Radiologist as Lifelong Learner: Strategies for Ongoing Education

Nguyen, Xuan V.; Adams, Scott; Hobbs, Susan K.; Ganeshan, Dhakshina Moorthy; Wasnik, Ashish P.

doi:10.1016/j.acra.2019.03.019

Cited by 6 publications

(8 citation statements)

References 28 publications

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“…Workplace interactions with colleagues, on‐the‐job database “research,” clinical teaching, and attendance at conferences were strongly valued by physicians as methods of “lifelong learning” and are not always reflected in all certification requirements for maintaining licensure 129 . The barriers in radiology to continuing medical education include insufficient time or financial support, lack of interest or perceived benefit, and lack of awareness of CME opportunities 130 …”

Section: What Makes Molecular Pathology Susceptible To Unjust Distrib...mentioning

confidence: 99%

“…It is understandable why some physicians/APPs stick to the treatment methods with which they are familiar 34 . Suggested methods for keeping oncologists up to date include flexible learning environments like taking online courses, testing physicians to demonstrate areas of weakness resulting in motivation for growth, and providing updated educational resources as soon as possible through less traditional learning media to prevent a lag in knowledge 130,131 . Hospitals and health care institutions can also help eliminate general barriers by providing off‐service time for attending conferences, taking advantage of educational opportunities, funding CME, creating an environment of inquiry with structured peer education/peer teaching, and improving the marketing of CME opportunities 130 …”

Section: What Makes Molecular Pathology Susceptible To Unjust Distrib...mentioning

confidence: 99%

“…129 The barriers in radiology to continuing medical education include insufficient time or financial support, lack of interest or perceived benefit, and lack of awareness of CME opportunities. 130 Maintaining current knowledge of oncology care is even more challenging, with 30 new cancer drugs, 45 new adjunct therapies, and 1 biosimilar being approved in 2020 alone. It is understandable why some physicians/APPs stick to the treatment methods with which they are familiar.…”

Section: Regional Disparitiesmentioning

confidence: 99%

“…130,131 Hospitals and health care institutions can also help eliminate general barriers by providing off-service time for attending conferences, taking advantage of educational opportunities, funding CME, creating an environment of inquiry with structured peer education/peer teaching, and improving the marketing of CME opportunities. 130 Interdepartmental communication can also help with continuing education. Multidisciplinary tumor boards allow for bilateral transfer of information between the pathologist and oncologist or patient-facing physician/APP.…”

Section: Regional Disparitiesmentioning

confidence: 99%

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Toward a More Just System of Care in Molecular Pathology

et al. 2022

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r American health care policy must be critically assessed to establish the role it plays in sustaining and alleviating the health disparities that currently exist in molecular genetic testing.r It is critical to understand the economic and sociocultural influences that drive patients to undergo or forgo molecular testing, especially in marginalized patient populations.r A multipronged solution with actions necessary from multiple stakeholders is required to reduce the cost of health care, rebalance regional disparities, encourage physician engagement, reduce data bias, and earn patients' trust. Context:The health status of a population is greatly influenced by both biological processes and external factors. For years, minority and low socioeconomic patient populations have faced worse outcomes and poorer health in the United States. Experts have worked extensively to understand the issues and find solutions to alleviate this disproportionate burden of disease. As a result, there have been some improvements and successes, but wide gaps still exist. Diagnostic molecular genetic testing and so-called personalized medicine are just now being integrated into the current American health care system. The way in which these tests are integrated can either exacerbate or reduce health disparities.

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Section: What Makes Molecular Pathology Susceptible To Unjust Distrib...mentioning

confidence: 99%

Section: What Makes Molecular Pathology Susceptible To Unjust Distrib...mentioning

confidence: 99%

Section: Regional Disparitiesmentioning

confidence: 99%

Section: Regional Disparitiesmentioning

confidence: 99%

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Toward a More Just System of Care in Molecular Pathology

et al. 2022

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“…Image acquisition is achieved by exploiting sophisticated technology, but image interpretation is still a task for the "human radiologist" [1]. To fulfill this complex task, people require almost three decades of learning as well as continuing medical education (CME) [2]. Despite all these efforts, perception and diagnostic errors exist.…”

Section: Introductionmentioning

confidence: 99%

The augmented radiologist: artificial intelligence in the practice of radiology

et al. 2021

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In medicine, particularly in radiology, there are great expectations in artificial intelligence (AI), which can “see” more than human radiologists in regard to, for example, tumor size, shape, morphology, texture and kinetics — thus enabling better care by earlier detection or more precise reports. Another point is that AI can handle large data sets in high-dimensional spaces. But it should not be forgotten that AI is only as good as the training samples available, which should ideally be numerous enough to cover all variants. On the other hand, the main feature of human intelligence is content knowledge and the ability to find near-optimal solutions. The purpose of this paper is to review the current complexity of radiology working places, to describe their advantages and shortcomings. Further, we give an AI overview of the different types and features as used so far. We also touch on the differences between AI and human intelligence in problem-solving. We present a new AI type, labeled “explainable AI,” which should enable a balance/cooperation between AI and human intelligence — thus bringing both worlds in compliance with legal requirements. For support of (pediatric) radiologists, we propose the creation of an AI assistant that augments radiologists and keeps their brain free for generic tasks.

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