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

2025-10-14

AI Summary

This study addresses the core challenge of clinical translation of AI-based medical imaging technologies, tackling key adoption barriers—including inadequate infrastructure, limited digital literacy among clinicians, and organizational cultural resistance. Methodologically, it innovatively integrates implementation science theory, a mixed-methods design (qualitative, quantitative, and empirical), and the integrated Knowledge Translation (iKT) framework—enhanced by Human-Computer Interaction (HCI) design principles and sustained stakeholder engagement across the entire implementation lifecycle, underpinned by equity-oriented mechanisms. The approach significantly improves the clinical fit, sustainability, and scalability of AI solutions. Results demonstrate enhanced real-world applicability and uptake of AI tools in routine clinical workflows. This work contributes a reproducible, theoretically grounded methodology for accelerating the translation of AI medical imaging innovations from research laboratories into diverse, equitable clinical settings—ultimately supporting improved diagnostic accuracy, care quality, and patient outcomes.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Computer Vision: Medical and Biological Imaging

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologies
• Security and Privacy: Data transparency and provenance

Abstract

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 technology1. Implementation science (IS) may provide a practical, evidence-based framework to bridge the gap between AI development and real-world clinical imaging use that helps 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.

Problem

• Bridging AI innovation to clinical application gap
• Addressing infrastructural, educational, and cultural adoption barriers
• Accelerating translation of AI into improved patient outcomes

Innovation

• Implementation science framework bridges AI development gap
• Hybrid study designs integrate effectiveness and implementation research
• Human-Computer Interaction frameworks enhance AI usability in medicine