A Collaborative Model for Integration of Artificial Intelligence in Primary Care

Serge Dolgikh


The cost of primary care is rapidly increasing in the developed world, and improving the accuracy of screening and diagnostic testing as well as other areas of primary care can be seen as an essential component in ensuring the long-term sustainability of the quality and efficiency of public health care systems. In this study, the authors propose a simple yet robust model of collaborative decision-making incorporating machine and human competences whereby the strengths and advantages of artificial intelligence methods can be harnessed to improve the overall accuracy of essential testing, diagnostics, screening, and other critical areas of patient care while addressing concerns and ensuring safety and complete human control over the course of diagnostics and treatment.


Doi: 10.28991/HEF-2021-02-04-07

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Artificial Intelligence; Diagnostics; Decision Making Models, Human and Machine Intelligences.


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DOI: 10.28991/HEF-2021-02-04-07


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