The three workshops represented in these joint proceedings were held on Friday, July 10, 2026, at the University of Ottawa in Ottawa, Canada, and were co-located with the 24th International Conference on Artificial Intelligence in Medicine (AIME 2026). AIME 2026 took place from July 7 to July 10, 2026. The Doctoral Consortium and tutorials were held on July 7, the main conference took place on July 8 and 9, and the workshops concluded the conference program on July 10. The present joint proceedings bring together seven short papers from three AIME 2026 workshops. Although the workshops address different methodological perspectives, they share a common interest in developing trustworthy, evidence-based, and practically deployable artificial intelligence methods for medicine and public health.
(2026). Preface [to AIME 2026 Workshops: 1st International Workshop on Multicentric and Privacy-preserving Learning in Healthcare, Foundation Models for Public Health and Epidemiology: From Promise to Practice, and 1st International Workshop on Knowledge Graphs for Health, AIME-WS 2026] . Retrieved from https://hdl.handle.net/10446/334205
Preface [to AIME 2026 Workshops: 1st International Workshop on Multicentric and Privacy-preserving Learning in Healthcare, Foundation Models for Public Health and Epidemiology: From Promise to Practice, and 1st International Workshop on Knowledge Graphs for Health, AIME-WS 2026]
Pala, Daniele;
2026-01-01
Abstract
The three workshops represented in these joint proceedings were held on Friday, July 10, 2026, at the University of Ottawa in Ottawa, Canada, and were co-located with the 24th International Conference on Artificial Intelligence in Medicine (AIME 2026). AIME 2026 took place from July 7 to July 10, 2026. The Doctoral Consortium and tutorials were held on July 7, the main conference took place on July 8 and 9, and the workshops concluded the conference program on July 10. The present joint proceedings bring together seven short papers from three AIME 2026 workshops. Although the workshops address different methodological perspectives, they share a common interest in developing trustworthy, evidence-based, and practically deployable artificial intelligence methods for medicine and public health.| File | Dimensione del file | Formato | |
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