Abstract
Introduction:
In this cross-sectional study, the authors explored the knowledge, attitudes, and practices related to artificial intelligence (AI) among medical students in Sudan. With AI increasingly impacting healthcare, understanding its integration into medical education is crucial. This study aimed to assess the current state of AI awareness, perceptions, and practical experiences among medical students in Sudan. The authors aimed to evaluate the extent of AI familiarity among Sudanese medical students by examining their attitudes toward its application in medicine. Additionally, this study seeks to identify the factors influencing knowledge levels and explore the practical implementation of AI in the medical field.
Method:
A web-based survey was distributed to medical students in Sudan via social media platforms and e-mail during October 2023. The survey included questions on demographic information, knowledge of AI, attitudes toward its applications, and practical experiences. The descriptive statistics, χ2 tests, logistic regression, and correlations were analyzed using SPSS version 26.0.
Results:
Out of the 762 participants, the majority exhibited a basic understanding of AI, but detailed knowledge of its applications was limited. Positive attitudes toward the importance of AI in diagnosis, radiology, and pathology were prevalent. However, practical application of these methods was infrequent, with only a minority of the participants having hands-on experience. Factors influencing knowledge included the lack of a formal curriculum and gender disparities.
Conclusion:
This study highlights the need for comprehensive AI education in medical training programs in Sudan. While participants displayed positive attitudes, there was a notable gap in practical experience. Addressing these gaps through targeted educational interventions is crucial for preparing future healthcare professionals to navigate the evolving landscape of AI in medicine.
Recommendations:
Policy efforts should focus on integrating AI education into the medical curriculum to ensure readiness for the technological advancements shaping the future of healthcare.
Keywords: AI—artificial intelligence, attitude, DL—deep learning, knowledge, medical students, ML—machine learning, practice, Sudan
Artificial intelligence (AI) is a software system designed to emulate human intelligence, utilizing data to make independent decisions or aid in decision-making. This broad term includes machine learning, representation learning, deep learning, and natural language processing, extending its influence beyond computer science into fields such as medicine, philosophy, psychology, linguistics, and statistics1.
In the medical field, AI plays a pivotal role, notably in radiology but also in dermatology, ophthalmology, psychiatry, cardiology, oncology, neuroscience, pathology, and general medicine. These algorithms assist in identifying abnormal characteristics, classifying conditions, hypothesizing about underlying issues, determining appropriate procedures, and interpreting results.In pathology, AI enhances predictive and prognostic capabilities, improving tissue histology and molecular data analysis. Similarly, in dermatology and ophthalmology, AI aids in diagnostic imaging and assessment of various conditions2.
While high-income countries invest significantly in AI research for healthcare, developing countries such as Sudan face challenges in education, research, and AI implementation. Limited resources, compounded by the pandemic, highlight the need for AI knowledge to reduce workload and diagnostic errors2.
Despite these challenges, the outlook for AI in healthcare is promising. This approach has the potential to address shortcomings in traditional diagnostic and treatment methods, reduce errors, address diagnostic inaccuracies, and alleviate patient anxiety. However, there are misconceptions about AI’s capabilities and impact on healthcare1,3–7.
In Sudan, despite a presidential AI initiative, obstacles, including resistance to change, financial constraints, a shortage of qualified medical professionals, insufficient data, concerns about physician displacement, societal barriers, confidentiality issues, and medico-legal implications, hinder healthcare AI integration2,8,9.
This research aimed to determine Sudanese medical students’ knowledge and perceptions of AI by assessing current AI practices in Sudan. The hypothesis suggests that medical students may not be fully aware of the implications of AI.
Methodology
Study design and sample size
Between 1 October and 30 October 2023, we conducted a cross-sectional study in Sudan in which a web-based survey was distributed to medical students and physicians through social media applications (WhatsApp, Facebook, Messenger) and e-mail. The questionnaire, adapted from prior research (Ahmed et al., 2022), was confirmed to be accurate for Sudanese respondents. Anonymous responses were collected, and only the principal investigator had access to the survey. Using a convenience sampling technique, we surveyed 30 participants experimentally before a pilot study with 50 individuals to confirm validity and reliability. Sub-scale consistency was assessed using Cronbach’s alpha values (knowledge =0.795, practice =0.702, attitude = 0.663). The tool allowed participants to modify replies and only completed, non-duplicate entries were considered. The inclusion criteria involved Sudanese medical students and doctors, while non-medical responders and incomplete surveys were excluded. The sample size was calculated with a calculator. Based on a 48 million population in 2023 (UN statistics), we aimed for 385 participants, accounting for a 50% design effect, 0.05 margin of error, and 95% confidence level. Participants were encouraged to complete the survey on the Google form. The work has been reported in line with the STROCSS criteria.
