  Importance: Lower-resource areas in Africa and Asia face a unique set of
healthcare challenges: the dual high burden of communicable and
non-communicable diseases; a paucity of highly trained primary healthcare
providers in both rural and densely populated urban areas; and a lack of
reliable, inexpensive internet connections. Objective: To address these
challenges, we designed an artificial intelligence assistant to help primary
healthcare providers in lower-resource areas document demographic and medical
sign/symptom data and to record and share diagnostic data in real-time with a
centralized database. Design: We trained our system using multiple data sets,
including US-based electronic medical records (EMRs) and open-source medical
literature and developed an adaptive, general medical assistant system based on
machine learning algorithms. Main outcomes and Measure: The application
collects basic information from patients and provides primary care providers
with diagnoses and prescriptions suggestions. The application is unique from
existing systems in that it covers a wide range of common diseases, signs, and
medication typical in lower-resource countries; the application works with or
without an active internet connection. Results: We have built and implemented
an adaptive learning system that assists trained primary care professionals by
means of an Android smartphone application, which interacts with a central
database and collects real-time data. The application has been tested by dozens
of primary care providers. Conclusions and Relevance: Our application would
provide primary healthcare providers in lower-resource areas with a tool that
enables faster and more accurate documentation of medical encounters. This
application could be leveraged to automatically populate local or national EMR
systems.
