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

Deep Learning: Man Vs Ai Imaging Pearls - Educational Tools | CT Scanning | CT Imaging | CT Scan Protocols - CTisus
Imaging Pearls ❯ Deep Learning ❯ Man vs AI

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  • Through the application of AI, information-intensive domains such as marketing, health care, financial services, education, and professional services could become simultaneously more valuable and less ex- pensive to society. Business drudgery in every indus- try and function—overseeing routine transactions, repeatedly answering the same questions, and ex- tracting data from endless documents—could become the province of machines, freeing up human workers to be more productive and creative. Cognitive tech- nologies are also a catalyst for making other data-in- tensive technologies succeed, including autonomous vehicles, the Internet of Things, and mobile and multi- channel consumer technologies.
  • Cognitive insight.
    The second most common type of project in our study (38% of the total) used algorithms to detect patterns in vast volumes of data and interpret their meaning. Think of it as “analytics on steroids.” These machine-learning applications are being used to:
    - predict what a particular customer is likely to buy;
    - identify credit fraud in real time and detect insur- ance claims fraud
    - analyze warranty data to identify safety or quality problems in automobiles and other manufactured products
    - automate personalized targeting of digital ads; and
    - provide insurers with more-accurate and detailed actuarial modeling.
  • AI in Radiology: The Bottom Line
    - AI will put Radiologists out of business
    - AI is all hype and will soon fade like many fads
    - The reality is that AI will change all aspects of Radiology but may be our savior rather the grim reaper?
  • Reality: AI is already in our patients homes (and in yours)
    - Voice-enabled assistants that use AI have entered the homes of many patients (Amazon Alexa, Google Home)
    -- Connectivity to our patients with pre-study or post-study information
    -- Can help reduce readmissions or un-necessary ER visits by answering patients questions
  • Reality: AI can eliminate needless costs
    - Eliminate positions in customer service, billing and administration
    - Eliminate significant numbers of staff in scheduling or call centers while improving the patient experience. Think Uber and Diner Reservations or even Airline reservations
  • Reality: Machine Learning can decrease medical error
    - Can AI be the ultimate second reader?
    - Clinical applications
    -- CT
    -- MR
    -- Plain Radiographs
    -- Ultrasound
    -- Pathology
  • “Second, machine learning will displace much of the work of radiologists and anatomical pathologists. These physicians focus largely on interpreting digitized images, which can easily be fed directly to algorithms instead. Massive imaging data sets, com- bined with recent advances in computer vision, will drive rapid improvements in performance, and machine accuracy will soon exceed that of humans. Indeed, radiology is already partway there: algorithms can replace a second radiologist reading mammograms and will soon exceed human accuracy.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “The patient- safety movement will increasingly advocate the use of algorithms over humans — after all, algorithms need no sleep, and their vigilance is the same at 2 a.m. as at 9 a.m. Algorithms will also monitor and interpret streaming physiological data, replacing aspects of anesthesiology and criti- cal care. The time scale for these disruptions is years, not decades.”

    
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 will become an indispensable tool for clinicians seeking to truly understand their patients. As patients’ conditions and medical technologies become more complex, the role of machine learning will grow, and clinical medicine will be challenged to grow with it.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “As in other industries, this challenge will create winners and losers in medicine. But we are optimistic that patients, whose lives and medical histories shape the algorithms, will emerge as the biggest winners as machine learning transforms clinical medicine.”


    Predicting the Future — Big Data, Machine Learning, and Clinical Medicine
Obermeyer Z, Emanuel EJ
N Engl J Med 375;13 September 29, 2016
  • “For example, a radiologist typically views 4000 images in a CT scan of multiple body parts (“pan scan”) in patients with multiple trauma. The abundance of data has changed how radiologists interpret images; from pattern recognition, with clinical context, to searching for needles in haystacks; from inference to detection. The radiologist, once a maestro with a chest ra- diograph, is now often visually fatigued searching for an occult fracture in a pan scan.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “Radiologists should identify cognitively simple tasks that could be addressed by artificial intelligence, such as screening for lung cancer on CT. This involves detecting, measuring, and characterizing a lung nodule, the management of which is standardized. A radiology residency or a medical degree is not needed to detect lung nodules.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “Because pathology and radiology have a similar past and a common destiny, perhaps these specialties should be merged into a single entity, the “information specialist,” whose responsibility will not be so much to extract information from images and histology but to manage the information extracted by artificial intelligence in the clinical context of the patient.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “The information specialist would interpret the important data, advise on the added value of another diagnostic test, such as the need for additional imaging, anatomical pathology, or a laboratory test, and integrate information to guide clinicians. Radiologists and pathologists will still be the physician’s physician.”

    
Adapting to Artificial Intelligence 
Radiologists and Pathologists as Information Specialists 
Jha S, Topol EJ
JAMA. Published online November 29, 2016. doi:10.1001/jama.2016.17438
  • “By virtue of its information technology-oriented infrastructure, the specialty of radiology is uniquely positioned to be at the forefront of efforts to promote data sharing across the healthcare enterprise, including particularly image sharing. The potential benefits of image sharing for clinical, research, and educational applications in radiology are immense. In this work, our group—the Association of University Radiologists (AUR) Radiology Research Alliance Task Force on Image Sharing—reviews the benefits of implementing image sharing capability, introduces current image sharing platforms and details their unique requirements, and presents emerging platforms that may see greater adoption in the future. By understanding this complex ecosystem of image sharing solutions, radiologists can become im- portant advocates for the successful implementation of these powerful image sharing resources.”