Ethical approval
All participants had the right to withdraw from the cross-sectional research at any point, and participation was entirely voluntary. Due to the absence of names or e-mails in the study, participants’ identities remained confidential. Ethics committee provided approval, and the study adhered to the principles of the Helsinki Declaration. For statistical analysis, we employed SPSS version 26.0 and presented variable frequencies through frequency tables. Cronbach’s α coefficient was used to assess the internal consistency of the scale. The χ2 test was used to determine the statistical correlation among categorical variables, with a p value less than 0.05 indicating statistical significance. The Mann–Whitney and Kruskal–Wallis tests were chosen based on the normality of the data. Additionally, univariate logistic regression predicted outcomes related to artificial intelligence (knowledge, attitudes, practices) from baseline characteristics. Unadjusted odds ratios and their respective 95% CIs were used in the regression.
Measurements
Demographic information
The questionnaire consisted of age, sex, qualification level, rank, and university year for the undergraduate participants.
Knowledge of AI
This sub-scale has seven questions about the general knowledge of AI, including knowledge of artificial intelligence machine learning, AI in the medical field, AI in radiology, AI in pathology, and AI during the training of postgraduate doctors (for the statistical analysis, yes = 1, no = 0 and good knowledge is above 3 points). Attitude toward artificial intelligence: This sub-scale has ten questions about attitudes toward AI, including the necessity of AI in the medical field, training, assessment, diagnosis, radiology, pathology, and importance (for the statistical analysis, “don’t know”, “disagree” or “strongly disagree” = 0, agree or strongly agree = 0, and “good attitude” is more than 5 points).
Practice toward AI
This sub-scale has seven questions about the practice of AI, including whether the doctor has inserted the AI in the medical field and whether the patient was willing to use this technique during training (for the statistical analysis, yes = 1, no, never applied = 0 and good practice is greater than 2 points).
Results
A total of 762 (100%) individuals responded to the survey. The participation rate was difficult to estimate due to potential redundancies across social media groups and mailing lists. The respondents’ mean age was 22.4± SD 2.8346, and 80.7% of the respondents were females. In terms of academic experience, there were students with different professional statuses in different institutes in Sudan. The baseline characteristics of all the populations are given in Tables 1 and 2. Knowledge, attitudes, and practice scores for medical students receiving AI.
Table 1.
General characteristics (N = 762)
| Characteristic | Value | Frequency | Percentage |
|---|---|---|---|
| Age (mean 22.4, SD 2.834) | <20 years | 129 | 16.9 |
| 20–22 years | 222 | 29.1 | |
| 23–25 years | 352 | 46.2 | |
| >25 years | 59 | 7.7 | |
| Sex | Male | 147 | 19.3 |
| Female | 615 | 80.7 | |
| Educational level | 1st Grade | 96 | 12.6 |
| 2nd Grade | 108 | 14.2 | |
| 3rd Grade | 96 | 12.6 | |
| 4th Grade | 171 | 22.4 | |
| 5th Grade | 120 | 15.7 | |
| 6th Grade | 171 | 22.4 |
Table 2.
