Applying Artificial Intelligence Methods for the Estimation of Disease Incidence: The Utility of Language Models.

AI-driven digital health tools often rely on estimates of disease incidence or prevalence, but obtaining these estimates is costly and time-consuming. We explored the use of machine learning models that leverage contextual information about diseases from unstructured text, to estimate disease incidence. We used a class of machine learning models, called language models, to extract contextual information relating to disease incidence.

A Comparison of Artificial Intelligence and Human Doctors for the Purpose of Triage and Diagnosis.

AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information.