An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging

Alexander, Alan; Jiang, Adam; Ferreira, Cara; Zurkiya, Delphine

doi:10.1016/j.jacr.2019.07.019

Cited by 66 publications

(49 citation statements)

References 8 publications

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“…Without any intervention, the continuous addition of workload that aims to improve patient care will eventually turn into work overload that may jeopardize the quality and safety of patient care [7][8][9]. AI is regarded as a potential solution to increase efficiency and ease the workload of radiologists [11,31]. However, the present study suggests that most current AI applications in medical imaging have the opposite effect, because they commonly require additional post-processing and interpretation time rather than being seamlessly integrated in the workflow and taking over tasks of the diagnostic radiologist.…”

Section: Discussionmentioning

confidence: 99%

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Kwee

2021

Insights Imaging

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Objective To determine the anticipated contribution of recently published medical imaging literature, including artificial intelligence (AI), on the workload of diagnostic radiologists. Methods This study included a random sample of 440 medical imaging studies published in 2019. The direct contribution of each study to patient care and its effect on the workload of diagnostic radiologists (i.e., number of examinations performed per time unit) was assessed. Separate analyses were done for an academic tertiary care center and a non-academic general teaching hospital. Results In the academic tertiary care center setting, 65.0% (286/440) of studies could directly contribute to patient care, of which 48.3% (138/286) would increase workload, 46.2% (132/286) would not change workload, 4.5% (13/286) would decrease workload, and 1.0% (3/286) had an unclear effect on workload. In the non-academic general teaching hospital setting, 63.0% (277/240) of studies could directly contribute to patient care, of which 48.7% (135/277) would increase workload, 46.2% (128/277) would not change workload, 4.3% (12/277) would decrease workload, and 0.7% (2/277) had an unclear effect on workload. Studies with AI as primary research area were significantly associated with an increased workload (p < 0.001), with an odds ratio (OR) of 10.64 (95% confidence interval (CI) 3.25–34.80) in the academic tertiary care center setting and an OR of 10.45 (95% CI 3.19–34.21) in the non-academic general teaching hospital setting. Conclusions Recently published medical imaging studies often add value to radiological patient care. However, they likely increase the overall workload of diagnostic radiologists, and this particularly applies to AI studies.

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Section: Discussionmentioning

confidence: 99%

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Kwee

2021

Insights Imaging

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“…To date, a substantial amount of money and resources have been invested in AI for medical imaging[ 163 ]. There were profound concerns about medical imaging professionals being replaced or obsolete.…”

Section: Future Directionmentioning

confidence: 99%

Challenges and optimization strategies in medical imaging service delivery during COVID-19

Tay¹,

Kothan²,

Kada³

et al. 2021

WJR

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In coronavirus disease 2019 (COVID-19), medical imaging plays an essential role in the diagnosis, management and disease progression surveillance. Chest radiography and computed tomography are commonly used imaging techniques globally during this pandemic. As the pandemic continues to unfold, many healthcare systems worldwide struggle to balance the heavy strain due to overwhelming demand for healthcare resources. Changes are required across the entire healthcare system and medical imaging departments are no exception. The COVID-19 pandemic had a devastating impact on medical imaging practices. It is now time to pay further attention to the profound challenges of COVID-19 on medical imaging services and develop effective strategies to get ahead of the crisis. Additionally, preparation for operations and survival in the post-pandemic future are necessary considerations. This review aims to comprehensively examine the challenges and optimization of delivering medical imaging services in relation to the current COVID-19 global pandemic, including the role of medical imaging during these challenging times and potential future directions post-COVID-19.

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“…Today, it appears that medical specialties such as radiology, cardiology, dermatology and pathology are most conducive to AI-based applications. To date, >75 AI algorithms are approved by the US Food and Drug Administration (FDA), and one report states that AI-based medical imaging investments have grown exponentially to USD 1.17 billion [22,23]. To our knowledge, a number of algorithms related to pulmonary medicine have received 510(k) premarket approval (legal, regulatory recognition that a medical device is safe and effective) from the FDA and several more are CE-marked (Conformité Européenne is the mandatory regulatory marking for products sold within the European Economic Area) [22,24] (table 2).…”

Section: A Brief Description Of Ai and Machine Learningmentioning

confidence: 99%

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

Khemasuwan

Sorensen²,

Colt

2020

Eur Respir Rev

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Artificial intelligence (AI) is transforming healthcare delivery. The digital revolution in medicine and healthcare information is prompting a staggering growth of data intertwined with elements from many digital sources such as genomics, medical imaging and electronic health records. Such massive growth has sparked the development of an increasing number of AI-based applications that can be deployed in clinical practice. Pulmonary specialists who are familiar with the principles of AI and its applications will be empowered and prepared to seize future practice and research opportunities. The goal of this review is to provide pulmonary specialists and other readers with information pertinent to the use of AI in pulmonary medicine. First, we describe the concept of AI and some of the requisites of machine learning and deep learning. Next, we review some of the literature relevant to the use of computer vision in medical imaging, predictive modelling with machine learning, and the use of AI for battling the novel severe acute respiratory syndrome-coronavirus-2 pandemic. We close our review with a discussion of limitations and challenges pertaining to the further incorporation of AI into clinical pulmonary practice.

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An Intelligent Future for Medical Imaging: A Market Outlook on Artificial Intelligence for Medical Imaging

Cited by 66 publications

References 8 publications

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Workload of diagnostic radiologists in the foreseeable future based on recent scientific advances: growth expectations and role of artificial intelligence

Challenges and optimization strategies in medical imaging service delivery during COVID-19

Artificial intelligence in pulmonary medicine: computer vision, predictive model and COVID-19

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