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Everything you need to know about Computed Tomography (CT) & CT Scanning

Deep Learning: Ai and the Emergency Room Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ AI and the Emergency Room

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  • Prerequisites: AI tools should not replace on-site radiological care by a local radiologist when possible. AI is not a substitute for the radiologist, as it is a simple decision-support tool. The human guarantor is indispensable, and the interpretation must be the responsibility of a radiologist with expertise in emergency imaging.  
    Proposals for the use of artificial intelligence in emergency radiology  
    Thibaut Jacques et al.  
    Diagnostic and Interventional Imaging 2021 (in press)
  • Proposal 1: the technical safety of an algorithmic solution must be guaranteed at least by a category IIa CE marking. Marking is necessary but not sufficient, as it is not intended to guarantee the real life clinical performance of a deployed algorithm.  
    Proposal 2: the in silico and in vivo performances of an algorithm are always different. Companies commercializing or deploying decision-support algorithms that use AI should: i), be transparent about the nature of the data used for each phase and about the confusion matrices of the algorithms under study; ii), be able to consistently provide corrected performance data based on robust extrinsic validation.  
    Proposals for the use of artificial intelligence in emergency radiology  
    Thibaut Jacques et al.  
    Diagnostic and Interventional Imaging 2021 (in press)
  • Proposal 3: after the deployment of an AI solution, routinely implement a pro-active algorithmic vigilance phase that is both ascending and descending, and strive to organize regular local Morbidity and Mortality Conferences in Artificial Intelligence (AI- MMCs) focused on algorithmic vigilance.  
    Proposal 4: as soon as an AI tool intended to aid medical decision is likely to be deployed, the representatives of the medical and sur- gical departments that will be impacted by the data resulting from the algorithm, in the institution, must be officially informed. This deployment must be part of the medical project of local imaging organization at the site.
    Proposals for the use of artificial intelligence in emergency radiology  
    Thibaut Jacques et al.  
    Diagnostic and Interventional Imaging 2021 (in press)
  • Proposal 5: all the necessary measures must be implemented to limit, on the one hand, the cognitive biases of users and, on the other hand, the risk of loss of competence (“deskilling”), notably among less experienced users (residents and non-experts).  
    Proposal 6: the use of AI as a decision-support tool is an active process carried out by a legally responsible radiologist. This process must result in traceability and in documentation of the decision- making process. The use of such a tool for all or part of the diagnosis should be routinely recorded in the patient’s file. The working group requests that AI tools be made unable to export their data to the PACS in the absence of prior validation of this export by the radiologist in charge of the interpretation. Internal traceability of major discrepancies could be achieved by holding regular AI-MMCs.  
    Proposals for the use of artificial intelligence in emergency radiology  
    Thibaut Jacques et al.  
    Diagnostic and Interventional Imaging 2021 (in press)
  • “The most significant takeaway from these studies may be that a need to clarify the role of AI in radiology is called for, and interest in developing curricula in AI for radiology training programs is slowly emerging. But how will it take shape? Or how will we shape it? Journals like Emergency Radiology and societies like the American Society of Emergency Radiology (ASER) have the opportunity to play important roles in defining a roadmap.”
    Developing a curriculum in artificial intelligence for emergency radiology
    Edmund M. Weisberg, Elliot K. Fishman
    Emergency Radiology (2020) 27:359–360
  • "There is no perfect answer or clear academic program ready to delineate. The technology is evolving rapidly, and even the literature that might be suitable now will be obsolete or less compelling in just a couple of years. But we should start with something. Such a rollout should begin with a course or two, based largely on a survey of the current literature and the current usage of the approved apps. At some point not far down the line, a comprehensive education policy to address AI in radiology may be appropriate, including fuller learning modules that include surveys into the complexity of deep learning and further exploration into app development and expanding the use of AI beyond the emergency radiology realm.”
    Developing a curriculum in artificial intelligence for emergency radiology
    Edmund M. Weisberg, Elliot K. Fishman
    Emergency Radiology (2020) 27:359–360
  • Technology That is Developed for Consumer Applications is Going to Revolutionize Medicine
    - Voice activated
    - Motion activated
    - Thought activated (Star-Trek)
    - Touch activated (an issue in COVID-19)
  • The Unknowns in AI in Radiology
    - Will we get reimbursed for using AI?
    - Will overall reimbursement increase or decrease?
    - If AI becomes very accurate will there be a push for non-radiologists to read studies (both other physicians like ER docs and orthopedic surgeons as well as PAs)
  • The Unknowns in AI in Radiology
    - When will AI become “standard of care” in Radiology
    - What are the legal ramifications of AI (using or not using it)
    - When will AI affect Radiology Residency and Fellowship training?
  • “With the ongoing fear of the pandemic, and the conflicting data regarding possible spread from surfaces, being able to have voice commands decrease risk and provides the ability to bypass common danger points from elevator buttons to door knobs to credit card processing machines. Outside of the individual, the increasing presence of voice-enabled devices affects the data-gathering and research approaches to this pandemic as well as to future public health crises. These devices can facilitate the sharing and gathering of information, provide near instantaneous updated information, and facilitate the pooling of data for use by public health experts and artificial intelligence algorithms.”
    Connecting With Patients: The Rapid Rise of Voice Right Now
    Isbitski D, Fishman EK, Rowe SP
    J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17]. 
  • "The combination of rapidly advancing voice-enabled technology and the social changes we have seen because of the coronavirus have driven the adoption of voice as a potential transformative way that patients can obtain information and communicate with their health care providers. Because of the changes that have already occurred, we can expect that we will never go back entirely to how things were and that voice will be an increasingly important influence on health care.”
    Connecting With Patients: The Rapid Rise of Voice Right Now
    Isbitski D, Fishman EK, Rowe SP
    J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17].
  • "The focus on artificial intelligence in radiology has been on the use of algorithms to enhance image interpretation and uncover imaging bio- markers. However, artificial intelligence will have profound impacts across radiology practices, and the rise of voice-enabled devices indicates that. We can expect that patient preparation, explanations of studies, and the consenting process will be well handled by voice-enabled devices with artificial intelligence algorithms.”
    Connecting With Patients: The Rapid Rise of Voice Right Now
    Isbitski D, Fishman EK, Rowe SP
    J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17].  
  • "Successful practices that emerge from the coronavirus pandemic in strong positions will find ways to leverage artificial intelligence, and voice-enabled technologies can play a large role in that. Our day-to-day work in our offices will also change. Voice-enabled technologies can finally help us to realize the “paperless” office. Our phone calls, dictations, and communications with colleagues can all be done in a contactless way using voice.”
    Connecting With Patients: The Rapid Rise of Voice Right Now
    Isbitski D, Fishman EK, Rowe SP
    J Am Coll Radiol. 2020;S1546-1440(20)30666-9. [published online ahead of print, 2020 July 17]. 
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