While it is relatively easy to imagine artificial intelligence taking over pattern recognition type of physician work (such as reading images like radiologists or identify skin conditions by looking at the skin like dermatologists), the job of a general internist is much more demanding and requires assimilation of data inputs from a large number of sources, hence making it difficult for an artificial intelligence system to accurately predict a disease (or prepare a list of differential diagnoses).
On the other hand, the process is not, and should not be, very difficult for a well-crafted system. Razzaki et al tested such an artificial intelligence algorithm and compared it with physicians in its ability to predict diagnosis. For their study, they adopted a semi-naturalistic, role-play paradigm that simulated a realistic consultation between a patient and either their artificial intelligence system or a human doctor. Their study was designed to assess both the clinical (diagnostic and triage) accuracy and the ability to gather all of the relevant history from the patient. What they found was that their artificial intelligence algorithm was able to provide diagnostic and triage advice with a level of accuracy and safety approaching that of human doctors. If there study results are replicated then such systems may not only reduce costs and improve access to healthcare worldwide but may also provide better standardization of care delivered to patients.
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