{"ID":2853825,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.15217","arxiv_id":"2510.15217","title":"Reflections from Research Roundtables at the Conference on Health, Inference, and Learning (CHIL) 2025","abstract":"The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to catalyze collaborative, small-group dialogue around critical, timely topics at the intersection of machine learning and healthcare. Each roundtable was moderated by a team of senior and junior chairs who fostered open exchange, intellectual curiosity, and inclusive engagement. The sessions emphasized rigorous discussion of key challenges, exploration of emerging opportunities, and collective ideation toward actionable directions in the field. In total, eight roundtables were held by 19 roundtable chairs on topics of \"Explainability, Interpretability, and Transparency,\" \"Uncertainty, Bias, and Fairness,\" \"Causality,\" \"Domain Adaptation,\" \"Foundation Models,\" \"Learning from Small Medical Data,\" \"Multimodal Methods,\" and \"Scalable, Translational Healthcare Solutions.\"","short_abstract":"The 6th Annual Conference on Health, Inference, and Learning (CHIL 2025), hosted by the Association for Health Learning and Inference (AHLI), was held in person on June 25-27, 2025, at the University of California, Berkeley, in Berkeley, California, USA. As part of this year's program, we hosted Research Roundtables to...","url_abs":"https://arxiv.org/abs/2510.15217","url_pdf":"https://arxiv.org/pdf/2510.15217v3","authors":"[\"Emily Alsentzer\",\"Marie-Laure Charpignon\",\"Bill Chen\",\"Niharika D'Souza\",\"Jason Fries\",\"Yixing Jiang\",\"Aparajita Kashyap\",\"Chanwoo Kim\",\"Simon Lee\",\"Aishwarya Mandyam\",\"Ashery Mbilinyi\",\"Nikita Mehandru\",\"Nitish Nagesh\",\"Brighton Nuwagira\",\"Emma Pierson\",\"Arvind Pillai\",\"Akane Sano\",\"Tanveer Syeda-Mahmood\",\"Shashank Yadav\",\"Elias Adhanom\",\"Muhammad Umar Afza\",\"Amelia Archer\",\"Suhana Bedi\",\"Vasiliki Bikia\",\"Trenton Chang\",\"George H. Chen\",\"Winston Chen\",\"Erica Chiang\",\"Edward Choi\",\"Octavia Ciora\",\"Paz Dozie-Nnamah\",\"Shaza Elsharief\",\"Matthew Engelhard\",\"Ali Eshragh\",\"Jean Feng\",\"Josh Fessel\",\"Scott Fleming\",\"Kei Sen Fong\",\"Thomas Frost\",\"Soham Gadgil\",\"Judy Gichoya\",\"Leeor Hershkovich\",\"Sujeong Im\",\"Bhavya Jain\",\"Vincent Jeanselme\",\"Furong Jia\",\"Qixuan Jin\",\"Yuxuan Jin\",\"Daniel Kapash\",\"Geetika Kapoor\",\"Behdokht Kiafar\",\"Matthias Kleiner\",\"Stefan Kraft\",\"Annika Kumar\",\"Daeun Kyung\",\"Zhongyuan Liang\",\"Joanna Lin\",\"Qianchu Liu\",\"Chang Liu\",\"Hongzhou Luan\",\"Chris Lunt\",\"Leopoldo Julían Lechuga López\",\"Matthew B. A. McDermott\",\"Shahriar Noroozizadeh\",\"Connor O'Brien\",\"YongKyung Oh\",\"Mixail Ota\",\"Stephen Pfohl\",\"Meagan Pi\",\"Tanmoy Sarkar Pias\",\"Emma Rocheteau\",\"Avishaan Sethi\",\"Toru Shirakawa\",\"Anita Silver\",\"Neha Simha\",\"Kamile Stankeviciute\",\"Max Sunog\",\"Peter Szolovits\",\"Shengpu Tang\",\"Jialu Tang\",\"Aaron Tierney\",\"John Valdovinos\",\"Byron Wallace\",\"Will Ke Wang\",\"Peter Washington\",\"Jeremy Weiss\",\"Daniel Wolfe\",\"Emily Wong\",\"Hye Sun Yun\",\"Xiaoman Zhang\",\"Xiao Yu Cindy Zhang\",\"Hayoung Jeong\",\"Kaveri A. Thakoor\"]","published":"2025-10-17T00:54:03Z","proceeding":"cs.LG","tasks":"[\"cs.LG\"]","methods":"[\"LoRA\"]","has_code":false}