According to the McKinsey Health Institute, while there has been significant rise in life expectancy for both men and women over the past 2 centuries, women spend more of their lives in poor health and with degrees of disability (the “health span” rather than the “life span”). This affects her ability to be present and/or productive at home, in the workforce, and in the community and reduces her earning potential ("Closing the women’s health gap: A $1 trillion opportunity to improve lives and economies").
While the report provides comprehensive data on healthcare gap for women, and makes an economic argument for addressing the data gaps, investment and innovation, Dr. Elinor Cleghorn points out in her powerful book "Unwell Women" how health science itself has failed women over centuries through misunderstansing and misdiagnosis of women's health issues. If we combine this with the lack of healthcare access for women underserved by the healthcare system, the urgency of action becomes even more evident.
Technology is seen as a great equalizer in settings of poor infrastructure - be it in healthcare, education or the economy - by acting as a powerful distribution channel. More women globally have their own phones (even in rural areas) - now more than ever before. My project builds upon this key insight and brings the power of artificial intelligence to this problem.
“AI सखी” (the word सखी "sakhi" means friend in Hindi) is a generative AI-powered expert system that can be delivered to under-served communities digitally. It can be used directly by women, or by rural healthcare workers in villages.
The system is based on Large Language AI Models (LLMs) from OpenAI. It augments generalist knowledge of these models with domain-specific knowledge bases and can accommodate targeted, population-specific data.
The system can operate in regional languages (such as Hindi, spoken by a large number of villagers in India) and has text-to-speech / speech-to-text capabilties.
The system also acts as a platform for longitudinal data collection at the point of care - both for convenient physician access when intervention is needed - and for developing statistical and predictive tools for women’s health.
Prototype 1
Prototype 2
Here are some pictures from my high school presentation :-)