Knowledge, attitudes, and practices scores for medical students receiving AI
| Characteristic | Value | N | Mean (Std. deviation) | |
|---|---|---|---|---|
| Knowledge of artificial intelligence | Age | <20 years | 129 | 2.3721 (1.40351) |
| 20–22 years | 222 | 1.7162 (1.40317) | ||
| 23–25 years | 352 | 2.4886 (1.49449) | ||
| >25 years | 59 | 2.4915 (1.91521) | ||
| Total | 762 | 2.2441 (1.52620) | ||
| Sex | Male | 147 | 2.4490 (1.63516) | |
| Female | 615 | 2.1951 (1.49624) | ||
| Total | 762 | 2.2441 (1.52620) | ||
| Educational level | 1st Grade | 96 | 2.1563 (1.42406) | |
| 2nd Grade | 108 | 1.9722 (1.26386) | ||
| 3rd Grade | 96 | 2.0000 (1.50787) | ||
| 4th Grade | 171 | 2.1053 (1.57947) | ||
| 5th Grade | 120 | 2.2750 (1.55549) | ||
| 6th Grade | 171 | 2.7193 (1.58026) | ||
| Total | 762 | 2.2441 (1.52620) | ||
| Attitude of artificial intelligence | Age | <20 years | 129 | 4.7209 (1.92831) |
| 20–22 years | 222 | 4.0270 (2.42707) | ||
| 23–25 years | 352 | 4.7869 (2.22202) | ||
| >25 years | 59 | 5.3559 (2.11532) | ||
| Total | 762 | 4.5984 (2.26123) | ||
| Sex | Male | 147 | 5.1837 (2.00349) | |
| Female | 615 | 4.4585 (2.29810) | ||
| Total | 762 | 4.5984 (2.26123) | ||
| Educational level | 1st Grade | 96 | 4.9688 (2.10989) | |
| 2nd Grade | 108 | 4.3056 (2.15501) | ||
| 3rd Grade | 96 | 4.7188 (2.41357) | ||
| 4th Grade | 171 | 4.1579 (2.38221) | ||
| 5th Grade | 120 | 4.3750 (2.11462) | ||
| 6th Grade | 171 | 5.1053 (2.18835) | ||
| Total | 96 | 4.9688 (2.10989) | ||
| Practice of artificial intelligence | Age | <20 years | 129 | 3.2636 (1.37227) |
| 20–22 years | 222 | 2.7793 (1.49234) | ||
| 23–25 years | 352 | 3.1449 (1.44964) | ||
| >25 years | 59 | 3.0847 (1.24966) | ||
| Total | 762 | 3.0538 (1.44400) | ||
| Sex | Male | 147 | 3.4966 (1.51420) | |
| Female | 615 | 2.9480 (1.40748) | ||
| Total | 762 | 3.0538 (1.44400) | ||
| Educational level | 1st Grade | 96 | 3.2396 (1.47073) | |
| 2nd Grade | 108 | 2.7778 (1.32081) | ||
| 3rd Grade | 96 | 2.9896 (1.48320) | ||
| 4th Grade | 171 | 2.7544 (1.56371) | ||
| 5th Grade | 120 | 3.1250 (1.41755) | ||
| 6th Grade | 171 | 3.4094 (1.28646) | ||
| Total | 762 | 3.0538 (1.44400) | ||
AI, artificial intelligence.
Knowledge of AI
With respect to knowledge of AI, individuals were questioned about the basic concept of AI; its subtypes, that is machine learning (ML), deep learning (ML) and DL (DL); and its applications. Overall, 642 (84.3%) had a basic understanding of AI, but only 249 (32.7%) had knowledge of ML and DL. Furthermore, only 330 (43.3%) participants demonstrated awareness of AI applications. Notably, 120 (15.7%) individuals lacked knowledge about the basic concept of AI, 513 (67.3%) had no knowledge about ML and DL, and 432 (56.7%) were unaware of any application of AI in the medical field. Only 186 (24.4%) individuals were aware of the application of AI in radiology, and only 165 (21.7%) knew about the application of AI in pathology. A few of the applications of AI known to individuals were in robotic surgery, diagnostic radiology, crisis technology, diagnostic imaging in ophthalmic pathologies, 3D anatomical studies, risk assessment in cardiac patients by imaging techniques, automated ventilators, radiological imaging modalities such as MRI, computed tomography (CT) scan, X-rays and ultrasound, stroke assessment, radiotherapy in cancer patients, histological imaging in pathology laboratories and electrocardiogram (ECG) assessment for cardiac anomalies.Table 3 shows descriptive statistics for knowledge of artificial intelligence among medical students. The correlation between knowledge of AI and different variables and odds ratios are given in Table 4. A lack of curriculum training during graduation and a lack of gender were significant factors affecting AI knowledge, with P values less than 0.05. Females were found to have more knowledge about AI than males. The qualification level and rank were not significant factors for knowledge of AI, with a P value greater than 0.05. Table 5 shows the binary logistic regression results for the baseline characteristics of the study population and the knowledge of artificial intelligence among medical students. Table 6 shows the binary logistic regression results for the baseline characteristics of the study population and the use of artificial intelligence among medical students.
