Medical Students Attitude Towards Artificial Intelligence: A Multicentre Survey

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To assess undergraduate health-related students’ attitudes towards artificial intelligence (AI) in radiology and medicine. A total of 263 students (166 female, 94 male, median age 23 years) responded to the questionnaire. Radiology really should take the lead in educating students about these emerging technologies. Respondents’ anonymity was ensured. A internet-based questionnaire was designed working with SurveyMonkey, and was sent out to students at 3 key healthcare schools. It consisted of several sections aiming to evaluate the students’ prior knowledge of AI in radiology and beyond, as nicely as their attitude towards AI in radiology particularly and in medicine in common. Respondents agreed that AI could potentially detect pathologies in radiological examinations (83%) but felt that AI would not be able to establish a definite diagnosis (56%). The majority agreed that AI will revolutionise and improve radiology (77% and 86%), though disagreeing with statements that human radiologists will be replaced (83%). More than two-thirds agreed on the want for AI to be integrated in health-related instruction (71%). In sub-group analyses male and tech-savvy respondents have been a lot more confident on the rewards of AI and less fearful of these technologies. Around 52% were conscious of the ongoing discussion about AI in radiology and 68% stated that they had been unaware of the technologies involved. Contrary to anecdotes published in the media, undergraduate medical students do not worry that AI will replace human radiologists, and are aware of the prospective applications and implications of AI on radiology and medicine.

The developments which are now getting known as "AI" arose mostly in the engineering fields linked with low-level pattern recognition and movement manage, and in the field of statistics - the discipline focused on obtaining patterns in data and on generating nicely-founded predictions, tests of hypotheses and choices. Indeed, the famous "backpropagation" algorithm that was rediscovered by David Rumelhart in the early 1980s, and which is now viewed as becoming at the core of the so-called "AI revolution," initial arose in the field of control theory in the 1950s and 1960s. One of its early applications was to optimize the thrusts of the Apollo spaceships as they headed towards the moon. Rather, as in the case of the Apollo spaceships, these suggestions have usually been hidden behind the scenes, and have been the handiwork of researchers focused on certain engineering challenges. Since the 1960s considerably progress has been created, but it has arguably not come about from the pursuit of human-imitative AI.

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