{"id":940080,"date":"2023-05-11T16:33:31","date_gmt":"2023-05-11T23:33:31","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-blog-post&#038;p=940080"},"modified":"2023-05-12T12:05:40","modified_gmt":"2023-05-12T19:05:40","slug":"microsofts-ai-for-health-program-solving-the-worlds-biggest-health-issues-one-life-at-a-time","status":"publish","type":"msr-blog-post","link":"https:\/\/www.microsoft.com\/en-us\/research\/articles\/microsofts-ai-for-health-program-solving-the-worlds-biggest-health-issues-one-life-at-a-time\/","title":{"rendered":"The Microsoft AI for Health program: Solving the world\u2019s biggest health issues, one life at a time"},"content":{"rendered":"\n<p><em>May 9, 2023 | <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/people\/wiweeks\/\">William B. Weeks<\/a>, MD, PhD, MBA, Director, AI for Health Research, AI for Good Lab<\/em><\/p>\n\n\n\n<p>Launched in January 2020, Microsoft\u2019s AI for Health program is committed to improving the health of the world\u2019s population.&nbsp;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.<\/p>\n\n\n\n<p>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:<\/p>\n\n\n\n<div class=\"wp-block-columns is-layout-flex wp-container-core-columns-is-layout-9d6595d7 wp-block-columns-is-layout-flex\">\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Population health<\/strong><\/p>\n\n\n\n<p>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.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Imaging analytics<\/strong><\/p>\n\n\n\n<p>Applying AI to image-based data to enhance clinical decision making\u202for increase the reach, precision, and accuracy of imaging tools.<\/p>\n<\/div>\n\n\n\n<div class=\"wp-block-column is-layout-flow wp-block-column-is-layout-flow\">\n<p><strong>Genomics & proteomics<\/strong><\/p>\n\n\n\n<p>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.<\/p>\n<\/div>\n<\/div>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h3 id=\"current-research\" class=\"wp-block-heading\">Current research<\/h3>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"355\" src=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-1024x355.png\" alt=\"AI for Health - US map visualization from the Health Equity Dashboard which allows users to compare county-level health data quickly and easily across a variety of measures, including health status, health services utilization and quality, and social determinants of health.\u202f\" class=\"wp-image-940089\" srcset=\"https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-1024x355.png 1024w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-300x104.png 300w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-768x266.png 768w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-1536x533.png 1536w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555-240x83.png 240w, https:\/\/www.microsoft.com\/en-us\/research\/wp-content\/uploads\/2023\/05\/US-map-graphic_1600x555.png 1600w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><figcaption class=\"wp-element-caption\">Visualization from the Health Equity Dashboard which allows users to compare county-level health data quickly and easily across a variety of measures, including health status, health services utilization and quality, and social determinants of health.\u202f<\/figcaption><\/figure>\n\n\n\n<div class=\"wp-block-buttons is-content-justification-center is-content-justification-center is-layout-flex wp-container-core-buttons-is-layout-16018d1d wp-block-buttons-is-layout-flex\">\n<div class=\"wp-block-button\"><a data-bi-type=\"button\" class=\"wp-block-button__link wp-element-button\" href=\"https:\/\/aka.ms\/healthequity\" target=\"_blank\" rel=\"noreferrer noopener\">View the dashboard<\/a><\/div>\n<\/div>\n\n\n\n<p><strong>Public health<\/strong>. Applying visualization, data analytics, machine learning, and modeling to:<\/p>\n\n\n\n<ul class=\"wp-block-list\" start=\"1\">\n<li>Understand the relationships between social determinants of health and health outcomes, clinical care, health behaviors, and health status.<\/li>\n\n\n\n<li>Identify the social determinants of health that\u2014if changed\u2014would have the greatest return to the health of the population.<\/li>\n\n\n\n<li>Allow researchers and policymakers to develop and define indices of health risks to rapidly identify areas for intervention.<\/li>\n\n\n\n<li>Focus on relationships between local economic distress and social determinants of health and <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.novartisfoundation.org\/transforming-population-health\/ai4healthycities\" target=\"_blank\" rel=\"noopener noreferrer\">cardiovascular disease in data-rich cities<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> (including New York City, Lisbon, Lausanne, Rio de Janeiro, and Singapore).