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Deep Learning: Ai and Healthcare (overview) Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ AI and Healthcare (overview)

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  • "Artificial neural networks are inspired by the ability of brains to learn complicated patterns in data by changing the strengths of synaptic connections between neurons. Deep learning uses deep networks with many intermediate layers of artificial "neurons" between the input and the output, and, like the visual cortex, these artificial neurons learn a hierarchy of progressively more complex feature detectors. By learning feature detectors that are optimized for classification, deep learning can substantially outperform systems that rely on features supplied by domain experts or that are designed by hand."
    Deep Learning—A Technology With the Potential to Transform Health Care
    Geoffrey Hinton
    published online August 30, 2018]. JAMA. doi:10.1001/jama .2018.11100
  • "Understandably, clinicians, scientists, patients, and regulators would all prefer to have a simple explanation for how a neural net arrives at its classification of a particular case. In the example of predicting whether a patient has a disease, they would like to know what hidden factors the network is using. However, when a deep neural network is trained to make predictions on a big data set, it typically uses its layers of learned, nonlinear features to model a huge number of complicated but weak regularities in the data. It is generally infeasible to interpret these features because their meaning depends on complex interactions with uninterpreted features in other layers."
    Deep Learning—A Technology With the Potential to Transform Health Care
    Geoffrey Hinton
    published online August 30, 2018]. JAMA. doi:10.1001/jama .2018.11100
  • As data sets get bigger and computers become more powerful, the results achieved by deep learning will get better, even with no improvement in the basic learning techniques, although these techniques are being improved. The neural networks in the human brain learn from fewer data and develop a deeper, more abstract understanding of the world. In contrast to machine-learning algorithms that rely on provision of large amounts of labeled data, human cognition can find structure in unlabeled data, a process commonly termed unsupervised learning.
    Deep Learning—A Technology With the Potential to Transform Health Care
    Geoffrey Hinton
    published online August 30, 2018]. JAMA. doi:10.1001/jama .2018.11100
  • "The creation of a smorgasbord of complex feature detectors based on unlabeled data appears to set the stage for humans to learn a classifier from only a small amount of labeled data. How the brain does this is still a mystery, but will not remain so. As new unsupervised learning algorithms are discovered, the data efficiency of deep learning will be greatly augmented in the years ahead, and its potential applications in health care and other fields will increase rapidly."
    Deep Learning—A Technology With the Potential to Transform Health Care
    Geoffrey Hinton
    published online August 30, 2018]. JAMA. doi:10.1001/jama .2018.11100
  • "In 1976, Maxmen predicted that artificial intelligence (AI) in the 21st century would usher in "the post-physician era," with health care provided by paramedics and computers. Today, the mass extinction of physicians remains unlikely. However, as outlined by Hinton2 in a related Viewpoint, the emergence of a radically different approach to AI, called deep learning, has the potential to effect major changes in clinical medicine and health care delivery."
    On the prospects for a (deep) learning health care system
    NaylorCD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • "Deep learning had intuitive appeal for health- related applications, given its demonstrable strengths in intricate pattern recognition and predictive model building from big high-dimensional data sets. These analytic capabilities have already proven useful for basic and applied researchers, ranging across health disciplines. Thus far, clinical application of deep learning has been most rapid in image-intensive fields such as radiology, radiotherapy, pathology, ophthalmology, dermatology, and image-guided surgery."
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • "In many cases, interpretation of images by deep learning systems has outperformed that by individual clinicians when measured against a consensus of expert readers or gold standards such as pathologic findings. Clinically relevant applications have widened beyond image processing to include risk stratification for a broad range of patient populations (eBox in the Supplement), and health care organizations are capitalizing on deep learning and other machine-learning tools to improve logistics, quality management, and financial oversight. "
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • "Digital imaging in all its forms is becoming more powerful and more integral to medicine and health care. Unlike deep learning, expert human interpretation fails to capitalize on all the patterns, or "regularities," that can be extracted from very large data sets and used for interpretation of still and moving images. Deep learning and related machine- learning methods can also learn from massively greater numbers of images than any human expert, continue learning and adapting over time, mitigate interobserver variability, and facilitate better decision making and more effective image-guided therapy."
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • Deep learning shows promise for streamlining routine work by health care professionals and empowering patients, thereby promoting a safer, more humane, and participatory paradigm for health care. Different sources offer varying estimates of the amount of time wasted by health care professionals on tasks amenable to some automation (eg, high-quality image screening) that could then be rededicated to more or better care. A growing number of research studies also suggest specific possibilities for reduction in errors and improved work flow in the clinical setting with appropriate deployment of AI.
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • Deep learning has enormous capacity to inform the process of discovery in health research and to facilitate hypothesis generation by identifying novel associations. Established and start-up companies are using deep learning to select or design novel molecules for testing as pharmaceuticals or biologics, with in silico exploration preceding in vitro examination and in vivo experimentation. Researchers across disciplines have also found unexpected clusters within data sets by comparing the intensity of activation of feature detectors in the hidden layers of deep neural nets. As always, however, basic and clinical experimentation remains essential to establish causation and causal pathways.
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • "In the longer term, deep learning can relate those personalized features to the clinical course of similar patients, using data from millions of patient records containing billions of medical events. Thus, while concerns are understandably raised that automation could de- humanize clinical care, these advances could provide professionals and patients alike with vastly better and more specific information, and, as Fogel and Kvedar argue, give physicians more time "to focus on the tasks that are uniquely human: building relationships, exercising empathy, and using human judgment to guide and advise."
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • Deep learning is diffusing rapidly through a combination of open- source and proprietary programs. Technology giants are making massive investments in the development of software libraries for deep learning, some of which are open sourced. These huge enterprises, as well as start-ups, are applying deep learning tools to health care all over the world. Moreover, many academic and nonprofit teams are publishing and sharing algorithms freely, and local development is now widespread.
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103
  • However, unlike a standardized diagnostic test or drug, the performance of deep learning and other machine-learning methods improves with exposure to larger or more relevant data sets, or with easily made modifications to the architecture of the models or training procedures. Regulators and technology assessors will need to distinguish issues inherent in decision-support algorithms from those attributable to misuse by clinical decision makers. Procurement agencies and health care administrators will need to be uncharacteristically nimble to keep up..
    On the prospects for a (deep) learning health care system
    Naylor CD
    [published online August 30, 2018]. JAMA. doi:10.1001/jama.2018.11103

