{"paper":{"title":"Recent Advances, Applications and Open Challenges in Machine Learning for Health: Reflections from Research Roundtables at ML4H 2024 Symposium","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY"],"primary_cat":"cs.LG","authors_text":"Adibvafa Fallahpour, Alina Peluso, Amin Adibi, Ayush Noori, Brighton Nuwagira, Dennis Shung, Elizabeth Healey, Geoffrey Woollard, Hanna A. Frank, Hejie Cui, James M. Rehg, Jason Alan Fries, Jessica Dafflon, Jivat Neet Kaur, Johnny Xi, John Wu, Leo Anthony Celi, Maxwell A Xu, Md. Belal Hossain, Megan Coffee, Michael Craig, Mohammad Ehsanul Karim, Mohsen Sadatsafavi, Nicole Zhang, Rahmatollah Beheshti, Rahul G. Krishnan, Ross Duncan, Sarah Jabbour, Sazan Mahbub, Shannon McWeeney, Shannon Zejiang Shen, Trenton Chang, Vasiliki Bikia, Wenqian Ye, Winston Chen, Xu Cao, Yuan Pu, Yuan Xia, Yurui Cao, Yuwei Zhang, Zongliang Ji, Zuheng Xu","submitted_at":"2025-02-10T17:17:09Z","abstract_excerpt":"The fourth Machine Learning for Health (ML4H) symposium was held in person on December 15th and 16th, 2024, in the traditional, ancestral, and unceded territories of the Musqueam, Squamish, and Tsleil-Waututh Nations in Vancouver, British Columbia, Canada. The symposium included research roundtable sessions to foster discussions between participants and senior researchers on timely and relevant topics for the ML4H community. The organization of the research roundtables at the conference involved 13 senior and 27 junior chairs across 13 tables. Each roundtable session included an invited senior"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.06693","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2502.06693/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}