Artificial Intelligence in Radiology Residency Training

Forney, Michael C.; McBride, Arran

doi:10.1055/s-0039-3400270

Cited by 29 publications

(21 citation statements)

References 41 publications

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“…The initial database search yielded 13,449 results; once duplicates were removed, the titles and abstracts of 10,094 (75.05%) unique citations were identified. From the 10,094 unique citations, we identified 41 (0.41%) articles [2,5,13,, where 13 unique, existing programs [32,35,39,43,49,50,59,[61][62][63] were mentioned in 10 (24%) articles, and the remaining 31 (76%) articles [2,5,13,[26][27][28][29][30][31]33,34,[36][37][38][40][41][42][44][45][46][47][48][51][52][53][54][55][56][57][58]60] discussed the desired or recommended curricular content. The article selection process is presented in Figure 1.…”

Section: Search Resultsmentioning

confidence: 99%

“…Study type [13,[26][27][28][29][30][32][33][34]36,38,[41][42][43][44][45]47,[49][50][51][52][53]55,56,[58][59][60][61][62][63] 30 (73) Commentary [2,5,40,48,54,57] 6 (15) Review [31,35,37] 3 (7) Empirical study [39] 1 (2) Case report [46] 1 (2) Best Evidence Medical Education Guide Publication year [39] 1 (2) 2013 [35,56,61] 3 (7) 2016 [31,50] 2 ( 5) 2017…”

Section: References Frequency N (%) Characteristicsmentioning

confidence: 99%

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Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review

Charow¹,

Jeyakumar²,

Younus³

et al. 2021

JMIR Med Educ

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Background As the adoption of artificial intelligence (AI) in health care increases, it will become increasingly crucial to involve health care professionals (HCPs) in developing, validating, and implementing AI-enabled technologies. However, because of a lack of AI literacy, most HCPs are not adequately prepared for this revolution. This is a significant barrier to adopting and implementing AI that will affect patients. In addition, the limited existing AI education programs face barriers to development and implementation at various levels of medical education. Objective With a view to informing future AI education programs for HCPs, this scoping review aims to provide an overview of the types of current or past AI education programs that pertains to the programs’ curricular content, modes of delivery, critical implementation factors for education delivery, and outcomes used to assess the programs’ effectiveness. Methods After the creation of a search strategy and keyword searches, a 2-stage screening process was conducted by 2 independent reviewers to determine study eligibility. When consensus was not reached, the conflict was resolved by consulting a third reviewer. This process consisted of a title and abstract scan and a full-text review. The articles were included if they discussed an actual training program or educational intervention, or a potential training program or educational intervention and the desired content to be covered, focused on AI, and were designed or intended for HCPs (at any stage of their career). Results Of the 10,094 unique citations scanned, 41 (0.41%) studies relevant to our eligibility criteria were identified. Among the 41 included studies, 10 (24%) described 13 unique programs and 31 (76%) discussed recommended curricular content. The curricular content of the unique programs ranged from AI use, AI interpretation, and cultivating skills to explain results derived from AI algorithms. The curricular topics were categorized into three main domains: cognitive, psychomotor, and affective. Conclusions This review provides an overview of the current landscape of AI in medical education and highlights the skills and competencies required by HCPs to effectively use AI in enhancing the quality of care and optimizing patient outcomes. Future education efforts should focus on the development of regulatory strategies, a multidisciplinary approach to curriculum redesign, a competency-based curriculum, and patient-clinician interaction.

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Section: Search Resultsmentioning

confidence: 99%

Section: References Frequency N (%) Characteristicsmentioning

confidence: 99%

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review

Charow¹,

Jeyakumar²,

Younus³

et al. 2021

JMIR Med Educ

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“…Artificial intelligence (AI) is used more and more often for radiologist education. AI-augmented radiology may enable precision medicine as well as precision medical education [14,15]. Robust data from stroke registries will be unbelievably valuable to find the right inclusion and exclusion treatment choice criteria.…”

Section: Stroke Managementmentioning

confidence: 99%

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Zeleňák

Krajina

Meyer

et al. 2021

Life

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Stroke remains one of the leading causes of death and disability in Europe. The European Stroke Action Plan (ESAP) defines four main targets for the years 2018 to 2030. The COVID-19 pandemic forced the use of innovative technologies and created pressure to improve internet networks. Moreover, 5G internet network will be helpful for the transfer and collecting of extremely big databases. Nowadays, the speed of internet connection is a limiting factor for robotic systems, which can be controlled and commanded potentially from various places in the world. Innovative technologies can be implemented for acute stroke patient management soon. Artificial intelligence (AI) and robotics are used increasingly often without the exception of medicine. Their implementation can be achieved in every level of stroke care. In this article, all steps of stroke health care processes are discussed in terms of how to improve them (including prehospital diagnosis, consultation, transfer of the patient, diagnosis, techniques of the treatment as well as rehabilitation and usage of AI). New ethical problems have also been discovered. Everything must be aligned to the concept of “time is brain”.

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“…raise various suspicions in the minds of resident physicians as well as medical students (42). However, it is clear that AI technologies only serve a supplemental purpose therefore, are often more appropriately and conveniently approved as augmentation equipment rather than replacement tools (42,43). Neverthe-less, some people associated with the healthcare profession predict a complete replacement would take part and this prediction instills a sense of anxiety (40).…”

Section: Where Are We Headed?mentioning

confidence: 99%

Modern Renaissance or an Ethical Conundrum: Reviewing the Implications of Artificial Intelligence in the Field of Radiology

Manan¹,

Manan²

2021

tmsj

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tilayered artificial architecture of neurons to realize its set goals and does not require any human intervention (5). Machine learning is essentially the basis for computer-aided diagnosis (CADx); which was developed to assist image reading (3). Both of these novel fields fall under the broad classification of AI technologies and find immense potential in medicine as well as surgery. As seen, changes are inevitable in the ever-growing field of radiology with the advent of higher degrees of machine intelligence. Therefore, a strong sense of innovation and adaptability must be ingrained in the minds of radiologists in order to assist their traditional diagnostic practices with the technological advancements in their field.

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Artificial Intelligence in Radiology Residency Training

Cited by 29 publications

References 41 publications

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review

Artificial Intelligence Education Programs for Health Care Professionals: Scoping Review

How to Improve the Management of Acute Ischemic Stroke by Modern Technologies, Artificial Intelligence, and New Treatment Methods

Modern Renaissance or an Ethical Conundrum: Reviewing the Implications of Artificial Intelligence in the Field of Radiology

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