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AI Methods for Implementation Science (AIM-IS): developing a framework, toolkit, and reporting standard for the

Guillaume Fontaine1,2,3,4, Susan Michie5, Rinad S Beidas6

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Summary

Artificial intelligence (AI) offers potential for implementation science, but lacks clear guidance. The AI Methods for Implementation Science (AIM-IS) program will create resources for responsible AI use in practice and research.

Keywords:
Artificial intelligenceGenerative AIImplementation practiceImplementation researchLarge language modelsMachine learningMethodologyReporting guideline

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Area of Science:

  • Implementation Science
  • Artificial Intelligence (AI)
  • Responsible AI

Background:

  • AI, including machine learning and large language models, has potential applications in implementation practice and research, such as evidence synthesis and strategy selection.
  • Current AI applications in implementation science are diverse, ranging from practice support to research methods, but lack standardized classification, evaluation, and reporting guidelines.
  • The AI Methods for Implementation Science (AIM-IS) program is designed to address this gap by developing products for responsible AI use.

Purpose of the Study:

  • To develop, validate, and maintain a suite of products that guide the responsible use of AI across implementation practice, research, and bridging use cases.
  • To establish clear guidelines for classifying, evaluating, and reporting AI applications within implementation science.

Main Methods:

  • A multi-phase, multi-method program focusing on AI-for-implementation use cases.
  • Phase 1: Living scoping review of published AI use cases, their evaluation, and associated risks.
  • Phase 2: Qualitative interviews with diverse stakeholders to refine use cases and identify priorities.
  • Phases 3-5: Development, refinement, and usability testing of AIM-IS products (framework, taxonomy, guardrails, guide, reporting items) using eDelphi and consensus methods.

Main Results:

  • The AIM-IS program will yield a comprehensive suite of products, including a framework, toolkit, and reporting standards.
  • These products will facilitate the specification, governance, evaluation, and reporting of AI in implementation science.

Conclusions:

  • The AIM-IS program's deliverables will enhance transparency, comparability, and accountability in the application of AI within implementation science.
  • These resources aim to promote equitable and responsible AI use by implementation practitioners and researchers.
  • The program incorporates a living-update approach to ensure ongoing relevance and refinement of AI guidance.