Table 3.
Descriptive statistics for knowledge of artificial intelligence among medical students
| Value, n (%) | ||
|---|---|---|
| Characteristic | Yes | No |
| Do you know what artificial intelligence is? | 642 (84.3) | 120 (15.7) |
| Do you know about machine learning and deep learning (subtypes of AI)? | 249 (32.7) | 513 (67.3) |
| Do you know about any application of AI in the medical field? | 330 (43.3) | 432 (56.7) |
| Have you ever been taught about Artificial intelligence in medical school? | 138 (18.1) | 624 (81.9) |
| Do you know about the application of AI in radiology? | 186 (24.4) | 576 (75.6) |
| Do you know about the application of AI in the pathology field? | 165 (21.7) | 597 (78.3) |
AI, artificial intelligence.
Table 4.
Knowledge of AI based on age, sex, and qualification level among medical students
| Knowledge of artificial intelligence, n (%) | |||
|---|---|---|---|
| Characteristic | Good, 315 (41.3) | Poor, 447 (58.7) | P |
| Age | |||
| <20 years | 54 (7.1) | 75 (9.8) | < 0.001 |
| 20–22 years | 60 (7.9) | 162 (21.3) | |
| 23–25 years | 175 (23.0) | 177 (23.2) | |
| >25 years | 26 (3.4) | 33 (4.3) | |
| Sex | |||
| Male | 72 (9.4) | 75 (9.8) | 0.023 |
| Female | 243 (31.9) | 372 (48.8) | |
| Educational level | |||
| 1st Grade | 36 (4.7) | 60 (7.9) | < 0.001 |
| 2nd Grade | 30 (3.9) | 78 (10.2) | |
| 3rd Grade | 33 (4.3) | 63 (8.3) | |
| 4th Grade | 72 (9.4) | 99 (13.0) | |
| 5th Grade | 48 (12.6) | 72 (9.4) | |
| 6th Grade | 96 (12.6) | 75 (9.8) | |
AI, artificial intelligence.
Table 5.
Binary logistic regression between baseline characteristics of the study population and knowledge of artificial intelligence among medical students
| Characteristic | Value | P | Odds ratio | Lower | Upper |
|---|---|---|---|---|---|
| Age | <20 years | 0.000 | Reference | — | — |
| 20–22 years | 0.000 | 4.074 | 1.932 | 8.590 | |
| 23–25 years | 0.047 | 2.325 | 1.013 | 5.336 | |
| >25 years | 0.011 | 3.376 | 1.318 | 8.648 | |
| Sex | Male | 0.021 | 0.630 | 0.425 | 0.932 |
| Female | Reference | — | — | — | |
| Educational level | 1st Grade | 0.007 | Reference | — | — |
| 2nd Grade | 0.901 | 1.041 | 0.551 | 1.966 | |
| 3rd Grade | 0.054 | 0.422 | 0.175 | 1.014 | |
| 4th Grade | 0.021 | 0.366 | 0.157 | 0.857 | |
| 5th Grade | 0.072 | 0.440 | 0.180 | 1.077 | |
| 6th Grade | 0.001 | 0.234 | 0.096 | 0.572 | |
| Constant | 0.105 | 1.443 | — | — |
Table 6.
