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    Here come the machines: Progress or replacing MDs?

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

    “The role of the radiologist will be obsolete in five years…There’s no reason a human should be doing [diagnostic radiology].”

    —Vinod Khosla, famed health-tech venture capitalist

    Medicine—including ophthalmology—has come a long way over the past century, driven by brilliant and committed people and better technology.

    In ophthalmology alone, we can see the progress in laser vision correction and cataract surgery over the past 20 years to premium lenses. Treatment of glaucoma is entering a renaissance with a multitude of minimally invasive techniques.

    One of the things that drew me to ophthalmology was the wide use of advanced technology. For example, retinal imaging, which utilizes some of the most advanced technologies available (such as adaptive optics imaging) to femtosecond laser-based eye surgeries. Many of my colleagues echo these sentiments.

    As we look at this emerging technology, we can ask ourselves: Is medicine (and the greater world) entering a new dawn of artificial intelligence (AI) and technology?

    What does this mean for ophthalmology?

    GoogLeNet AI reviewed thousands of medical images supplied by a Dutch university and was able to identify malignant tumors in breast cancer images with an 89% accuracy rate, compared with 73% for its human counterparts.

    -Detecting Cancer Metastases on Gigapixel Pathology Images, Google/Alphabet

    A neural network algorithm proves to be more sensitive than experienced radiologists for detecting thyroid nodules in ultrasound imaging.
    -American Journal of Roentgenology, 2016

    AI holds great promise in medicine. Machine learning and deep learning--both subfields of AI--are particularly of interest. In areas such as pathology and radiology, pattern recognition is the basis for making a diagnosis.

    As we see in the studies mentioned, machines are exceptional at recognizing complicated patterns at a complexity that only has been possible by humans until now. Furthermore, machines are faster and more consistent, without work hour rules, overtime costs, or costly benefits.

    Ophthalmic diagnosis utilizes pattern recognition extensively. Most ophthalmology diagnoses can be made exclusively with the ophthalmic examination, and more with addition of modern multimodal imaging. This likely means that machine-based diagnostic modalities are well suited for the ophthalmology space.

    Machine learning in ophthalmology

    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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