Reflection and legacy of AIM RSF

The final AIM RSF online seminar took place on 11th March 2025, where we reflected on our journey with the AIM community, shared key lessons, celebrated our impact, and revisited the case studies and resources that shaped our legacy. This final seminar was an opportunity to look back on three years of the AIM grant, highlighting key insights and guiding participants on how to access and build upon our resources. We discussed our open science approach, cross-collaborative efforts, and commitment to inclusivity—including PPIE, ECR support, and capacity building. We also explored the programme’s legacy, archived resources, and the broader impact we made. This project is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-03-11
DOI
https://doi.org/10.5281/zenodo.15004902
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
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article

Reflection and legacy of AIM RSF

Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
article

Reflection and legacy of AIM RSF

article en

Abstract

The final AIM RSF online seminar took place on 11th March 2025, where we reflected on our journey with the AIM community, shared key lessons, celebrated our impact, and revisited the case studies and resources that shaped our legacy. This final seminar was an opportunity to look back on three years of the AIM grant, highlighting key insights and guiding participants on how to access and build upon our resources. We discussed our open science approach, cross-collaborative efforts, and commitment to inclusivity—including PPIE, ECR support, and capacity building. We also explored the programme’s legacy, archived resources, and the broader impact we made. This project is funded by the NIHR Artificial Intelligence for Multiple Long-Term Conditions (AIM) programme (NIHR202647). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.

Zenodo (CERN European Organization for Nuclear Research)
Partnerships for the goals
Openalex Percentile: Top 58%
Artificial Intelligence in Healthcare and Education
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