    Image Sharing in Radiology— A Primer 
Chatterjee AR et al.
Acad Radiol 2017; 24:286–294
  • “Cloud-based image sharing platforms based on interoperability standards such as the IHE-XDS-I profile are currently the most widely used method for sharing of clinical radiological images and will likely continue to grow in the coming years. Conversely, no single image sharing platform has emerged as a clear leader for research and educational applications. Radiologists, clinicians, investigators, technologists, educators, administrators, and patients all stand to benefit from medical image sharing. With their continued support, more wide- spread adoption of image sharing infrastructure will assuredly improve the standard of clinical care, research, and education in modern radiology.”

    
Image Sharing in Radiology— A Primer 
Chatterjee AR et al.
Acad Radiol 2017; 24:286–294
  • “In summary, radiologists will not be replaced by machines. Radiologists of the future will be essential data scientists of medicine. We will leverage clinical data science and ML to diagnose and treat patients better, faster, and more efficiently. Although this new clinical data science milieu will undoubtedly alter radiology practice, if performed correctly, it will empower radiologists to continue to provide better actionable recommendations on the basis of new insights from the medical images and other relevant data.”


    Big Data and Machine Learning—Strategies for Driving This Bus: A Summary of the 2016 Intersociety Summer Conference
 Kruskal JB et al.
JACR (in press)
  • “Personalized predictive medicine necessitates the modeling of patient illness and care processes, which inherently have long-term temporal dependencies. Healthcare observations, stored in electronic medical records are episodic and irregular in time. We introduce DeepCare, an end-to-end deep dynamic neural network that reads medical records, stores previous illness history, infers current illness states and predicts future medical outcomes. At the data level, DeepCare represents care episodes as vectors and models patient health state trajectories by the memory of historical records.”

    
Predicting healthcare trajectories from medical records: A deep learning approach.
Pham T et al.
J Biomed Inform. 2017 May;69:218-229. 
  • “Built on Long Short-Term Memory (LSTM), DeepCare introduces methods to handle irregularly timed events by moderating the forgetting and consolidation of memory. DeepCare also explicitly models medical interventions that change the course of illness and shape future medical risk.”


    Predicting healthcare trajectories from medical records: A deep learning approach.
Pham T et al.
J Biomed Inform. 2017 May;69:218-229. 
  • “The missing piece in the dialectic around artificial intelligence and machine learning in health care is understanding the key step of separating prediction from action and recommendation. Such separation of prediction from action and recommendation requires a change in how clinicians think about using models developed using machine learning. In 2001, the statistician Breiman suggested the need to move away from the culture of assuming that models that are not causal and cannot explain the underlying process are useless. Instead, clinicians should seek a partnership in which the machine predicts (at a demonstrably higher accuracy), and the human explains and decides on action.”


    What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “The 2 cultures—computer and the physician—must work together. For example, clinicians are biased toward optimistic prediction, often overestimating life expectancy by a factor of 5, while predictive models trained from vast amounts of data do better; using these well-calibrated probability estimates of an outcome, clinicians can then can act appropriately for patients at the highest risk. The lead time a predictive model can offer to allow for an alternative action matters a great deal. Well-calibrated levels of risk for each outcome, and the timely execution of an alternative action, are needed for a model to be useful.”

    
What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “Better diagnosis, and diagnostic algorithms providing more accurate differential diagnoses, might reshape the traditional CPC (clinical problem solving) exercise, just as the development of imaging modalities and sophisticated laboratory testing made the autopsy less relevant.”


    What This Computer Needs Is a Physician: Humanism and Artificial Intelligence
Abraham Verghese, MD1; Nigam H. Shah, MBBS, PhD1; Robert A. Harrington, MD
JAMA (in press) doi:10.1001/jama.2017.19198
  • “Human experts and machines have different strengths. Accordingly, there are tasks that are better suited for machines and others for humans. Some advantages of machines are that they can work 24 hours per day and contemporaneously. Also, machines may be designed to provide consistent analysis for a given input or series of input parameters. This allows for precision and potential for quantification in results reporting. Machines can analyze large volumes of data and find complex associations hidden within these data that may be otherwise difficult for a human to do.”


    Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
  • “There are a number of ways in which machine learning can help radiology practices today, including many tasks that are frequently performed by radiologists and ordering clinicians, such as imaging appropriateness assessment, creating study protocols, and standardization of radiology reporting, that could benefit from automation. Although many of these examples could be implemented using conventional procedural programming methodologies, the machine learning approach holds the promise to perform these tasks with a higher level of proficiency that can improve over time as the system “learns” new data.”

    
Machine Learning in Radiology: 
Applications Beyond Image Interpretation 
Paras Lakhani et al.
J Am Coll Radiol (in press)
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