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Importance: Lower-resource areas in Africa and Asia face a unique set ofhealthcare challenges: the dual high burden of communicable andnon-communicable diseases; a paucity of highly trained primary healthcareproviders in both rural and densely populated urban areas; and a lack ofreliable, inexpensive internet connections. Objective: To address thesechallenges, we designed an artificial intelligence assistant to help primaryhealthcare providers in lower-resource areas document demographic and medicalsign/symptom data and to record and share diagnostic data in real-time with acentralized database. Design: We trained our system using multiple data sets,including US-based electronic medical records (EMRs) and open-source medicalliterature and developed an adaptive, general medical assistant system based onmachine learning algorithms. Main outcomes and Measure: The applicationcollects basic information from patients and provides primary care providerswith diagnoses and prescriptions suggestions. The application is unique fromexisting systems in that it covers a wide range of common diseases, signs, andmedication typical in lower-resource countries; the application works with orwithout an active internet connection. Results: We have built and implementedan adaptive learning system that assists trained primary care professionals bymeans of an Android smartphone application, which interacts with a centraldatabase and collects real-time data. The application has been tested by dozensof primary care providers. Conclusions and Relevance: Our application wouldprovide primary healthcare providers in lower-resource areas with a tool thatenables faster and more accurate documentation of medical encounters. Thisapplication could be leveraged to automatically populate local or national EMRsystems.

Lightweight Mobile Automated Assistant-to-physician for Global Lower-resource Areas