To assess undergraduate healthcare 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 should really take the lead in educating students about these emerging technologies. Respondents’ anonymity was ensured. A net-based questionnaire was created making use of SurveyMonkey, and was sent out to students at 3 main medical schools. It consisted of numerous sections aiming to evaluate the students’ prior knowledge of AI in radiology and beyond, as properly as their attitude towards AI in radiology particularly and in medicine in general. 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 enhance radiology (77% and 86%), while disagreeing with statements that human radiologists will be replaced (83%). Over two-thirds agreed on the need to have for AI to be included in health-related education (71%). In sub-group analyses male and tech-savvy respondents had been much more confident on the rewards of AI and significantly less fearful of these technologies. Around 52% have been aware of the ongoing discussion about AI in radiology and 68% stated that they have been unaware of the technologies involved. Contrary to anecdotes published in the media, undergraduate health-related students do not be concerned that AI will replace human radiologists, and are aware of the prospective applications and implications of AI on radiology and medicine.
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Though-in contrast to GOFAI robots-they include no objective representations of the globe, some of them do construct temporary, topic-centered (deictic) representations. The main aim of situated roboticists in the mid-1980s, such as Rodney Brooks, was to solve/avoid the frame trouble that had bedeviled GOFAI (Pylyshyn 1987). GOFAI planners and robots had to anticipate all possible contingencies, such as the side effects of actions taken by the system itself, if they were not to be defeated by unexpected-possibly seemingly irrelevant-events. Brooks argued that reasoning shouldn’t be employed at all: the technique must just react appropriately, in a reflex style, to specific environmental cues. This was 1 of the factors provided by Hubert Dreyfus (1992) in arguing that GOFAI could not possibly succeed: Intelligence, he mentioned, is unformalizable. But since the general nature of that new proof had to be foreseen, the frame issue persisted. Several methods of implementing nonmonotonic logics in GOFAI were recommended, permitting a conclusion previously drawn by faultless reasoning to be negated by new evidence.
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