Dr. Bhramar Mukherjee, an Indian American researcher at Yale, has won the 2026 Hecht Global Health Policy Innovation and Systems Impact Faculty Network Award for researching use of AI models for screening anemia in India.
Her research will bring together the Yale School of Public Health (YSPH) and the Khushi Baby Association, a nonprofit organization working in India, to test whether deep learning-based artificial intelligence (AI) models for image-based anemia screening can work effectively in field settings.
These models will be used by accredited social health activists in India for tasks such as consent, image capture, and result interpretation, with algorithms updated in real time, according to a Yale Institute for Global Health (YIGH) media release.
“The question my Hecht Award project sets out to answer is whether deep learning-based AI models for image-based screening of anemia can be used effectively in field settings,” says Mukherjee, Senior Associate Dean of Public Health Data Science and Data Equity at Yale School of Public Health.
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The award is intended to serve as a launching point, helping recipients build a foundation for larger-scale research and future funding.
Mukherjee serves as the Anna M.R. Lauder Professor of Biostatistics and Professor of Chronic Disease Epidemiology at YSPH. She holds a secondary appointment in the Department of Statistics and Data Science and is affiliated with the MacMillan Center and the Institute for the Foundations of Data Science. She serves on the Yale Cancer Center Director’s cabinet.
With a master’s from Indian Statistical Institute, Applied Statistics and Data Analysis, Mukherjee earned her MS and PhD in Statistics from Purdue University, West Lafayette, Indiana.
India bears the world’s largest burden of maternal anemia, with 52% of pregnant women nationally and over 60% in tribal and rural districts affected. Current methods for community-level screening often miss moderate anemia, which is the threshold for treatment escalation and referral. This leads to a series of health management challenges: undetected anemia, missed referrals, and preventable maternal and neonatal harm.
AI-enabled smartphone clinical decision support tools (CDST) for screening could close this gap, but globally these tools are being built and refined faster than they are being evaluated for real-world use, which leaves policymakers without the evidence needed to adopt them.
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The project will focus on a mixed-methods, three-step adaptation, evaluation, and implementation study of MAHILA (Maternal Anemia Hemoglobin Imaging for Last-mile Assessment), a non-invasive smartphone CDST.
The study will show how well the tool works in community settings, how consistently it can be used, and what training and supervision community health workers need.
It will also produce a policy briefing book. Students in collaboration with Khushi Baby data scientists and student interns recruited from Indian institutions will work on the real-world adaptation and transportability of the base deep learning model.