Binary logistic regression between baseline characteristics of the study population and artificial intelligence practices among medical students
| Characteristic | Value | P | Odds ratio | Lower | Upper |
|---|---|---|---|---|---|
| Age | <20 years | 0.367 | Reference | — | — |
| 20–22 years | 0.111 | 1.670 | 0.889 | 3.135 | |
| 23–25 years | 0.219 | 1.586 | 0.760 | 3.313 | |
| >25 years | 0.133 | 1.944 | 0.817 | 4.626 | |
| Sex | Male | 0.000 | 0.451 | 0.291 | 0.698 |
| Female | Reference | — | — | — | |
| Educational level | 1st Grade | 0.002 | Reference | — | — |
| 2nd Grade | 0.287 | 1.403 | 0.752 | 2.616 | |
| 3rd Grade | 0.311 | 1.488 | 0.689 | 3.214 | |
| 4th Grade | 0.186 | 1.654 | 0.785 | 3.483 | |
| 5th Grade | 0.652 | 0.831 | 0.371 | 1.859 | |
| 6th Grade | 0.225 | 0.606 | 0.270 | 1.362 | |
| Constant | 0.000 | 0.387 | — | — |
Attitude toward AI
Concerning attitudes toward AI in the health sector, 237 (31.1%) individuals strongly agreed, and 366 (48.0%) agreed that AI is essential in the medical field. Approximately 108 (14.2%) had no opinion, with the majority being females. Additionally, 225 (29.5%) medical students strongly agreed, and 324 (42.5%) agreed that AI aids practitioners in early diagnosis and assessment of disease severity. Conversely, 162 (21.3%) individuals had no opinion. For pathology, 165 (21.7%) medical students strongly agreed, 303 (39.8%) doctors agreed that AI is essential for diagnostic techniques, and 234 (30.7%) individuals had no opinion. Table 7 presents descriptive statistics for attitudes toward artificial intelligence among medical students, covering AI applications in radiology, curriculum inclusion in residency training, AI as a practitioner’s aid, and concerns about AI as a burden or replacement for physicians. While many believe that an appropriate budget should be allocated for promoting AI in the health sector, some disagree. The correlations of attitudes toward AI essentialness in the medical field with variables such as gender, lack of curriculum, and qualification level, along with the odds ratio, are presented in Table 8. These findings indicate that lack of curriculum is a significant factor (p < 0.05), while gender has no significant effect on attitudes. Table 9 displays the binary logistic regression results between the baseline characteristics of the study population and attitudes toward artificial intelligence among medical students.
Table 7.
Descriptive statistics for attitudes toward artificial intelligence among medical students
| Value, n (%) | |||||
|---|---|---|---|---|---|
| Characteristic | Strongly agree | Agree | Don’t know | Disagree | Strongly disagree |
| Do you believe AI is essential in the medical field? | 237 (31.1) | 366 (48.0) | 108 (14.2) | 42 (5.5) | 9 (1.2) |
| Do you think AI should be included in the curriculum in medical school as well as specialist training? | 234 (30.7) | 339 (44.5) | 117 (15.4) | 48 (6.3) | 24 (3.1) |
| Do you think that AI aids practitioners in early diagnosis and assessment of the severity of disease? | 225 (29.5) | 324 (42.5) | 162 (21.3) | 36 (4.7) | 15 (2.0) |
| Do you believe that AI will replace physicians in the future? | 102 (13.4) | 138 (18.1) | 204 (26.8) | 180 (23.6) | 138 (18.1) |
| Do you believe AI is very essential in the field of radiology? | 204 (26.8) | 306 (40.2) | 195 (25.6) | 48 (6.3) | 9 (1.2) |
| Do You believe AI is essential in the field of Pathology? | 165 (21.7) | 303 (39.8) | 234 (30.7) | 51 (6.7) | 9 (1.2) |
| .Do you believe AI would be a burden for practitioners? | 84 (11.0) | 192 (25.2) | 327 (42.9) | 111 (14.6) | 48 (6.3) |
| Do you believe AI would increase the percentage of errors in diagnosis? | 99 (13.0) | 186 (24.4) | 276 (36.2) | 150 (19.7) | 51 (6.7) |
AI, artificial intelligence.
Table 8.
The attitudes toward AI were based on age, sex, and qualification level among medical students
| Attitude of artificial intelligence, n (%) | |||
|---|---|---|---|
| Characteristic | Favourable, 543 (71.3) | Unfavourable, 219 (28.7) | P |
| Age | |||
| <20 years | 99 (13.0) | 30 (3.9) | 0.001 |
| 20–22 years | 138 (18.1) | 84 (11.0) | |
| 23–25 years | 256 (33.6) | 96 (12.6) | |
| >25 years | 50 (6.6) | 9 (1.2) | |
| Sex | |||
| Male | 117 (15.4) | 30 (3.9) | 0.007 |
| Female | 426 (55.9) | 189 (24.8) | |
| Educational level | |||
| 1st Grade | 78 (10.2) | 18 (2.4) | 0.032 |
| 2nd Grade | 66 (8.7) | 42 (5.5) | |
| 3rd Grade | 66 (8.7) | 30 (3.9) | |
| 4th Grade | 120 (15.7) | 51 (6.7) | |
| 5th Grade | 84 (11.0) | 36 (4.7) | |
| 6th Grade | 129 (16.9) | 42 (5.5) | |
AI, artificial intelligence.
