Smartphone sensor–enabled health measurement, as part of medical device technologies, is rapidly progressing from theory to practice, therefore utilizing comprehensive health datasets to train and validate artificial intelligence (AI) models for digital health applications.

There is a critical gap in the reach of medical devices in low–resource settings where supply of basic health technologies like pulse oximeters, hemoglobinometers, and patient monitors do not meet clinical demand, while many companies prioritize wellness products for high–income populations.

A strong need exists to strengthen the innovation ecosystem to allow AI researchers to externalize algorithms more easily to deliver public health products faster, specifically for solutions intended for use by health care providers in low–resource clinical settings.

The goal of this guide is to provide insight and ideas to merge the innovative ecosystem of wellness products and medical device AI technology to suit all populations and deliver basic health technology to those who need it.

The intended audiences for this toolkit are organizations working to develop AI tools for public health, as well as groups investing in the development of these tools for low–resource settings.