The Sindh Health Department, Lahore University of Management Sciences (LUMS) and Aga Khan University (AKU) have signed a three-year Letter of Collaboration to integrate a National AI Hub for Maternal, Newborn and Child Health into the province’s public health system.
The initiative aims to bring artificial intelligence tools to frontline healthcare to improve health outcomes for vulnerable women and children. The agreement was signed at the office of Sindh Minister for Health and Population Welfare Dr Azra Fazal Pechuho by Health Secretary Tahir Hussain, LUMS Associate Professor Dr Maryam Mustafa, Hub Director and Principal Investigator, and AKU Dean of the Medical College Dr Karim Damji.
Dr Anita Zaidi, President of Gender Equality at the Gates Foundation, attended the signing ceremony. The Gates Foundation is supporting the Hub through a multi-year grant. AKU Associate Professor of Paediatrics and Child Health Dr Zahra Hoodbhoy, who has been working on point-of-care ultrasound AI models, was also present.
Under the agreement, the three institutions will jointly roll out risk-stratification models, clinical decision-support systems and voice-assisted tools for frontline healthcare providers delivering maternal and child health services.
Dr Pechuho said the initiative would focus on primary healthcare facilities. “I would like to see healthier babies, and prematurity rates go down,” she said, adding that she hoped the initiative would also help identify congenital problems and improve paramedical services.
The Hub is the outcome of a multi-year collaboration among computer scientists, human-centred design experts and clinical epidemiologists. Its clinical research is being led by AKU’s Dr Fyezah Jehan, Chair of the Department of Paediatrics and Child Health. Her clinical trial networks, maternal-newborn cohort datasets and field-based epidemiological research provide clinical data for validating predictive algorithms and developing AI models suited to healthcare delivery in low-resource settings.
Speaking after the signing, Dr Damji said the partnership would focus on using AI to improve maternal, newborn and child health services. “We have brilliant faculty and that’s the magic,” he said. “I think it’s fantastic because we are serving people better, in this case maternal, newborn, child health, and looking at ways to understand what value we can offer for safer deliveries for the mothers, but also for the children.” He said AI should be embraced while ensuring that its use was as cost-effective and efficient as possible.
The Hub’s technology builds on Dr Maryam Mustafa’s Awaaz-e-Sehat, a voice-enabled electronic record management platform initially funded through a Grand Challenges grant. Her team’s work in localised speech recognition and natural-language technologies is designed for low-literacy environments, enabling frontline health workers to interact with AI tools in local languages.
Dr Mustafa said Pakistan was lagging behind other countries in adopting AI tools in healthcare, partly because of the lack of formalised data infrastructure needed to scale such technologies. “The tech part is actually easier to build. It is how do you embed it in existing infrastructures and get governments to adopt and scale,” she said, adding that even people in remote areas should have access to such tools.
Under the partnership, the Sindh Health and Population Welfare Department will provide strategic oversight, facilitate integration with provincial health facilities, ensure alignment with national health policies and support public-sector deployment.
LUMS will serve as the technical partner, leading model development, algorithm validation, speech technologies and technical safeguards. AKU will act as the clinical, research and implementation partner, guiding clinical safety, real-world trial evaluation and field-level implementation across primary healthcare facilities.
The collaboration will follow national regulatory standards, data-privacy guidelines and ethical safeguards. The partners have stressed that the AI solutions will assist, rather than replace, clinical judgment.
