Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Skg, Ooi; Makmur, Andrew; Ayq, Soon; Fook‐Chong, Stephanie; Liew, Charlene; Sy, Sia; Yh, Ting; Cy, Lim

doi:10.11622/smedj.2019141

Cited by 74 publications

(87 citation statements)

References 26 publications

(33 reference statements)

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“…Additionally, radiologists, radiology residents, medical students have increasingly recognized the need for a basic understanding of artificial intelligence. 4 , 5 , 6 Despite these impending changes, medical education designed for future radiologists have only recently begun. 7 , 8 , 9 …”

Section: Introductionmentioning

confidence: 99%

AI-RADS: An Artificial Intelligence Curriculum for Residents

et al. 2021

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Rationale and Objectives: Artificial intelligence (AI) has rapidly emerged as a field poised to affect nearly every aspect of medicine, especially radiology. A PubMed search for the terms "artificial intelligence radiology" demonstrates an exponential increase in publications on this topic in recent years. Despite these impending changes, medical education designed for future radiologists have only recently begun. We present our institution's efforts to address this problem as a model for a successful introductory curriculum into artificial intelligence in radiology titled AI-RADS. Materials and Methods: The course was based on a sequence of foundational algorithms in AI; these algorithms were presented as logical extensions of each other and were introduced as familiar examples (spam filters, movie recommendations, etc.). Since most trainees enter residency without computational backgrounds, secondary lessons, such as pixel mathematics, were integrated in this progression. Didactic sessions were reinforced with a concurrent journal club highlighting the algorithm discussed in the previous lecture. To circumvent often intimidating technical descriptions, study guides for these papers were produced. Questionnaires were administered before and after each lecture to assess confidence in the material. Surveys were also submitted at each journal club assessing learner preparedness and appropriateness of the article. Results: The course received a 9.8/10 rating from residents for overall satisfaction. With the exception of the final lecture, there were significant increases in learner confidence in reading journal articles on AI after each lecture. Residents demonstrated significant increases in perceived understanding of foundational concepts in artificial intelligence across all mastery questions for every lecture. Conclusion: The success of our institution's pilot AI-RADS course demonstrates a workable model of including AI in resident education.

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Section: Introductionmentioning

confidence: 99%

AI-RADS: An Artificial Intelligence Curriculum for Residents

et al. 2021

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“…This implies that updated regulatory policies and continuing education of all users (including adequate information to patients about the purposes, rights, and legal terms related to the use of AI for their health management) should be promoted, as AI systems are poised to become more widely available, complex and powerful. To this purpose, it is noteworthy that the majority of Singaporean radiology residents joining a national multiprogram survey thought that since AI will drastically change radiology practice, AI/ML knowledge should be taught during residency (84.8% of survey participants), and this was as important as imaging physics and clinical skills/knowledge curricula (80.0 and 72.8%, respectively) ( Ooi et al, 2021 ). From a psychological standpoint, it has been observed that openness to experience is associated with higher trust toward robots and AI, as well as having a degree in technology or engineering, exposure to robots online, and robot use self-efficacy ( Oksanen et al, 2020 ), highlighting the importance of technology knowledge in addition to personal differences in building AI confidence.…”

Section: Artificial Intelligence and Humansmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

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Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

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“…One survey showed that resident physicians and medical students desired AI-related training but that only a few had opportunities to participate in personal-level data science or machine learning courses. 60 Moreover, medical workers in less developed areas have great concerns about whether they will be replaced by AI in the future. 61 Possible solutions…”

Section: Societal Acceptancementioning

confidence: 99%

Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies

Jiang

et al. 2021

J Int Med Res

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Recent advancements in the field of artificial intelligence have demonstrated success in a variety of clinical tasks secondary to the development and application of big data, supercomputing, sensor networks, brain science, and other technologies. However, no projects can yet be used on a large scale in real clinical practice because of the lack of standardized processes, lack of ethical and legal supervision, and other issues. We analyzed the existing problems in the field of artificial intelligence and herein propose possible solutions. We call for the establishment of a process framework to ensure the safety and orderly development of artificial intelligence in the medical industry. This will facilitate the design and implementation of artificial intelligence products, promote better management via regulatory authorities, and ensure that reliable and safe artificial intelligence products are selected for application.

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Attitudes toward artificial intelligence in radiology with learner needs assessment within radiology residency programmes: a national multi-programme survey

Cited by 74 publications

References 26 publications

AI-RADS: An Artificial Intelligence Curriculum for Residents

AI-RADS: An Artificial Intelligence Curriculum for Residents

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

Opportunities and challenges of artificial intelligence in the medical field: current application, emerging problems, and problem-solving strategies

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