{"id":979329,"date":"2023-11-26T21:16:53","date_gmt":"2023-11-27T05:16:53","guid":{"rendered":"https:\/\/find.codeghost.online\/en-us\/research\/?post_type=msr-project&p=979329"},"modified":"2026-07-07T06:15:31","modified_gmt":"2026-07-07T13:15:31","slug":"healthcare","status":"publish","type":"msr-project","link":"https:\/\/find.codeghost.online\/en-us\/research\/project\/healthcare\/","title":{"rendered":"Healthcare Research at MSR India"},"content":{"rendered":"
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Healthcare Research at MSR India<\/h1>\n\n\n\n

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At Microsoft Research Lab India, our healthcare research spans smartphone-based low-cost diagnostics, generative AI chatbots that support the healthcare ecosystem, and multimodal models for radiology across 2D and 3D imaging.\u00a0<\/p>\n\n\n\n

Low-cost diagnostics<\/h2>\n\n\n\n

Healthcare is not accessible to a huge population across the globe. There are a variety of reasons for that including skewed doctor-to-patient ratio, unskilled to semi-skilled healthcare workers, long waiting and long commute to see a doctor which also results in loss of income for daily wage earners. Our aim is to democratize healthcare. And our vision is to develop low-cost smartphone-based diagnostic solutions<\/strong> with AI assistant embedded in them, to enable community health workers, teachers, primary clinicians, and even Swiggy\/Amazon delivery personnel, to perform preliminary screening of certain diseases with minimal training. With that vision in mind, over the past four years, we have been working towards developing a variety of diagnostics tool—detecting keratoconus using a smartphone-based corneal topographer (SmartKC<\/a>), estimating refractive errors (Auto-Retinoscopy<\/a>), computing the dryness level of the eye, classifying crackle and wheeze lung sound (RespireNet (opens in new tab)<\/span><\/a>), and estimating height (opens in new tab)<\/span><\/a> of children for malnutrition prediction—all using smartphone. These works has been done in close collaboration with hospitals like Sankara Eye Hospital and NGO\u2019s like WeltHungerHilfe.<\/p>\n\n\n\n

HealthBots<\/h2>\n\n\n\n

Patients and their caregivers require timely, trustworthy, detailed, and accurate information about their condition and treatment. Access to such information can help reduce anxiety and improve preparedness before, during, and after treatment. To address this need, we designed and developed a family of AI-powered HealthBots that leverage state-of-the-art generative AI models grounded in clinician-provided knowledge bases. These systems, including CataractBot (opens in new tab)<\/span><\/a>, OncoBot, and DRBot, assist patients and caregivers by answering questions related to pre-treatment preparation, treatment procedures, recovery, and post-treatment care. A key aspect of these systems is the expert-in-the-loop <\/strong>component, wherein healthcare professionals and patient coordinators review bot responses and provide feedback, enabling continuous improvement in answer quality over time. The bots are designed to be multimodal and multilingual. Learn more about the HealthBots<\/a> project.<\/p>\n\n\n\n

Building on this work, we have extended the HealthBot paradigm beyond patients to support frontline healthcare workers through ASHABot (opens in new tab)<\/span><\/a>, an AI assistant designed to help Accredited Social Health Activists (ASHAs) access reliable health information and guidance in their day-to-day work.<\/p>\n\n\n\n

Multimodal AI in Radiology<\/h2>\n\n\n\n

Through project\u00a0CARE\u00a0<\/a>(Clinically Aligned Radiology Expertise), we investigate how AI assistants can be integrated into radiology workflows in a reliable and clinically meaningful manner. CARE develops foundation and multimodal models for radiology across 2D and 3D imaging, with an emphasis on clinical fidelity, calibration, and interpretability \u2014 aligning model behavior with the way radiologists reason and validating it through collaborations with hospital partners.<\/p>\n\n\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

At Microsoft Research Lab India, our healthcare research spans smartphone-based low-cost diagnostics, generative AI chatbots that support the healthcare ecosystem, and multimodal models for radiology across 2D and 3D imaging. <\/p>\n","protected":false},"featured_media":986760,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13553,13568],"msr-locale":[268875],"msr-impact-theme":[261673],"msr-pillar":[],"class_list":["post-979329","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-medical-health-genomics","msr-research-area-technology-for-emerging-markets","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[713035,776404,787486,857817,871818,1085772,1085778,1085784,1106766,1148450,1156393,1174071],"related-downloads":[],"related-videos":[],"related-groups":[144784],"related-events":[],"related-opportunities":[],"related-posts":[],"related-articles":[],"tab-content":[],"related-researchers":[{"type":"user_nicename","display_name":"Mohit Jain","user_id":38769,"people_section":"Related people","alias":"mohja"},{"type":"user_nicename","display_name":"Nipun Kwatra","user_id":37634,"people_section":"Related people","alias":"nkwatra"},{"type":"user_nicename","display_name":"Mercy Ranjit","user_id":43716,"people_section":"Related people","alias":"meranjit"}],"msr_research_lab":[199562],"msr_impact_theme":["Health"],"_links":{"self":[{"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/979329","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":15,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/979329\/revisions"}],"predecessor-version":[{"id":1178056,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/979329\/revisions\/1178056"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/media\/986760"}],"wp:attachment":[{"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/media?parent=979329"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=979329"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=979329"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=979329"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/find.codeghost.online\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=979329"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}