Students, Medical” [Title/Abstract] OR “Medical Students” [Title/Abstract] OR “Student, Medical” [Title/Abstract] OR “Medical Student” [Title/Abstract] OR “Medical Science Student” [Title/Abstract]) | N = 251,194 |
| 4 | (“Attitude” [Title/Abstract] OR “Sentiment” [Title/Abstract] OR “Sentiments” [Title/Abstract] OR “Opinions” [Title/Abstract] OR “Opinion “OR “Perception” [Title/Abstract] OR “Knowledge” [Title/Abstract] OR “Awareness” [Title/Abstract] OR “Skill” [Title/Abstract]) | N = 1,370,846 |
| 5 | 2 AND 3 AND 4 | N = 314 |
| 6 | 1 OR 5 | N = 325 |
| # | Search strategy in embase database | Results |
|---|---|---|
| 1 | “artificial intelligence”: ti, ab, kw OR “deep learning”: ti, ab, kw OR “machine learning”: ti, ab, kw OR “neural net”: ti, ab, kw OR “computational intelligence”: ti, ab, kw OR “intelligence, artificial”: ti, ab, kw OR “machine intelligence”: ti, ab, kw OR “computer reasoning”: ti, ab, kw OR “ai”: ti, ab, kw | N = 175,835 |
| 2 | (“Trainee”: ti, ab, kw OR “student”: ti, ab, kw OR “Resident”: ti, ab, kw OR “Students, Medical”: ti, ab, kw OR “Medical Students”: ti, ab, kw OR “Student, Medical”: ti, ab, kw OR “Medical Student”: ti, ab, kw OR “Medical Science Student”: ti, ab, kw) | N = 359,966 |
| 3 | attitude:ti, ab, kw OR “sentiment”: ti, ab, kw OR “sentiments”: ti, ab, kw OR “opinions”: ti, ab, kw OR “opinion”: ti, ab, kw OR “perception”: ti, ab, kw OR “knowledge”: ti, ab, kw OR “awareness”: ti, ab, kw OR “skill”: ti, ab, kw | N = 1,726,169 |
| 4 | 1 AND 2 AND 3 | N = 393 |
| # | Search strategy in scopus database | Results |
|---|---|---|
| 1 | TITLE‐ABS‐KEY (“Artificial intelligence”) OR TITLE‐ABS‐KEY (“Deep learning”) OR TITLE‐ABS‐KEY (“Machine learning”) OR TITLE‐ABS‐KEY (“Machine intelligence”) OR TITLE‐ABS‐KEY (“Neural net”) OR TITLE‐ABS‐KEY (“Computational Intelligence”) OR TITLE‐ABS‐KEY (“Intelligence, Artificial”) OR TITLE‐ABS‐KEY (“Machine Intelligence”) OR TITLE‐ABS‐KEY (“Computer Reasoning”) OR TITLE‐ABS‐KEY (“AI”) | N = 1,010,660 |
| 2 | TITLE‐ABS‐KEY (“Trainee”) OR TITLE‐ABS‐KEY (“student”) OR TITLE‐ABS‐KEY (“Resident”) OR TITLE‐ABS‐KEY (“Students, Medical”) OR TITLE‐ABS‐KEY (“Medical Students”) OR TITLE‐ABS‐KEY (“Student, Medical”) OR TITLE‐ABS‐KEY (“Medical Student”) OR TITLE‐ABS‐KEY (“Medical Science Student”) | N = 1,732,348 |
| TITLE‐ABS‐KEY (attitude) OR TITLE‐ABS‐KEY (“Sentiment”) OR TITLE‐ABS‐KEY (“Sentiments”) OR TITLE‐ABS‐KEY (“Opinions”) OR TITLE‐ABS‐KEY (“Opinion”) OR TITLE‐ABS‐KEY (“Perception”) OR TITLE‐ABS‐KEY (“Knowledge”) OR TITLE‐ABS‐KEY (“Awareness”) OR TITLE‐ABS‐KEY (“Skill”) | N = 5,130,813 | |
| 3 | 1 AND 2 | N = 9731 |
| # | Search strategy in web of science database | Results |
|---|---|---|
| 1 | TS = (“Artificial intelligence” OR “Deep learning” OR “Machine learning” OR “Machine intelligence” OR “Neural net” OR “Computational Intelligence” OR “Intelligence, Artificial” OR “Machine Intelligence” OR “Computer Reasoning” OR “AI”) | N = 506,228 |
| 2 | TS = (“Trainee” OR “student” OR “Resident” OR “Students, Medical” OR “Medical Students” OR “Student, Medical” OR “Medical Student” OR “Medical Science Student”) | N = 522,761 |
| 3 | TS = (Attitude OR “Sentiment” OR “Sentiments” OR “Opinions” OR “Opinion” OR “Perception” OR “Knowledge” OR “Awareness” OR “Skill”) | N = 3,252,443 |
| 4 | 1 AND 2 AND 3 | N = 1827 |
Mousavi Baigi SF, Sarbaz M, Ghaddaripouri K, Ghaddaripouri M, Mousavi AS, Kimiafar K. Attitudes, knowledge, and skills towards artificial intelligence among healthcare students: a systematic review. Health Sci Rep. 2023;6:e1138. 10.1002/hsr2.1138
DATA AVAILABILITY STATEMENT
Data are available on request from the authors.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available on request from the authors.