The role of artificial intelligence in medical imaging research

Tang, Xiaoli

doi:10.1259/bjro.20190031

Cited by 110 publications

(47 citation statements)

References 36 publications

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“…In general, AI in healthcare refers to all artificial intelligence-based technologies that make educated decisions regarding a patient's diagnosis, monitoring, treatment, and management. The importance of AI has specifically increased many folds when imaging comes into play, mainly because of large volumetric data sizes and the extensive need to characterize and quantify the disease via lesion images [ [130] , [131] , [132] ]. Tissue imaging and its characterization is of prime importance since it has a direct influence on decisions related to COVID-19 severity for a patient [ [133] , [134] , [135] ].…”

Section: Machine Learning and Deep Learning For Tissue Characterizatimentioning

confidence: 99%

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Suri¹,

Puvvula²,

Biswas³

et al. 2020

Computers in Biology and Medicine

102

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Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1 st , 2020 . Since the outbreak began, almost 28,000 articles about COVID-19 have been published ( https://pubmed.ncbi.nlm.nih.gov ); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

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Section: Machine Learning and Deep Learning For Tissue Characterizatimentioning

confidence: 99%

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Suri¹,

Puvvula²,

Biswas³

et al. 2020

Computers in Biology and Medicine

102

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“…The number of commercially available AI applications is increasing [1]. This can be clearly seen from the number of scientific publications and presentations at medical conferences, the number of commercial booths at these conferences and the many webinars being organized on the same topics.…”

Section: Introductionmentioning

confidence: 99%

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Bosmans

Zanca²,

Gelaude

2021

Physica Medica

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In this study, we propose a framework to help the MPE take up a unique and important role at the introduction of AI solutions in clinical practice, and more in particular at procurement, acceptance, commissioning and QA. Material and methods: The steps for the introduction of Medical Radiological Equipment in a hospital setting were extrapolated to AI tools. Literature review and in-house experience was added to prepare similar, yet dedicated test methods. Results: Procurement starts from the clinical cases to be solved and is usually a complex process with many stakeholders and possibly many candidate AI solutions. Specific KPIs and metrics need to be defined. Acceptance testing follows, to verify the installation and test for critical exams. Commissioning should test the suitability of the AI tool for the intended use in the local institution. Results may be predicted from peer reviewed papers that treat representative populations. If not available, local data sets can be prepared to assess the KPIs, or 'virtual clinical trials' could be used to create large, simulated test data sets. Quality assurance must be performed periodically to verify if KPIs are stable, especially if the software is upscaled or upgraded, and as soon as selflearning AI tools would enter the medical practice. Discussion: MPEs are well placed to bridge between manufacturer and medical team and help from procurement up to reporting to the management board. More work is needed to establish consolidated test protocols.

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“…Artificial Intelligence in Medical Imaging: "Images Are More Than Pictures, They Are Data" (Gillies et al, 2016) During its development, medical imaging has enjoyed great benefit from technological progress (Nance et al, 2013;Nguyen and Shetty, 2018), and the scientific relevance of the development of AI systems in radiology has been underscored by an everincreasing number of publications on AI. For diagnostic imaging alone, the number of publications on AI has increased from about 100-150 per year in 2007-2008 to 1000-1100 per year in 2017-2018 (Tang, 2020).…”

Section: Artificial Neural Network and Deep Learningmentioning

confidence: 99%

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

et al. 2021

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Artificial intelligence (AI) has seen dramatic growth over the past decade, evolving from a niche super specialty computer application into a powerful tool which has revolutionized many areas of our professional and daily lives, and the potential of which seems to be still largely untapped. The field of medicine and medical imaging, as one of its various specialties, has gained considerable benefit from AI, including improved diagnostic accuracy and the possibility of predicting individual patient outcomes and options of more personalized treatment. It should be noted that this process can actively support the ongoing development of advanced, highly specific treatment strategies (e.g., target therapies for cancer patients) while enabling faster workflow and more efficient use of healthcare resources. The potential advantages of AI over conventional methods have made it attractive for physicians and other healthcare stakeholders, raising much interest in both the research and the industry communities. However, the fast development of AI has unveiled its potential for disrupting the work of healthcare professionals, spawning concerns among radiologists that, in the future, AI may outperform them, thus damaging their reputations or putting their jobs at risk. Furthermore, this development has raised relevant psychological, ethical, and medico-legal issues which need to be addressed for AI to be considered fully capable of patient management. The aim of this review is to provide a brief, hopefully exhaustive, overview of the state of the art of AI systems regarding medical imaging, with a special focus on how AI and the entire healthcare environment should be prepared to accomplish the goal of a more advanced human-centered world.

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The role of artificial intelligence in medical imaging research

Cited by 110 publications

References 36 publications

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

COVID-19 pathways for brain and heart injury in comorbidity patients: A role of medical imaging and artificial intelligence-based COVID severity classification: A review

Procurement, commissioning and QA of AI based solutions: An MPE’s perspective on introducing AI in clinical practice

Human, All Too Human? An All-Around Appraisal of the “Artificial Intelligence Revolution” in Medical Imaging

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