Readiness to Embrace Artificial Intelligence Among Medical Doctors and Students: Questionnaire-Based Study

Abstract

Background Similar to understanding how blood pressure is measured by a sphygmomanometer, physicians will soon have to understand how an artificial intelligence–based application has come to the conclusion that a patient has hypertension, diabetes, or cancer. Although there are an increasing number of use cases where artificial intelligence is or can be applied to improve medical outcomes, the extent to which medical doctors and students are ready to work and leverage this paradigm is unclear. Objective This research aims to capture medical students’ and doctors’ level of familiarity toward artificial intelligence in medicine as well as their challenges, barriers, and potential risks linked to the democratization of this new paradigm. Methods A web-based questionnaire comprising five dimensions—demographics, concepts and definitions, training and education, implementation, and risks—was systematically designed from a literature search. It was completed by 207 participants in total, of which 105 (50.7%) medical doctors and 102 (49.3%) medical students trained in all continents, with most of them in Europe, the Middle East, Asia, and North America. Results The results revealed no significant difference in the familiarity of artificial intelligence between medical doctors and students (P=.91), except that medical students perceived artificial intelligence in medicine to lead to higher risks for patients and the field of medicine in general (P<.001). We also identified a rather low level of familiarity with artificial intelligence (medical students=2.11/5; medical doctors=2.06/5) as well as a low attendance to education or training. Only 2.9% (3/105) of medical doctors attended a course on artificial intelligence within the previous year, compared with 9.8% (10/102) of medical students. The complexity of the field of medicine was considered one of the biggest challenges (medical doctors=3.5/5; medical students=3.8/5), whereas the reduction of physicians’ skills was the most important risk (medical doctors=3.3; medical students=3.6; P=.03). Conclusions The question is not whether artificial intelligence will be used in medicine, but when it will become a standard practice for optimizing health care. The low level of familiarity with artificial intelligence identified in this study calls for the implementation of specific education and training in medical schools and hospitals to ensure that medical professionals can leverage this new paradigm and improve health outcomes.

Classroom Behaviour of Talent Cultivation in Colleges and Universities Supported by Deep Learning Technology

“…In this section, we estimate the usefulness of our preferred strategy in contrast to the previously mentioned methods for Chinese students' data for the given data. Our proposed technique method also matched with other standard techniques artificial intelligence (AI) [26], machine learning (ML) [27], particle swarm optimization-support vector machine (PSO-SVM) [28], and Fuzzy k nearest neighbor (F-KNN) [29]. Several significant disadvantages of artificial intelligence applications, according to AI, are the safety risk associated with submitting critical material to virtualized systems that may require proper security controls [30].…”

“…The proposed method has enhanced accuracy due to the results obtained thus far. Existing works in AI [26], ML [27], PSO-SVM [28], and F-KNN [29] are compared using measures such as precision, F1 score, student performance, and accuracy. As a result, compared to existing works, the proposed CNN-AM performs better.…”

“…The proposed novel convolution neural network with attention mechanism and statistical analysis approaches are functional according to this section. The performance metrics like precision, F1 score, student performance, and accuracy are used to compare the existing works like artificial intelligence (AI) [26], machine learning (ML) [27], particle swarm optimization-support vector machine (PSO-SVM) [28], and Fuzzy k nearest neighbor (F-KNN) [29].…”

Telemedicine and virtual respiratory care in the era of COVID-19

“…[9] Effective implementation strategies, however, are poorly developed, [10] and the potential for AI to replace (as opposed to support) clinical decision-making [11] currently outstrips the trust of many patients and professionals. [12,13] Whilst we are mindful of this wider perspective, in this clinical review we focus on selected examples to provide an expert perspective on the potential of telemedicine to deliver virtual respiratory care. We will consider how the COVID-19 pandemic has had a major influence on this story, promoting implementation of remote ways to monitor the progress of COVID in people at risk, [14][15][16][17][18] highlighting the potential of safely maintaining the care of people with non-communicable respiratory diseases, [19,20] and of supporting self-management during times of lockdown.…”

Medical and Dental Professionals Readiness for Artificial Intelligence for Saudi Arabia Vision 2030

“…It is worth noting that many of these studies were specifically interested in AI use for radiology [26], particularly in Saudi Arabia [37][38][39]. In one multi-continent study, medical doctors and students had low levels of familiarity with AI, and only 2.9-9.8% had attended courses about AI [40]. Results of Saudi Arabian studies among medical doctors and students showed low levels of knowledge about AI despite the desire to learn.…”