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

Deep Learning: Ai and Legal Issues Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ AI and Legal Issues

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  • Rationale and Objectives: Generative adversarial networks (GANs) are deep learning models aimed at generating fake realistic looking images. These novel models made a great impact on the computer vision field. Our study aims to review the literature on GANs applications in radiology.
    Conclusion: GANs are increasingly studied for various radiology applications. They enable the creation of new data, which can be used to improve clinical care, education and research.”
    Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review
    Vera Sorin et al.
    Acad Radiol 2020 (in press)
  • “Generative adversarial networks (GANs) are a more recent deep learning development, invented by Ian Goodfellow and colleagues. GAN is a type of deep learning model that is aimed at generating new images. GANs are now at the center of public attention due to “deepfake” digital media manipulations. This technique uses GANs to generate artificial images of humans. As an example, this webpage uses GAN to create random fake pictures of non-existent people.”
    Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review
    Vera Sorin et al.
    Acad Radiol 2020 (in press)
  • "Deep learning can improve diagnostic imaging tasks in radiology enabling segmentation of images, improvement of image quality, classification of images, detection of findings, and prioritization of examinations according to urgent diagnoses. Successful training of deep learning algorithms requires large-scale data sets. However, the difficulty of obtaining sufficient data limits the development and implementation of deep learning algorithms in radiology. GANs can help to overcome this obstacle. As dem- onstrated in this review, several studies have successfully trained deep learning algorithms using augmented data generated by GANs. Data augmentation with generated images significantly improved the performance of CNN algorithms. Furthermore, using GANs can reduce the amount of clinical data needed for training. The increasing research focus on GANs can therefore impact successful automatic image analysis in radiology.”
    Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review
    Vera Sorin et al.
    Acad Radiol 2020 (in press)
  • "Some risks are involved with the development of GANs. In a recent publication Mirski et al. warn against hacking of imaging examinations, artificially adding or removing medical conditions from patient scans. Also, using generated images in clinical practice should be done with caution, as the algorithms are not without limitations. For example, in image reconstruction details can get lost at translation, while fake inexistent details can suddenly appear.”
    Creating Artificial Images for Radiology Applications Using Generative Adversarial Networks (GANs) – A Systematic Review
    Vera Sorin et al.
    Acad Radiol 2020 (in press)
  • “The medico-legal issue that then arises is the question of “who is responsible for the diagnosis,” especially if it is wrong. Whether data scientists or manufacturers involved in development, marketing, and installation of AI systems will carry the ultimate legal responsibility for adverse outcomes arising from AI algorithm use is a dif- ficult legal question; if doctors are no longer the primary agents of interpretation of radiological studies, will they still be held accountable?”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • ”If radiologists monitor AI system outputs and still have a role in validating AI interpretations, do they still carry the ultimate responsibility, even though they do not understand, and cannot interrogate the precise means by which a diagnosis was determined? This “black box” element of AI poses many challenges, not least to the basic human need to under- stand how and why important decisions were made."
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Furthermore, if patient data are used to build AI products which go on to generate profit, consideration needs to be given to the issue of intellectual property rights. Do the involved patients and the collecting organizations have a right to share in the profits that derive from their data?”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • “Fundamentally, each patient whose data is used by a third party should pro- vide consent for that use, and that consent may need to be obtained afresh if the data is re-used in a different context (e.g., to train an updated software version). Moreover, ownership of imaging datasets varies from one jurisdiction to another. In many countries, the ultimate ownership of such personal data resides with the patient, although the data may be stored, with consent, in a hospital or imaging centre repository.
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
  • The real challenge is not to oppose the incorporation of AI into the professional lives (a futile effort) but to embrace the inevitable change of radiological practice, incorporating AI in the radiological workflow. The most likely danger is that “[w]e’ll do what computers tell us to do, because we’re awestruck by them and trust them to make important decisions”
    What the radiologist should know about artificial intelligence – an ESR white paper
    Insights into Imaging (2019) 10:44 https://doi.org/10.1186/s13244-019-0738-2 
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