<\/li>\n<\/ul>\n\n\n\n<p><strong>Imaging analytics<\/strong>. Efforts here have ranged from applying artificial intelligence and machine learning to:<\/p>\n\n\n\n<ul class=\"wp-block-list\" start=\"1\">\n<li>Early identification of leprosy from skin photographs in the Brazilian population, thereby preserving people\u2019s fingers, toes, and limbs and, hopefully, accelerating the elimination of this ancient disease (with the <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.novartis.com\/diseases\/leprosy\" target=\"_blank\" rel=\"noopener noreferrer\">Novartis Foundation<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>). Read the paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/reimagining-leprosy-elimination-with-ai-analysis-of-a-combination-of-skin-lesion-images-with-demographic-and-clinical-data\/\" target=\"_blank\" rel=\"noreferrer noopener\">Reimagining Leprosy Elimination with AI Analysis of a Combination of Skin Lesion Images with Demographic and Clinical Data<\/a>.<\/li>\n\n\n\n<li>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 Cl\u00ednica Oftalmol\u00f3gica Pe\u00f1aranda, Red RoP de la Provincia de Buenos Aires, Centro integral de salud visual Daponte).<\/li>\n\n\n\n<li>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 <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/www.drumbeat.ai\/\" target=\"_blank\" rel=\"noopener noreferrer\">University of Sydney<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>). Read the paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/evaluating-the-generalizability-of-deep-learning-image-classification-algorithms-to-detect-middle-ear-disease-using-otoscopy\/\" target=\"_blank\" rel=\"noreferrer noopener\">Evaluating the Generalizability of Deep Learning Image Classification Algorithms to Detect Middle Ear Disease Using Otoscopy<\/a>&nbsp;<\/li>\n\n\n\n<li>Radiological images, to identify, segment, and indicate abnormalities in the pancreas, the female breast, the liver, the lungs (with a multiplicity of partners).<\/li>\n<\/ul>\n\n\n\n<p><strong>Genomics and proteomics<\/strong>.&nbsp;Applying artificial intelligence, machine learning, and modeling to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Support the development of <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/content.tecsalud.mx\/proyectoorigen\" target=\"_blank\" rel=\"noopener noreferrer\">a biobank repository<span class=\"sr-only\"> (opens in new tab)<\/span><\/a> for the Mexican population.&nbsp;<\/li>\n\n\n\n<li>Integrate genomics data to analyses of health outcomes from imaging.<\/li>\n\n\n\n<li>Identify potential drug binding sites from protein simulations with <a class=\"msr-external-link glyph-append glyph-append-open-in-new-tab glyph-append-xsmall\" href=\"https:\/\/foldingathome.org\/?lng=en\" target=\"_blank\" rel=\"noopener noreferrer\">Folding@Home<span class=\"sr-only\"> (opens in new tab)<\/span><\/a>. Read the paper: <a href=\"https:\/\/www.microsoft.com\/en-us\/research\/publication\/predicting-the-locations-of-cryptic-pockets-from-single-protein-structures-using-the-pocketminer-graph-neural-network\/\" target=\"_blank\" rel=\"noreferrer noopener\">Predicting the Locations of Cryptic Pockets from Single Protein Structures Using the PocketMiner Graph Neural Network<\/a><\/li>\n<\/ul>\n\n\n\n<p>Looking forward, we anticipate continuing the above work and expanding efforts to include the application of large language models in our analytic repertoire.&nbsp;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).<\/p>\n\n\n\n<blockquote class=\"wp-block-quote is-style-spectrum is-layout-flow wp-block-quote-is-layout-flow\">\n<p>&#8220;The fact that a health problem can be predicted in advance will reshape the cost curve of healthcare.&#8221;<\/p>\n<cite>\u2014 Satya Nadella<\/cite><\/blockquote>\n\n\n\n<p>It will also dramatically change the health and wellbeing of the world\u2019s population.\u00a0Artificial 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.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>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.<\/p>\n","protected":false},"author":42735,"featured_media":940083,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-content-parent":696544,"msr_hide_image_in_river":0,"footnotes":""},"research-area":[],"msr-locale":[268875],"msr-post-option":[],"class_list":["post-940080","msr-blog-post","type-msr-blog-post","status-publish","has-post-thumbnail","hentry","msr-locale-en_us"],"msr_assoc_parent":{"id":696544,"type":"group"},"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/940080","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-blog-post"}],"author":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/users\/42735"}],"version-history":[{"count":16,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/940080\/revisions"}],"predecessor-version":[{"id":940755,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-blog-post\/940080\/revisions\/940755"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/940083"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=940080"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=940080"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=940080"},{"taxonomy":"msr-post-option","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-post-option?post=940080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}