{"id":978063,"date":"2023-11-24T01:00:00","date_gmt":"2023-11-24T09:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=978063"},"modified":"2026-02-03T08:28:34","modified_gmt":"2026-02-03T16:28:34","slug":"project-maira","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/project-maira\/","title":{"rendered":"Project MAIRA"},"content":{"rendered":"
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Project MAIRA<\/h1>\n\n\n\n

Multimodal AI for Radiology Applications<\/p>\n\n\t\t\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t<\/div>\n\t\t<\/div>\n\t<\/div>\n<\/section>\n\n\n\n\n\n

Project MAIRA is a research project from Microsoft Health Futures<\/a> that builds innovative, multimodal AI technology to assist radiologists in delivering effective patient care and to empower them in their work. The goal of the project is to leverage rich healthcare data \u2013 including medical domain knowledge, temporal sequences of medical images and corresponding radiology reports, and other clinical context information \u2013 as inputs to developing multimodal frontier models that can be scaled and fine-tuned to many different radiology applications.<\/p>\n\n\n\n

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Read RAD-DINO technical report<\/a><\/div>\n\n\n\n
Read MAIRA-2: Grounded Radiology Report Generation technical report<\/a><\/div>\n<\/div>\n\n\n\n
\"Schematic<\/figure>\n\n\n\n
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New approaches combining various data modalities and their temporal connections enable AI tasks such as: detecting reporting errors; auto-generating draft reports; or improving disease progression assessments and their quantification over time. Such innovations will play a crucial role in detecting missed clinical observations; improving reporting capacity in an already heavily over-burdened radiology workforce; and assisting reporting consistency, accuracy and equity \u2013 all of which serve to reduce shortcomings in existing imaging workflows and to increase patient safety and care quality.  <\/p>\n\n\n\n

To advance this work requires a human-centred, responsible approach to AI development that places clinical utility and careful workflow integration at its core. This involves close stakeholder engagements and clinical collaborations within real-world healthcare contexts; the creation of a new research frontier in evaluating large multimodal models in a clinically relevant manner; and an overall drive to move from technical AI innovation towards successful healthcare delivery. <\/p>\n\n\n\n

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Multimodal AI for Radiology Applications Project MAIRA is a research project from Microsoft Health Futures that builds innovative, multimodal AI technology to assist radiologists in delivering effective patient care and to empower them in their work. The goal of the project is to leverage rich healthcare data \u2013 including medical domain knowledge, temporal sequences of […]<\/p>\n","protected":false},"featured_media":984078,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","footnotes":""},"research-area":[13556,13553],"msr-locale":[268875],"msr-impact-theme":[261673],"msr-pillar":[],"class_list":["post-978063","msr-project","type-msr-project","status-publish","has-post-thumbnail","hentry","msr-research-area-artificial-intelligence","msr-research-area-medical-health-genomics","msr-locale-en_us","msr-archive-status-active"],"msr_project_start":"","related-publications":[858441,917718,923706,952185,981816,986898,1009149,1012407,1047813,1047834,1051485,1105596,1143010,1157538,1157568],"related-downloads":[],"related-videos":[],"related-groups":[780706,1143270],"related-events":[],"related-opportunities":[],"related-posts":[946080,985488,994098,1048935,1088394,1101105,1142540,1146680],"related-articles":[],"tab-content":[],"slides":[],"related-researchers":[{"type":"user_nicename","display_name":"Shruthi Bannur","user_id":39213,"people_section":"Section name 0","alias":"shbannur"},{"type":"user_nicename","display_name":"Kenza Bouzid","user_id":43290,"people_section":"Section name 0","alias":"kenzabouzid"},{"type":"user_nicename","display_name":"Daniel Coelho de Castro","user_id":39811,"people_section":"Section name 0","alias":"dacoelh"},{"type":"user_nicename","display_name":"Stephanie Hyland","user_id":38458,"people_section":"Section name 0","alias":"sthyland"},{"type":"user_nicename","display_name":"Max Ilse","user_id":41095,"people_section":"Section name 0","alias":"maxilse"},{"type":"user_nicename","display_name":"Fernando Pérez-García","user_id":41473,"people_section":"Section name 0","alias":"fperezgarcia"},{"type":"user_nicename","display_name":"Anton Schwaighofer","user_id":31059,"people_section":"Section name 0","alias":"antonsc"},{"type":"user_nicename","display_name":"Harshita Sharma","user_id":41602,"people_section":"Section name 0","alias":"harssharma"},{"type":"user_nicename","display_name":"Sam Bond-Taylor","user_id":43452,"people_section":"Section name 0","alias":"sbondtaylor"},{"type":"user_nicename","display_name":"Hannah Richardson (nee Murfet)","user_id":37703,"people_section":"Section name 0","alias":"hamurfet"},{"type":"user_nicename","display_name":"Kenji Takeda","user_id":32522,"people_section":"Section name 0","alias":"kenjitak"},{"type":"user_nicename","display_name":"Tristan Lazard","user_id":43910,"people_section":"Section name 0","alias":"t-tlazard"}],"msr_research_lab":[849856],"msr_impact_theme":["Health"],"_links":{"self":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/978063","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project"}],"about":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/types\/msr-project"}],"version-history":[{"count":39,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/978063\/revisions"}],"predecessor-version":[{"id":1161295,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-project\/978063\/revisions\/1161295"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media\/984078"}],"wp:attachment":[{"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/media?parent=978063"}],"wp:term":[{"taxonomy":"msr-research-area","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/research-area?post=978063"},{"taxonomy":"msr-locale","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-locale?post=978063"},{"taxonomy":"msr-impact-theme","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-impact-theme?post=978063"},{"taxonomy":"msr-pillar","embeddable":true,"href":"https:\/\/www.microsoft.com\/en-us\/research\/wp-json\/wp\/v2\/msr-pillar?post=978063"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}