Artificial intelligence for precision education in radiology

Duong, Michael Tran; Rauschecker, Andreas M.; Rudie, Jeffrey D.; Chen, Po Hao; Cook, Tessa S.; Bryan, R. Nick; Mohan, Suyash

doi:10.1259/bjr.20190389

Cited by 130 publications

(70 citation statements)

References 64 publications

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“…A number of resources have emerged attempting to specifically address this concern for radiology professionals taught to varying levels of ability. 10 , 11 , 12 , 13 , 14 , 15 , 16 While these resources are tailored to imaging applications, as of writing we have found few examples of formal integration into residency training.…”

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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“…The postlearning score in the AIL group was significantly better than the prelearning score. Thus, the AI-based education system demonstrated its utility in improving trainee performance through quality measures that should be integral to the improvement in medical education, especially with the utilization of AI [7,17,18]. Furthermore, we found that the HipGuide system particularly helped novice students who had lower prelearning scores.…”

Section: Discussionmentioning

confidence: 75%

“…In conventional image teaching, students are expected to first learn diagnostic characteristics from educators and practice afterward. AI in medical education is still in the development stage [2,7,[24][25][26]. AI can empower flipped classroom-like teaching [27,28] and complete the existing bottom-up platforms used to teach radiology [29].…”

Section: Discussionmentioning

confidence: 99%

“…However, owing to time constraints, training opportunities might be compressed, and trainees may not be able to access as many images as their tutors or teachers. Individual variability is thought to substantially affect learning styles [6,7]. Moreover, learning is dictated by the number and diversity of cases encountered, with varying practices and patient mixes.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence-based education assists medical students’ interpretation of hip fracture

Cheng

Chen

et al. 2020

Insights Imaging

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Background With recent transformations in medical education, the integration of technology to improve medical students’ abilities has become feasible. Artificial intelligence (AI) has impacted several aspects of healthcare. However, few studies have focused on medical education. We performed an AI-assisted education study and confirmed that AI can accelerate trainees’ medical image learning. Materials We developed an AI-based medical image learning system to highlight hip fracture on a plain pelvic film. Thirty medical students were divided into a conventional (CL) group and an AI-assisted learning (AIL) group. In the CL group, the participants received a prelearning test and a postlearning test. In the AIL group, the participants received another test with AI-assisted education before the postlearning test. Then, we analyzed changes in diagnostic accuracy. Results The prelearning performance was comparable in both groups. In the CL group, postlearning accuracy (78.66 ± 14.53) was higher than prelearning accuracy (75.86 ± 11.36) with no significant difference (p = .264). The AIL group showed remarkable improvement. The WithAI score (88.87 ± 5.51) was significantly higher than the prelearning score (75.73 ± 10.58, p < 0.01). Moreover, the postlearning score (84.93 ± 14.53) was better than the prelearning score (p < 0.01). The increase in accuracy was significantly higher in the AIL group than in the CL group. Conclusion The study demonstrated the viability of AI for augmenting medical education. Integrating AI into medical education requires dynamic collaboration from research, clinical, and educational perspectives.

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“…For segmentation and rendering of anatomic details of interest, many different software solutions are available to fit specific requirements or personal preferences. Recent research in 3D segmentation and rendering focuses on automation, e.g., using artificial intelligence [9]. However, to produce anatomically precise and reproducible models of a patient's individual anatomy, it is still important to confirm correct results in every step of the 3D printing process, i.e., to implement quality control mechanisms, which requires expertise of specialized personnel [23].…”

Section: Introductionmentioning

confidence: 99%