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    Review of 'Adoption of machine learning for medical diagnosis'

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    Adoption of machine learning for medical diagnosisCrossref
    Average rating:
        Rated 5 of 5.
    Level of importance:
        Rated 4 of 5.
    Level of validity:
        Rated 5 of 5.
    Level of completeness:
        Rated 5 of 5.
    Level of comprehensibility:
        Rated 5 of 5.
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    Adoption of machine learning for medical diagnosis

     Mohammed Shaheen (corresponding) (2021)
    The healthcare industry has historically been an early adopter of technology advancements and has reaped significant benefits. Machine learning (an artificial intelligence subset) is being used in a variety of health-related fields, including the invention of new medical treatments, the management of patient data and records, and the treatment of chronic diseases. One of the most important uses of machine learning in healthcare is the detection and diagnosis of diseases and conditions that are otherwise difficult to identify. This can range from tumors that are difficult to detect in their early stages to other hereditary illnesses. This research identifies and discusses the various usages of machine learning in medical diagnosis.
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      Review information

      10.14293/S2199-1006.1.SOR-COMPSCI.APHMKA6.v1.RDALNW

      This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

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      This is very interesting article about the implementation of machine learning strategies for the improvement of medical diagnosis. It is worth publishing. 

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