Table 9.
Binary logistic regression between baseline characteristics of the study population and attitudes toward artificial intelligence among medical students
| Characteristic | Value | P | Odds ratio | Lower | Upper |
|---|---|---|---|---|---|
| Age | <20 years | 0.034 | Reference | — | — |
| 20–22 years | 0.035 | 2.036 | 1.053 | 3.938 | |
| 23–25 years | 0.425 | 1.376 | 0.629 | 3.013 | |
| >25 years | 0.714 | 0.828 | 0.301 | 2.273 | |
| Sex | Male | 0.048 | 0.630 | 0.399 | 0.996 |
| Female | Reference | — | — | — | |
| Educational level | 1st Grade | 0.325 | Reference | ||
| 2nd Grade | 0.037 | 2.054 | 1.043 | 4.045 | |
| 3rd Grade | 0.607 | 1.246 | 0.539 | 2.876 | |
| 4th Grade | 0.501 | 1.322 | 0.586 | 2.984 | |
| 5th Grade | 0.422 | 1.426 | 0.599 | 3.394 | |
| 6th Grade | 0.680 | 1.205 | 0.497 | 2.919 | |
| Constant | .000 | 0.225 | — | — |
Practices of AI
Table 10 provides descriptive statistics for the practice of artificial intelligence among medical students. Only 267 (35%) had ever practically applied AI, and all agreed that it facilitated their respective tasks. Conversely, 90 (11.8%) individuals had never applied AI in any task. Notably, many surgeons have practical experience with AI in radiology, utilizing X-ray, CT, and MRI modalities for diagnostic and research purposes. A significant majority, 543 (71.3%), of the individuals expressed readiness to practically apply AI in the future, while 159 (20.9%) did not provide a clear opinion on working with AI in the future. Table 11 outlines the correlation between current AI practices and different variables, including odds ratios (ORs), revealing that a lack of curriculum and gender are significant factors affecting AI practices, with p values less than 0.05. Table 6 presents binary logistic regression results relating to the baseline characteristics of the study population and the practice of artificial intelligence among medical students.
Table 10.
Descriptive statistics for the practice of artificial intelligence among medical students
| Value, n (%) | |||
|---|---|---|---|
| Characteristic | Yes | No | Never applied |
| Application AI technology in any field? | 267 (35.0) | 405 (53.2) | 90 (11.8) |
| Did AI make your task easy? | 441 (57.9) | 42 (5.5) | 279 (36.6) |
| Physician role is important in application and evaluation? | 639 (83.9) | 18 (2.4) | 105 (13.8) |
| Would you like to work on AI in future? | 543 (71.3) | 60 (7.9) | 159 (20.9) |
AI, artificial intelligence.
Table 11.
The practice of AI was based on age, sex, and qualification level among medical students
| Characteristic | Practice of artificial intelligence, n (%) | P | |
|---|---|---|---|
| High; 489 (64.2) | Low; 273 (35.8) | ||
| Age | |||
| <20 years | 91 (11.9) | 38 (5.0) | 0.016 |
| 20–22 years | 124 (16.3) | 98 (12.9) | |
| 23–25 years | 233 (30.6) | 119 (15.6) | |
| >25 years | 41 (5.4) | 18 (2.4) | |
| Sex | |||
| Male | 111 (14.6) | 36 (4.7) | 0.001 |
| Female | 378 (49.6) | 237 (31.1) | |
| Educational level | |||
| 1st Grade | 69 (9.1) | 27 (3.5) | < 0.001 |
| 2nd Grade | 65 (8.5) | 43 (5.6) | |
| 3rd Grade | 54 (7.1) | 42 (5.5) | |
| 4th Grade | 91 (11.9) | 80 (10.5) | |
| 5th Grade | 83 (10.9) | 37 (4.9) | |
| 6th Grade | 127 (16.7) | 44 (5.8) | |
AI, artificial intelligence.
Discussion
AI has revolutionized healthcare delivery10, as it allows tasks to be completed efficiently and accurately through the use of algorithms based on human intelligence11–16. Machine learning, a subtype of AI, relies on algorithms that require pre-calculated data and feature input, while deep learning is more advanced and skips the need for pre-designed classification and features8,16.
