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

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  • “If AI is an unreimbursed business expense, it changes the potential return on investment for all of the outside money that continues to pour into companies creating products using AI. When even the troglodytes of radiology see a future with AI benefiting both patients and the specialty, we should perhaps temper our enthusiasm because of these financial realities. The barriers to entry for new products and services in health care are high, and for good reason. But without the promise of governmental largesse or large inflows of reimbursements from private payers, vendors may take a pass on investing resources in radiology or health care-specific applications for AI.”


    Artificial Intelligence: Who Pays and How?
Schoppe, Kurt
Journal of the American College of Radiology (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 


  • “Most computer-based algorithms in medicine are “expert systems” — rule sets encoding knowledge on a given topic, which are applied to draw conclusions about specific clinical scenarios, such as detecting drug interactions or judging the appropriateness of obtaining imaging. Expert systems work the way an ideal medical student would: they take general principles about medicine and apply them to new patients.”


    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, conversely, approaches problems as a doctor progressing through residency might: by learning rules from data. Starting with patient-level observations, algorithms sift through vast numbers of variables, looking for combinations that reliably predict outcomes.”


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