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    Here come the machines: Progress or replacing MDs?

    Medicine entering new dawn with artificial intelligence that may change physician dynamic


    Machines better?

    Physicians have asked the question: “Will a machine diagnose as well as a physician?” We are one step closer to knowing that answer and it looks as though the answer may be a resounding “Yes,” especially for pattern recognition-based diagnosis.

    Today, with machine learning showing promise across society and in areas of medicine, it perhaps is becoming clearer, although with much work to do. We need to ask ourselves, as skilled subspecialists charged with preserving patients’ vision, how do we utilize these AI-based systems to provide the best care possible to patients?



    Peter A. Karth, MD
    Peter A. Karth, MD, is clinical instructor at Stanford University Department of Ophthalmology, Stanford, California, USA. He may ...

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    • UBM User
      In general I agree with Dr. Karth and as a consequence, the digitalization of retinal images that will allow physicians to manage individual risk prediction by monitoring digitized results of image interpretation that will enable the monitoring in individuals of digitized variable changes chronologically of which Dr. Eric Tool has enumerated. Also the immediate delivery of results of imaged screening will significantly improve follow-up outcomes as it has been demonstrated that delayed communication of the results to the patient results in 80% failure to follow-up. Certainly imaging provides much improved recognition of lesions than provider examination and should be done without pupil dilation as this severely reduces "compliance" with recommended screening. However retinal images vary tremendously in the information about the disease process that is revealed, currently based on very old lesion knowledge, and machine learning, whatever the methodology is based on the imaged aspects. Non-myd cameras that provide color fundus photographs are poor and the images presented are not correlated with vision or vision prognosis and themselves are too expensive. SLO's and OCT's, while able to image through undiluted pupils, are far too expensive and provide (thus far) image analysis outcomes that also are similarly poor. What are needed are newly derived outcomes that examine the neuronopathy of the diabetes process and interpretation of these enhanced images by experts that can be digitized and crowd sourced to provide the screening that is needed.


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