# What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging

arXiv: https://arxiv.org/abs/2510.13006 · HTML: https://mlsanity.com/paper/2510.13006/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 63 references that are in the map.
- First posted on arXiv 14 Oct 2025.

## Builds on (most cited first, 4 of 4)

- [Explainable Medical Imaging AI Needs Human-Centered Design: Guidelines and Evidence from a Systematic Review](https://mlsanity.com/paper/2112.12596/) (2021)
- [Objective task-based evaluation of artificial intelligence-based medical imaging methods: Framework, strategies and role of the physician](https://mlsanity.com/paper/2107.04540/) (2021)
- [A brief history of AI: how to prevent another winter (a critical review)](https://mlsanity.com/paper/2109.01517/) (2021)
- [Current State of Community-Driven Radiological AI Deployment in Medical Imaging](https://mlsanity.com/paper/2212.14177/) (2022)

## Built on by (most cited first, 1 of 1)

- [Rethinking Artificial Intelligence in Medical Imaging: Assumptions, Reality, and Reframing](https://mlsanity.com/paper/2607.27428/) (2026)

## Abstract (arXiv)

The transformative potential of artificial intelligence (AI) in medical Imaging (MI) is well recognized. Yet despite promising reports in research settings, many AI tools fail to achieve clinical adoption in practice. In fact, more generally, there is a documented 17-year average delay between evidence generation and implementation of a technology. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use, to shorten this lag through systematic frameworks, strategies, and hybrid research designs. We outline challenges specific to AI adoption in MI workflows, including infrastructural, educational, and cultural barriers. We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs and the role of integrated KT (iKT), stakeholder engagement, and equity-focused co-creation in designing sustainable and generalizable solutions. We discuss integration of Human-Computer Interaction (HCI) frameworks in MI towards usable AI. Adopting IS is not only a methodological advancement; it is a strategic imperative for accelerating translation of innovation into improved patient outcomes.