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Pages 56-57

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From page 56...
... Hricak summarized a number of strategies that could improve patient access to expertise and technologies in oncologic imaging and pathology, including enhanced education and training, clinical decision support tools, models of care delivery and payment, computational oncology, and data sharing. In terms of education and training, Hricak said that oncologic imaging needs to be fully integrated into the curriculum of radiology residency programs, and should also be included as a clinical practice area for ongoing longitudinal assessment through MOC.
From page 57...
... Hricak reported on essential elements for quality improvement efforts in cancer diagnosis and care, including a constructive, non-punitive culture; clinical and operational leadership; feedback on clinician performance and patient outcomes supported by data systems and measurement infrastructure; and the bandwidth for clinicians to engage in quality improvement efforts by removing burdensome non-patient care activities. Hricak noted that machine learning and AI have the potential to fundamentally change how cancer care is delivered and will facilitate unified diagnostics and precision oncology: "There's no question that integrated diagnostics is the future, but we are not there yet." She noted that interoperability and standardization remain major challenges that need to be addressed in order to facilitate improved data sharing to study the interrelationships among diagnosis, treatment, and patient outcomes at scale.

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