Abstract
In their Perspective, Ara Darzi and Hutan Ashrafian give us a tour of the future policymaker's machine learning toolkit.
Highlights
The most prominent contribution of artificial intelligence (AI) to health policy knowledge currently resides within the application of Machine learning (ML) to large, population-level datasets such as those from medical imaging, electronic health records (EHRs), and whole-genome studies
ML flagging systems have been applied to identify patients at a high risk for colorectal cancer based on a simple complete blood count test [9]
This test was utilised to help identify individuals at high risk of colorectal cancer who were noncompliant to a national screening programme; this technology may eventually have the capacity to offer full population screening
Summary
The most prominent contribution of AI to health policy knowledge currently resides within the application of ML to large, population-level datasets such as those from medical imaging, electronic health records (EHRs), and whole-genome studies. November 13, 2018 million individuals in the form of EHRs or even patient health records (PHRs; in which patient data accompany the patients directly) can offer one of the largest datasets worldwide for analysis by ML. This could offer improved predictions for clinical outcomes from current records and could provide novel hypothesis-generating concepts that may lead research to better understand disease behaviours and their treatments.
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