Lessons learned from AIM RSF

Lessons Learned from AI in Multiple Long-Term Conditions Research Support Facility (AIM RSF) Abstract: Conducting AI-driven research into Multiple Long-Term Conditions (MLTCs) presents significant challenges around data, capabilities and capacity, and inclusion of all the necessary stakeholders, particularly within large-scale, multi-institutional research programmes. This talk will share approaches, resources and lessons learned from the AI for Multiple Long-Term Conditions Research Support Facility (AIM RSF), a UK-wide team of data science experts funded by the NIHR and led by The Alan Turing Institute, Swansea University, and the University of Edinburgh, to support and drive forward research for the AIM programme. Through collaboration with the eight AIM research consortia, AIM RSF has identified systemic barriers that challenge progress in AI and machine learning research. Delays in data access are often misaligned with funding timelines, governance frameworks often lack the flexibility needed to accommodate AI methodologies, and limitations in technical infrastructure pose further constraints. We have tackled the issue of building skills, knowledge and behaviours for health related data science by embedding experts in research teams to support implementation of new methods and best practices, offering bespoke training and building communities to support and mentor early career researchers. We have also taken an intentional and structured approach to coordination and knowledge sharing throughout the programme so that we can surface additional challenges, generate greater collaboration and make research accessible to all relevant stakeholders. This talk will explore how AIM RSF has supported researchers in navigating these challenges and introduce some of the tools developed in response, including the Research Data and Infrastructure Horizon Scan and case studies on data access, capacity building and Patient and Public Involvement and Engagement (PPIE). We will share key lessons learned, provide recommendations for future projects, and highlight resources designed to outlive the duration of the AIM programme. Find out more about AIM RSF on our webpage: https://www.turing.ac.uk/research/research-projects/ai-for-multiple-long-term-conditions-research-support-facility AIM RSF 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.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-05-01
DOI
https://doi.org/10.5281/zenodo.15319186
Primary Topic
MicroRNA in disease regulation
Type
article
Field-Weighted Citation Impact
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article

Lessons learned from AIM RSF

Zenodo (CERN European Organization for Nuclear Research)
MicroRNA in disease regulation
article

Lessons learned from AIM RSF

article en

Abstract

Lessons Learned from AI in Multiple Long-Term Conditions Research Support Facility (AIM RSF) Abstract: Conducting AI-driven research into Multiple Long-Term Conditions (MLTCs) presents significant challenges around data, capabilities and capacity, and inclusion of all the necessary stakeholders, particularly within large-scale, multi-institutional research programmes. This talk will share approaches, resources and lessons learned from the AI for Multiple Long-Term Conditions Research Support Facility (AIM RSF), a UK-wide team of data science experts funded by the NIHR and led by The Alan Turing Institute, Swansea University, and the University of Edinburgh, to support and drive forward research for the AIM programme. Through collaboration with the eight AIM research consortia, AIM RSF has identified systemic barriers that challenge progress in AI and machine learning research. Delays in data access are often misaligned with funding timelines, governance frameworks often lack the flexibility needed to accommodate AI methodologies, and limitations in technical infrastructure pose further constraints. We have tackled the issue of building skills, knowledge and behaviours for health related data science by embedding experts in research teams to support implementation of new methods and best practices, offering bespoke training and building communities to support and mentor early career researchers. We have also taken an intentional and structured approach to coordination and knowledge sharing throughout the programme so that we can surface additional challenges, generate greater collaboration and make research accessible to all relevant stakeholders. This talk will explore how AIM RSF has supported researchers in navigating these challenges and introduce some of the tools developed in response, including the Research Data and Infrastructure Horizon Scan and case studies on data access, capacity building and Patient and Public Involvement and Engagement (PPIE). We will share key lessons learned, provide recommendations for future projects, and highlight resources designed to outlive the duration of the AIM programme. Find out more about AIM RSF on our webpage: https://www.turing.ac.uk/research/research-projects/ai-for-multiple-long-term-conditions-research-support-facility AIM RSF 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)
The Alan Turing Institute (GB)
Industry, innovation and infrastructure
Openalex Percentile: Top 45%
MicroRNA in disease regulation
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