Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland

Blease, Charlotte; Kharko, Anna; Bernstein, Michael H.; Bradley, Colin; Houston, Muiris; Walsh, Ian; Hägglund, Maria; DesRoches, Catherine M.; Mandl, Kenneth D.

doi:10.1136/bmjhci-2021-100480

Cited by 38 publications

(42 citation statements)

References 15 publications

(30 reference statements)

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“…Parallel to the lack of education and the feeling of incompetence, the students thought that AI should be part of the medical training, as was revealed in the other studies as well [15][16][17]19]. Although there are some recommendations in the literature for curricular objectives [7,8,11,14,32,34], Lee et al concluded in their review that almost all curriculum recommendations lacked specific learning outcomes and were not based on a particular education theory [11].…”

Section: What To Teach and Howmentioning

confidence: 98%

“…According to Grunhut et al, the curriculum should be incorporated through previously proven methods in similar drastic curricular changes [14]. Cross-disciplinary courses, small-group sessions, experiential learning/providing opportunities for students to work directly with AI tools, e-modules, interactive case-based workshops, selflearning modules, and student site visits to learn about the creation of AI products are among the suggested methods to teach students AI basics and improve their understanding of AI ethics [11,19,34]. Multidisciplinarity is regarded as crucial while implementing those learning strategies.…”

Section: What To Teach and Howmentioning

confidence: 99%

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Artificial intelligence in medical education: a cross-sectional needs assessment

et al. 2022

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Background As the information age wanes, enabling the prevalence of the artificial intelligence age; expectations, responsibilities, and job definitions need to be redefined for those who provide services in healthcare. This study examined the perceptions of future physicians on the possible influences of artificial intelligence on medicine, and to determine the needs that might be helpful for curriculum restructuring. Methods A cross-sectional multi-centre study was conducted among medical students country-wide, where 3018 medical students participated. The instrument of the study was an online survey that was designed and distributed via a web-based service. Results Most of the medical students perceived artificial intelligence as an assistive technology that could facilitate physicians’ access to information (85.8%) and patients to healthcare (76.7%), and reduce errors (70.5%). However, half of the participants were worried about the possible reduction in the services of physicians, which could lead to unemployment (44.9%). Furthermore, it was agreed that using artificial intelligence in medicine could devalue the medical profession (58.6%), damage trust (45.5%), and negatively affect patient-physician relationships (42.7%). Moreover, nearly half of the participants affirmed that they could protect their professional confidentiality when using artificial intelligence applications (44.7%); whereas, 16.1% argued that artificial intelligence in medicine might cause violations of professional confidentiality. Of all the participants, only 6.0% stated that they were competent enough to inform patients about the features and risks of artificial intelligence. They further expressed that their educational gaps regarding their need for “knowledge and skills related to artificial intelligence applications” (96.2%), “applications for reducing medical errors” (95.8%), and “training to prevent and solve ethical problems that might arise as a result of using artificial intelligence applications” (93.8%). Conclusions The participants expressed a need for an update on the medical curriculum, according to necessities in transforming healthcare driven by artificial intelligence. The update should revolve around equipping future physicians with the knowledge and skills to effectively use artificial intelligence applications and ensure that professional values and rights are protected.

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Section: What To Teach and Howmentioning

confidence: 98%

Section: What To Teach and Howmentioning

confidence: 99%

Artificial intelligence in medical education: a cross-sectional needs assessment

et al. 2022

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“…However, AI also presents certain limitations. It has been shown that a possible limitation would be related to the knowledge about artificial intelligence and machine learning of students [ 42 ]. In addition, it has been observed that some students may be reluctant to accept these technological developments as they consider that they have greater learning with a teacher interacting face-to-face and not on-line, being interaction and error correction one of the basic learning points for them [ 43 , 44 ].…”

Section: Discussionmentioning

confidence: 99%

Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills

Suárez

Velasco

García

et al. 2022

IJERPH

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Knowing how to diagnose effectively and efficiently is a fundamental skill that a good dental professional should acquire. If students perform a greater number of clinical cases, they will improve their performance with patients. In this sense, virtual patients with artificial intelligence offer a controlled, stimulating, and safe environment for students. To assess student satisfaction after interaction with an artificially intelligent chatbot that recreates a virtual patient, a descriptive cross-sectional study was carried out in which a virtual patient was created with artificial intelligence in the form of a chatbot and presented to fourth and fifth year dental students. After several weeks interacting with the AI, they were given a survey to find out their assessment. A total of 193 students participated. A large majority of the students were satisfied with the interaction (mean 4.36), the fifth year students rated the interaction better and showed higher satisfaction values. The students who reached a correct diagnosis rated this technology more positively. Our research suggests that the incorporation of this technology in dental curricula would be positively valued by students and would also ensure their training and adaptation to new technological developments.

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“…In fact, there is some debate over whether AI would increase or decrease workload in radiology departments [16]. Many medical students do not consider radiology a future career option due to AI's integration [17]. Although there is widespread concern that AI will replace human jobs [18], there seems to be no evidence to support this hypothesis [4].…”

Section: Discussionmentioning

confidence: 99%

Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia

Aldhafeeri

2022

Insights Imaging

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Objectives This study aimed to gain insight into radiographers’ views on the application of artificial intelligence (AI) in Saudi Arabia by conducting a qualitative investigation designed to provide recommendations to assist radiographic workforce improvement. Materials and methods We conducted an online cross-sectional online survey of Saudi radiographers regarding perspectives on AI implementation, job security, workforce development, and ethics. Results In total, 562 valid responses were received. Most respondents (90.6%) believed that AI was the direction of diagnostic imaging. Among the respondents, 88.5% stated that AI would improve the accuracy of diagnosis. Some challenges in implementing AI in Saudi Arabia include the high cost of equipment, inadequate knowledge, radiologists’ fear of losing employment, and concerns related to potential medical errors and cyber threats. Conclusion Radiographers were generally positive about introducing AI to radiology departments. To integrate AI successfully into radiology departments, radiographers need training programs, transparent policies, and motivation.

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Machine learning in medical education: a survey of the experiences and opinions of medical students in Ireland

Cited by 38 publications

References 15 publications

Artificial intelligence in medical education: a cross-sectional needs assessment

Artificial intelligence in medical education: a cross-sectional needs assessment

Using a Virtual Patient via an Artificial Intelligence Chatbot to Develop Dental Students’ Diagnostic Skills

Perspectives of radiographers on the emergence of artificial intelligence in diagnostic imaging in Saudi Arabia

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