The Microsoft AI for Health program: Solving the world’s biggest health issues, one life at a time
May 9, 2023 | William B. Weeks, MD, PhD, MBA, Director, AI for Health Research, AI for Good Lab
Launched in January 2020, Microsoft’s AI for Health program is committed to improving the health of the world’s population. Since then, the AI for Health program has partnered with over 200 grantees on projects designed to accelerate medical research, build research capabilities, increase global health insights, and address health inequities.
Given that the COVID-19 pandemic surprised the world just months after program launch, the AI for Health program rapidly focused efforts on understanding, modeling, and visualizing COVID-19 infection, vaccination, and outcomes. As the pandemic has transformed to endemicity, the program has focused its efforts on three broad areas:
Population health
Bringing together data from health and health influencing sectors and applying visualization techniques and AI to provide decision makers with insights about drivers of disease.
Imaging analytics
Applying AI to image-based data to enhance clinical decision making or increase the reach, precision, and accuracy of imaging tools.
Genomics & proteomics
Applying AI to genomic and proteomic data to predict disease risks or quickly and accurately identify areas in proteins that warrant further investigation for disease intervention.
Current research
Public health. Applying visualization, data analytics, machine learning, and modeling to:
- Understand the relationships between social determinants of health and health outcomes, clinical care, health behaviors, and health status.
- Identify the social determinants of health that—if changed—would have the greatest return to the health of the population.
- Allow researchers and policymakers to develop and define indices of health risks to rapidly identify areas for intervention.
- Focus on relationships between local economic distress and social determinants of health and cardiovascular disease in data-rich cities (opens in new tab) (including New York City, Lisbon, Lausanne, Rio de Janeiro, and Singapore).
Imaging analytics. Efforts here have ranged from applying artificial intelligence and machine learning to:
- Early identification of leprosy from skin photographs in the Brazilian population, thereby preserving people’s fingers, toes, and limbs and, hopefully, accelerating the elimination of this ancient disease (with the Novartis Foundation). Read the paper: Reimagining Leprosy Elimination with AI Analysis of a Combination of Skin Lesion Images with Demographic and Clinical Data.
- Using cell-phone videos to identify children in Mexico that are at risk of retinopathy of prematurity the leading cause of preventable childhood blindness (with Clínica Oftalmológica Peñaranda, Red RoP de la Provincia de Buenos Aires, Centro integral de salud visual Daponte).
- Using captured images of tympanic membranes to identify Australian Aboriginal and Torres Strait children who have chronic otitis media, helping to prevent childhood deafness (with the University of Sydney). Read the paper: Evaluating the Generalizability of Deep Learning Image Classification Algorithms to Detect Middle Ear Disease Using Otoscopy
- Radiological images, to identify, segment, and indicate abnormalities in the pancreas, the female breast, the liver, the lungs (with a multiplicity of partners).
Genomics and proteomics. Applying artificial intelligence, machine learning, and modeling to:
- Support the development of a biobank repository (opens in new tab) for the Mexican population.
- Integrate genomics data to analyses of health outcomes from imaging.
- Identify potential drug binding sites from protein simulations with Folding@Home. Read the paper: Predicting the Locations of Cryptic Pockets from Single Protein Structures Using the PocketMiner Graph Neural Network
Looking forward, we anticipate continuing the above work and expanding efforts to include the application of large language models in our analytic repertoire. Further, we will continue to form deep, collaborative, global relationships with renown not-for-profit organizations (like Novartis Foundation) and academic institutions (like Tec Monterrey, Johns Hopkins University, New York University, and the Institute for Health Metrics and Evaluation at the University of Washington).
“The fact that a health problem can be predicted in advance will reshape the cost curve of healthcare.”
— Satya Nadella
It will also dramatically change the health and wellbeing of the world’s population. Artificial intelligence is the tool that allows for such advanced predictions; its application in healthcare will radically transform how healthcare is practiced and lead to a healthier, more productive, and more equitable world.