The Health Department of the Government of Sindh, the Lahore University of Management Sciences, and the Aga Khan University have signed a three-year letter of collaboration to integrate a National AI Hub for Maternal, Newborn and Child Health into the public health system. The agreement is intended to bring artificial intelligence directly into frontline care settings, with the stated goal of improving health indicators for some of the most vulnerable women and children accessing Sindh’s public healthcare system.
The agreement was formally signed at the office of the Sindh Minister for Health and Population Welfare, with signatories including Health Secretary Tahir Hussain, Associate Professor Dr Maryam Mustafa, who serves as Hub Director and Principal Investigator at LUMS, and Dr Karim Damji, Dean of the Medical College at AKU. The involvement of senior leadership from both a provincial health department and two of Pakistan’s leading academic institutions reflects the collaborative, multi-institutional structure behind the initiative, bringing together government health administration with academic research and medical expertise.
President of Gender Equality at the Gates Foundation, Dr Anita Zaidi, was present at the signing, with the Gates Foundation supporting the Hub through a multi-year grant, providing the financial backing needed to sustain the initiative’s research and implementation work over its three-year timeline. Also present was AKU Paediatrics and Child Health Associate Professor Dr Zahra Hoodbhoy, who has been working specifically on point-of-care ultrasound AI models, adding a direct link between the Hub’s broader mission and existing AI research already underway within AKU’s paediatric health programmes.
Under the agreement, the three institutions will jointly roll out risk-stratification models, clinical decision-support systems, and voice-assisted tools specifically designed for frontline care providers delivering maternal and child health services. These tools are intended to support healthcare workers operating at the point of care, potentially helping identify high-risk cases earlier and providing decision support in settings where specialist expertise may not always be readily available. By combining risk-stratification technology with voice-assisted tools, the initiative appears designed to make AI-supported care more accessible to frontline health workers who may be operating in resource-constrained or high-volume clinical environments across Sindh’s public health system, extending specialized clinical insight to a broader network of providers than would otherwise be possible through traditional staffing and training alone.
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