Advancing the Diagnostic Cockpit of the Future: An Opportunity to Improve Diagnostic Accuracy and Efficiency

Krupiński, Elizabeth A.; Bronkalla, Mark D.; Folio, Les; Keller, Brad M.; Mather, Richard T.; Seltzer, Steven E.; Schnall, Mitchell D.; Cruea, Renee

doi:10.1016/j.acra.2018.11.017

Cited by 9 publications

(2 citation statements)

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“…38 The Academy for Radiology and Biomedical Imaging Research has been working for the past few years on the concept of the ''diagnostic cockpit of the future.'' 39 The concept is of a ''future-state digital platform to aggregate, organize, and simplify medical imaging results and patient-centric clinical data . (for) detecting disease early, making accurate diagnoses, driving imageguided interventions, and improving downstream clinical management of patients.''…”

Section: Some Key Challengesmentioning

confidence: 99%

Artificial Intelligence: Lessons Learned from Radiology

Krupiński

2019

Healthcare Transformation

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Artificial intelligence (AI) will continue to increase its significant impact on the everyday acquisition, interpretation, and application of data in our healthcare system. It is difficult to predict whether and to what extent AI will change the health status of patients, but it certainly will change the way decisions are made, how healthcare is delivered, and the ways providers, patients, and healthcare enterprises interact with and use data from an increasing array of sources. Radiology has often been at the forefront of technological change in healthcare and the AI revolution is no different. The lessons radiology has learned to date, its successes and challenges, can help provide guidance for other specialties, especially since radiology interacts with and cross-cuts nearly every other specialty and touches nearly every patient at least once in his or her lifetime.

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Section: Some Key Challengesmentioning

confidence: 99%

Artificial Intelligence: Lessons Learned from Radiology

Krupiński

2019

Healthcare Transformation

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“…Simultaneously, there exists a perspective that the introduction of these innovative technologies may potentially render radiographers obsolete. In 2017, under the guidance of the Interagency Working Group on Medical Imaging and the Committee on Science, the National Science and Technology Council formulated a strategic blueprint to steer the trajectory of future imaging research and development [9]. This document strives to anticipate transformations across four pivotal facets of medical imaging research.…”

Section: Introductionmentioning

confidence: 99%

From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images

Arslan,

Haider,

Khurshid

et al. 2023

Cureus

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Radiology has been a pioneer in the healthcare industry's digital transformation, incorporating digital imaging systems like picture archiving and communication system (PACS) and teleradiology over the past thirty years. This shift has reshaped radiology services, positioning the field at a crucial junction for potential evolution into an integrated diagnostic service through artificial intelligence and machine learning. These technologies offer advanced tools for radiology's transformation. The radiology community has advanced computer-aided diagnosis (CAD) tools using machine learning techniques, notably deep learning convolutional neural networks (CNNs), for medical image pattern recognition. However, the integration of CAD tools into clinical practice has been hindered by challenges in workflow integration, unclear business models, and limited clinical benefits, despite development dating back to the 1990s. This comprehensive review focuses on detecting chest-related diseases through techniques like chest X-rays (CXRs), magnetic resonance imaging (MRI), nuclear medicine, and computed tomography (CT) scans. It examines the utilization of computer-aided programs by researchers for disease detection, addressing key areas: the role of computer-aided programs in disease detection advancement, recent developments in MRI, CXR, radioactive tracers, and CT scans for chest disease identification, research gaps for more effective development, and the incorporation of machine learning programs into diagnostic tools.

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