  • Deep Learning Approach for Evaluating Knee MR Images: Achieving High Diagnostic Performance for Cartilage Lesion Detection
    FangLiu et al.
    Radiology 2018 (in press)


  • "By now, it’s almost old news: big data will transform medicine. It’s essential to remember, however, that data by themselves are useless. To be useful, data must be analyzed, interpreted, and acted on. Thus, it is algorithms —
    not data sets — that will prove transformative."
    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
    Obermeyer Z, Emanuel EJ
    N Engl J Med 375;13 September 29, 2016
  • “But where machine learning shines is in handling enormous numbers of predictors — sometimes, remarkably, more predictors than observations — and combining them in nonlinear and highly interactive ways.This capacity al- lows us to use new kinds of data, whose sheer volume or complexity would previously have made analyzing them unimaginable.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Another key issue is the quantity and quality of input data. Machine learning algorithms are highly data hungry, often re- quiring millions of observations to reach acceptable performance levels.In addition, biases in data collection can substantially affect both performance and generalizability. Lactate might be a good predictor of the risk of death, for example, but only a small, nonrepresentative sample of patients have their lactate levels checked.”

    
Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Machine learning has become ubiquitous and indispensable for solving complex problems in most sciences. In astronomy, algorithms sift through millions of images from telescope surveys to classify galaxies and find supernovas.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “Increasingly, the ability to transform data into knowledge will disrupt at least three areas of medicine. First, machine learning will dramatically improve the ability of health professionals to es- tablish a prognosis. Current prognostic models (e.g., the Acute Physiology and Chronic Health Evaluation [APACHE] score and the Sequential Organ Failure Assessment [SOFA] score) are restricted to only a handful of vari- ables, because humans must enter and tally the scores. But data could instead be drawn directly from EHRs or claims databases, allow- ing models to use thousands of rich predictor variables.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016


  • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135


  • Canadian Association of Radiologists White Paper on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
  • “One of the first and most significant hurdles to getting a CPT code is the need for peer-reviewed research in the United States that demonstrates both the efficacy and safety of the procedure. The second hurdle is the need for the procedure to be widely performed by a large number of physicians in the United States. These two requirements will prevent many AI software programs from achieving a CPT code. But, let us presume that at least one AI tool makes the cut and gets a CPT code. It will then have to be valued by the Relative Value Scale Update Committee (RUC) to get assigned RVUs.”


    Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • “The RUC values the professional component of a medical procedure based upon the work of a physician. The primary components of physi- cian work include the time it takes to perform the service, the level of technical skill required, and the mental effort and judgment necessary. For most AI tools I have seen, there is minimal to no physician work. Some AI processes run in the background and “prioritize” CT scans based on characteristics that may indicate an emergent finding. There is no physician work in this. Some AI processes may highlight specific imaging findings for the radiologist. This type of operation would be considered similar to computer-aided detection, and so would be valued similarly to prior CPT codes for computer-aided detection used in chest radiographs or mammography, though much of this work is either unreimbursed or bundled into the actual diagnostic procedure (eg, mammography and breast MRI).”

    
Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • “My opinion is that neither the government nor private payers will reimburse physicians and hospitals for using AI-driven software products. I believe that we will all purchase AI tools and treat them as an unreimbursed business expense. We will invest in AI software to ensure we are delivering high- quality work, to increase our efficiency, and to simplify clerical type tasks. In this way, paying for AI tools will merely be a cost of doing business like other operational expenses we incur.”


    Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (in press)
  • ”Artificial intelligence (AI) is rapidly moving from an experimental phase to an implementation phase in many fields, including medicine. The combination of improved availability of large datasets, increasing computing power, and advances in learning algorithms has created major performance breakthroughs in the development of AI applications. In the last 5 years, AI techniques known as deep learning have delivered rapidly improving performance in image recognition, caption generation, and speech recognition. Radiology, in particular, is a prime candidate for early adoption of these techniques. It is anticipated that the implementation of AI in radiology over the next decade will significantly improve the quality, value, and depth of radiology’s contribution to patient care and population health, and will revolutionize radiologists’ workflows.”

    
Canadian Association of Radiologists White Paper 
on Artificial Intelligence in Radiology
An Tang et al. 
Canadian Association of Radiologists Journal 69 (2018) 120e135
  • “In conclusion, with the current fast pace in development of machine learning techniques, and deep learning in particular, there is prospect for a more widespread clinical adoption of machine learning in radiology practice. Machine learning and artificial intelligence are not expected to replace the radiologists in the foreseeable future. ese techniques can potentially facilitate radiology work ow, increase radiologist productivity, improve detection and interpretation of findings, reduce the chance of error, and enhance patient care and satisfaction.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Radiomics is a process designed to extract a large number of quantitative features from radiology images . Radiomics is an emerging field for machine learning that allows for conversion of radiologic images into mineable high-dimensional data. For instance, Zhang et al evaluated over 970 radiomics features extracted from MR images by using machine learning methods and correlated with features to predict local and distant treatment failure of advanced nasopharyngeal carcinoma.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • “Machine learning approaches to the interrogation of a wide spectrum of such data (sociodemographic, imaging, clinical, laboratory, and genetic) has the potential to further personalize health care, far beyond what would be possible through imaging applications alone. Precision medicine require the use of novel computational techniques to harness the vast amounts of data required to discover individualized disease factors and treatment decisions.”


    Current Applications and Future Impact of Machine Learning in Radiology 
Garry Choy et al.
 Radiology 2018; 00:1–11
  • AI in Healthcare

  • AI in Healthcare

  • “Deep learning is a type of representation learning in which the algorithm learns a composition of features that re ect a hierarchy of structures in the data. Complex representations are expressed in terms of simpler representations.”
Deep Learning: A Primer for Radiologists.”

    
Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Although neural networks have been used for decades, in re- cent years three key factors have enabled the training of large neural networks: (a) the availability of large quantities of la- beled data, (b) inexpensive and powerful parallel computing hardware, and (c) improvements in training techniques and architectures.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “Deep CNNs exploit the compositional structure of natural images so that shifts and deformations of objects in the images do not significantly affect the overall performance of the network.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
  • “The creation of these large databases of labeled medical images and many associated challenges will be fundamental to foster future research in deep learning applied to medical images.”
Deep Learning: A Primer for Radiologists.”


    Chartrand G et al.
RadioGraphics 2017; 37:2113–2131
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