Abstract
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 Full Text: PDF
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
