Knowledge, attitude, and perception of Arab medical students towards artificial intelligence in medicine and radiology: A multi-national cross-sectional study

OBJECTIVES

We aimed to assess undergraduate medical students' knowledge, attitude, and perception regarding artificial intelligence (AI) in medicine.

METHODS

A multi-national, multi-center cross-sectional study was conducted from March to April 2022, targeting undergraduate medical students in nine Arab countries. The study utilized a web-based questionnaire, with data collection carried out with the help of national leaders and local collaborators. Logistic regression analysis was performed to identify predictors of knowledge, attitude, and perception among the participants. Additionally, cluster analysis was employed to identify shared patterns within their responses.

RESULTS

Of the 4492 students surveyed, 92.4% had not received formal AI training. Regarding AI and deep learning (DL), 87.1% exhibited a low level of knowledge. Most students (84.9%) believed AI would revolutionize medicine and radiology, with 48.9% agreeing that it could reduce the need for radiologists. Students with high/moderate AI knowledge and training had higher odds of agreeing to endorse AI replacing radiologists, reducing their numbers, and being less likely to consider radiology as a career compared to those with low knowledge/no AI training. Additionally, the majority agreed that AI would aid in the automated detection and diagnosis of pathologies.

CONCLUSIONS

Arab medical students exhibit a notable deficit in their knowledge and training pertaining to AI. Despite this, they hold a positive perception of AI implementation in medicine and radiology, demonstrating a clear understanding of its significance for the healthcare system and medical curriculum.

CLINICAL RELEVANCE STATEMENT

This study highlights the need for widespread education and training in artificial intelligence for Arab medical students, indicating its significance for healthcare systems and medical curricula.

KEY POINTS

• Arab medical students demonstrate a significant knowledge and training gap when it comes to using AI in the fields of medicine and radiology. • Arab medical students recognize the importance of integrating AI into the medical curriculum. Students with a deeper understanding of AI were more likely to agree that all medical students should receive AI education. However, those with previous AI training were less supportive of this idea. • Students with moderate/high AI knowledge and training displayed increased odds of agreeing that AI has the potential to replace radiologists, reduce the demand for their services, and were less inclined to pursue a career in radiology, when compared to students with low knowledge/no AI training.

Inclusion of MCQs written by radiology residents in their annual evaluation: innovative method to enhance resident's empowerment?

AIM

We hypothesized that multiple-choice questions written by radiology residents (MCQ^resident) for their weekly case presentations during radiology staff meetings could be used along with multiple-choice questions written by radiology teachers (MCQ^teacher) for their annual evaluation. The current prospective study aimed at determining the educational characteristics of MCQ^resident and at comparing them with those of MCQ^teacher.

METHODS

Fifty-one radiology residents of the first to the fifth year of training took the 2017 exam that contained 58 MCQ^resident and 63 of MCQ^teacher. The difficulty index, the discrimination power, and the distractor's quality were calculated in the two series of MCQs and were compared by using Student t test. Two radiologists classified each MCQ according to Bloom's taxonomy and frequencies of required skills of both MCQ series were compared.

RESULTS

The mean ± SD difficulty index of MCQ^resident was statistically significantly higher than that of MCQ^teacher (0.81 ± 0.1 vs 0.64 ± 0.2; p < 0.0001). The mean ± SD discrimination index of MCQ^resident was statistically significantly higher than that of MCQ^teacher (0.34 ± 0.2 vs 0.23 ± 0.2; p = 0.0007). The mean number of non-functional distractors per MCQ^resident was statistically significantly higher than that per MCQ^teacher (1.36 ± 0.9 vs 0.86 ± 0.9; p = 0.0031). MCQ^resident required recalling skills more frequently than MCQ^teacher which required more advanced skills to obtain a correct answer.

CONCLUSIONS

Educational characteristics of MCQ^resident differ from those of MCQ^teacher. This study highlights the characteristics to optimize the writing of MCQs by radiology residents.

Taking the professional leap from trainee to attending

The transition from trainee to attending can be one of the most difficult transitions in a radiologist's career. Even if a recent graduate is clinically competent, their future successful integration as part of a practice relies on more than just performing correct image interpretation. This article addresses several strategies to help new hires successfully transition to their new practice and become permanent, contributing members of their team.

Toward Data-Driven Radiology Education-Early Experience Building Multi-Institutional Academic Trainee Interpretation Log Database (MATILDA)

The residency review committee of the Accreditation Council of Graduate Medical Education (ACGME) collects data on resident exam volume and sets minimum requirements. However, this data is not made readily available, and the ACGME does not share their tools or methodology. It is therefore difficult to assess the integrity of the data and determine if it truly reflects relevant aspects of the resident experience. This manuscript describes our experience creating a multi-institutional case log, incorporating data from three American diagnostic radiology residency programs. Each of the three sites independently established automated query pipelines from the various radiology information systems in their respective hospital groups, thereby creating a resident-specific database. Then, the three institutional resident case log databases were aggregated into a single centralized database schema. Three hundred thirty residents and 2,905,923 radiologic examinations over a 4-year span were catalogued using 11 ACGME categories. Our experience highlights big data challenges including internal data heterogeneity and external data discrepancies faced by informatics researchers.

Radiology education in Europe: Analysis of results from 22 European countries

AIM

To assess the state of radiology education across Europe by means of a survey study.

METHODS

A comprehensive 23-item radiology survey was distributed via email to the International Society of Radiology members, national radiological societies, radiologists and medical physicists. Reminders to complete the survey were sent and the results were analyzed over a period of 4 mo (January-April 2016). Survey questions include length of medical school and residency training; availability of fellowship and subspecialty training; number of residency programs in each country; accreditation pathways; research training; and medical physics education. Descriptive statistics were used to analyze and summarize data.

RESULTS

Radiology residency training ranges from 2-6 years with a median of 5 years, and follows 1 year of internship training in 55% (12 out of 22) European countries. Subspecialty fellowship training is offered in 55% (12 out of 22) European countries. Availability for specialization training by national societies is limited to eight countries. For nearly all respondents, less than fifty percent of radiologists travel abroad for specialization. Nine of 22 (41%) European countries have research requirements during residency. The types of certifying exam show variation where 64% (14 out of 22) European countries require both written and oral boards, 23% (5 out of 22) require oral examinations only, and 5% (1 out of 22) require written examinations only. A degree in medical physics is offered in 59% (13 out of 22) European countries and is predominantly taught by medical physicists. Nearly all respondents report that formal examinations in medical physics are required.

CONCLUSION

Comparative learning experiences across the continent will help guide the development of comprehensive yet pragmatic infrastructures for radiology education and collaborations in radiology education worldwide.

Musculoskeletal imaging insight 2015: Kenya

Over the past 6 years the International Skeletal Society (ISS) outreach programs have become popular amongst the various radiology organizations in sub-Saharan Africa. So much so that that the ISS outreach is now routinely expected to participate in many of the international radiology conferences in that part of the world. The organizational planning for an outreach visit to Kenya took place over a 3-year period. Eventually a double-headed event; the seventh and eighth sub-Saharan outreach efforts were organized in Nairobi and in Mombasa, Kenya. The Nairobi outreach was an educational course on musculoskeletal imaging at the University of Nairobi and the Aga Khan University in Nairobi from 26 to 28 May 2015. The Mombasa outreach was organized in collaboration with the African Society of Radiology (ASR) at their annual meeting in Mombasa from 30 May to 2 June 2015.