During the recent armed conflict, healthcare facilities and workers faced a crisis due to the reallocation of resources. In developing countries such as Sudan, there is an urgent need for patient-centred AI tools to assist physicians in diagnosis and treatment5,17. Sudan is still in the initial phases of AI introduction and implementation, and little native data are available.
Our research focused on the population of medical students in Sudan and evaluated different aspects of the knowledge, attitudes, and practices of AI in the field of medicine. A total of 762 medical students participated in the study; 19.3% were males, and 80.7%were females, resulting in a male-to-female ratio of 0.23. Of the 762 participants, 642 (84.3%) had basic knowledge of AI subtypes, but only 249 (32.7%) had knowledge about the subtypes ML and DL.
Most of the individuals with knowledge of AI were males, and almost 432 (56.7%) participants were not aware of the practical application of AI in medicine. This suggests that Sudanese medical students, despite having basic AI knowledge, lack awareness of its practical implications. Three-fourths (74.4%) of the study population acknowledged the importance of AI in modern diagnostics and considered it essential in advanced medicine.
This aligns with a study conducted in the UK’s medical institutes, where a three-fourths majority of students acknowledged the essential role of AI in healthcare, similar to our results18. In our study, 13.3% of participants agreed that implementing AI in medicine would reduce diagnostic errors, which is consistent with the findings of a study in India in which 89% of students expressed optimistic views about AI implementation19.
Moreover, 192 (25.2%) medical students acknowledged that AI could soon serve as a practitioner’s aid. Most of them do not consider AI a physician’s replacement but rather a diagnostic aid. The majority (44.5%) also agreed on including an AI curriculum in medical schools, consistent with results from studies in the USA20,21 and Sudan1.
The major causes of AI implementation failure in Sudan, as expressed by participants, include lack of adequate knowledge and awareness, disinterest in the field, poor training, no curriculum, low financial resources, and lack of technological advancements in our country22–25. Furthermore, 219 (26.5%) students considered AI essential in advanced radiology, with many agreeing on its importance in the COVID-19 pandemic due to the reallocation of healthcare resources.
Conclusion
Our results show that most doctors and medical students have basic knowledge about AI but lack detailed knowledge about its applications in the medical field. Overall, the attitudes of doctors and medical students toward the need for AI in the medical field are satisfactory, and the majority consider AI essential in radiology, pathology, and other medical fields.
Most individuals agreed with the idea of including an AI curriculum in medical colleges and postgraduate residency training, considering it a physician’s aid in early diagnosis and error reduction rather than a replacement. Only a minority (11.3%) of the participants had practical applications of AI in the medical field for diagnostic and research purposes, primarily in radiology (X-ray, CT, and MRI) and pathology (histopathological tests and culture and sensitivity testing).
Our research provides unique insights into the extent of knowledge, attitudes, and practices of students and doctors working in different institutes in Sudan and the factors affecting these attitudes. Since there is a need to address the willingness to adapt innovations and increase awareness of AI applications in current medicine, it is recommended that an appropriate AI curriculum be designed and implemented in the medical field in Sudan, as AI will play a progressively larger and more important role in the future of medicine and healthcare. Senior decision-makers should aim to develop policies to bring about innovations in the field.
Ethical approval
All participants could withdraw from the cross-sectional research at any time, and participation is completely voluntary. The participant could not be identified since the study did not provide names or e-mails. Each participant’s identity is therefore wholly protected during the investigation. Alzaiem Alazhari University ethics committee granted its permission and gave the research the go-ahead, and the Helsinki Declaration carried out the study.
Consent
All participants could withdraw from the cross-sectional research at any time, and participation is completely voluntary. The participant could not be identified since the study did not provide names or e-mails. Each participant’s identity is therefore wholly protected during the investigation. Alzaiem Alazhari University ethics committee granted its permission and gave the research the go-ahead, and the Helsinki Declaration carried out the study
Sources of funding
No funding.
Author contribution
The author contributed to this work.
Conflicts of interest disclosure
The author declares no conflicts of interest.
Research registration unique identifying number (UIN)
research study did not involving human subjects so we do not register it.
Guarantor
Mohammed Hammad Jaber Amin1.
Data availability statement
Data are available upon reasonable request.
Provenance and peer review
Not commissioned, externally peer-reviewed.
Footnotes
Sponsorships or competing interests that may be relevant to content are disclosed at the end of this article.
Published online 24 April 2024
References
- 1.Abid S, Awan B, Ismail T, et al. Artificial intelligence: medical student s attitude in the district of Peshawar, Pakistan, Pak. J Public Health 2019;9:19–21. [Google Scholar]
- 2.Waymel Q, Badr S, Demondion X, et al. Impact of the rise of artificial intelligence in radiology: what do radiologists think. Diagn Interv Imaging 2019;100:327–336. [DOI] [PubMed] [Google Scholar]
- 3.Ooi SK, Makmur A, Soon AY, et al. Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes:a national multiprogramme survey. Singapore Med J 2021;62:126. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Wahl B, Cossy-Gantner A, Germann S, et al. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health 2018;3:e000798. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Xiang Y, Zhao L, Liu Z, et al. Implementation of artificial intelligence in medicine: status analysis and development suggestions. Artif Intell Med 2020;102:101780. [DOI] [PubMed] [Google Scholar]
- 6.Shinners L, Aggar C, Grace S, et al. Exploring healthcare professionals’ perceptions of artificial intelligence: validating a questionnaire using the e-Delphi method. Digit Health 2021;7:20552076211003433. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Sarwar S, Dent A, Faust K, et al. Physician perspectives on the integration of artificial intelligence into diagnostic pathology. NPJ Digit Med 2019;2:1–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Guo J, Li B. The application of medical artificial intelligence technology in rural areas of developing countries. Health Equity 2018;2:174–181. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Sit C, Srinivasan R, Amlani A, et al. Attitudes and perceptions of UK medical students towardartificial intelligence and radiology: a multicenter survey. Insights, Imaging 2020;11:14. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Yan Y, Zhang JW, Zang GY, et al. The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine? J Geriatr Cardiol 2019;16:585. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Shimizu H, Nakayama KI. Artificial intelligence in oncology. Cancer Sci 2020;111:1452. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hassabis D, Kumaran D, Summerfield C, et al. Neuroscience-inspired artificial intelligence. Neuron 2017;95:245–258. [DOI] [PubMed] [Google Scholar]
- 13.Hosny A, Parmar C, Quackenbush J, et al. Artificial intelligence in radiology. Nat Rev Cancer 2018;18:500–510. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Kahn CE, Jr. Artificial intelligence in radiology: decision support systems. Radiographics 1994;14:849–861. [DOI] [PubMed] [Google Scholar]
- 15.Li CX, Shen CB, Xue K, et al. Artificial intelligence in dermatology: past, present, and future. Chin Med J 2019;132:2017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Du-Harpur X, Watt FM, Luscombe NM, et al. What is AI? Applications of artificial intelligence to dermatology. Br J Dermatol 2020;183:423–430. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ting DS, Pasquale LR, Peng L, et al. Artificial intelligence and deep learning in ophthalmology. Br J Ophthalmol 2019;103:167–175. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Moraru AD, Costin D, Moraru RL, et al. Artificial intelligence and deep learning in ophthalmology-present and future. Exp Ther Med 2020;20:3469–3473. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Chan KS, Zary N. Applications and challenges of implementing artificial intelligence in medical education: integrative review. JMIR Med EdU 2019;5:e13930. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Laï MC, Brian M, Mamzer MF. Perceptions of artificial intelligence in healthcare: findings from a qualitative survey study among actors in France. J Transl Med 2020;18:1–3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Rodela TT, Tasnim S, Mazumder H, et al. The Economic Impacts of Coronavirus Disease (COVID-19) in Developing Countries SocArXiv. Wygpk. Preprint, 2020. 10.31219/osf.io/wygpk [DOI] [Google Scholar]
- 22.Castagno S, Khalifa M. Perceptions of artificial intelligence among healthcare staff: a qualitative survey study. Front Artif Intell 2020;3:84. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Jindal A, Bansal M. Knowledge and education about artificial intelligence among medical students from teaching institutions in India: a brief survey. MedEdPublish 2020;16:9. [Google Scholar]
- 24.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] [PMC free article] [PubMed] [Google Scholar]
- 25.Mathew G, Agha R.for the STROCSS Group. STROCSS 2021 . Strengthening the Reporting of cohort, cross-sectional and case-control studies in Surgery. Int J Surg 2021;96:106165. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
Data are available upon reasonable request.