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

• URL: http://arxiv.org/abs/2510.13006v2
• Date: Mon, 20 Oct 2025 22:11:05 GMT
• Title: What is Implementation Science; and Why It Matters for Bridging the Artificial Intelligence Innovation-to-Application Gap in Medical Imaging
• Authors: Ahmad Fayaz-Bakhsh, Janice Tania, Syaheerah Lebai Lutfi, Abhinav K. Jha, Arman Rahmim,
• Abstract summary: Implementation science (IS) may provide a framework to bridge the gap between AI development and real-world clinical imaging use.<n>We outline challenges specific to AI adoption in medical Imaging (MI)<n>We highlight the complementary roles of effectiveness research and implementation research, emphasizing hybrid study designs.
• Score: 0.8969078296493108
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• MedSAM-Agent: Empowering Interactive Medical Image Segmentation with Multi-turn Agentic Reinforcement Learning [53.37068897861388]
  MedSAM-Agent is a framework that reformulates interactive segmentation as a multi-step autonomous decision-making process.<n>We develop a two-stage training pipeline that integrates multi-turn, end-to-end outcome verification.<n>Experiments across 6 medical modalities and 21 datasets demonstrate that MedSAM-Agent achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2026-02-03T09:47:49Z)
• Towards AI-Supported Research: a Vision of the TIB AIssistant [6.36260975777314]
  We present the vision of the TIB AIssistant, a domain-agnostic human-machine collaborative platform designed to support researchers across disciplines in scientific discovery.<n>We describe the conceptual framework, system architecture, and implementation of an early prototype that demonstrates the feasibility and potential impact of our approach.
  arXiv  Detail & Related papers  (2025-12-18T12:08:46Z)
• An AI Implementation Science Study to Improve Trustworthy Data in a Large Healthcare System [1.6881002551798014]
  This study presents an AI implementation case study within Shriners Childrens (SCs), a large multisite pediatric system.<n>We introduce a Python-based data quality assessment tool compatible with SCs infrastructure, extending OHDsi's R/Java-based Data Quality Dashboard.<n>We also compare systematic and case-specific AI implementation strategies for Craniofacial Microsomia (CFM) using the FHIR standard.
  arXiv  Detail & Related papers  (2025-12-01T14:21:16Z)
• Generative Artificial Intelligence in Medical Imaging: Foundations, Progress, and Clinical Translation [14.306027161664565]
  Generative artificial intelligence (AI) is rapidly transforming medical imaging.<n>Generative AI contributes to key stages of the imaging continuum from acquisition and reconstruction to cross-modality synthesis.<n>This review aims to guide future research and foster interdisciplinary collaboration at the intersection of AI, medicine, and biomedical engineering.
  arXiv  Detail & Related papers  (2025-08-07T07:58:40Z)
• Medical Reasoning in the Era of LLMs: A Systematic Review of Enhancement Techniques and Applications [59.721265428780946]
  Large Language Models (LLMs) in medicine have enabled impressive capabilities, yet a critical gap remains in their ability to perform systematic, transparent, and verifiable reasoning.<n>This paper provides the first systematic review of this emerging field.<n>We propose a taxonomy of reasoning enhancement techniques, categorized into training-time strategies and test-time mechanisms.
  arXiv  Detail & Related papers  (2025-08-01T14:41:31Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• From Screens to Scenes: A Survey of Embodied AI in Healthcare [31.183244202702983]
  "EmAI in healthcare" spans diverse fields such as algorithms, robotics, and biomedicine.<n>We provide a comprehensive overview of the "brain" of EmAI for healthcare, wherein we introduce AI algorithms for perception, actuation, planning, and memory.<n>We discuss the technical barriers and explore ethical considerations, offering a forward-looking perspective on the future of EmAI in healthcare.
  arXiv  Detail & Related papers  (2025-01-13T16:35:52Z)
• Transforming the Hybrid Cloud for Emerging AI Workloads [82.21522417363666]
  This white paper envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads.<n>The proposed framework addresses critical challenges in energy efficiency, performance, and cost-effectiveness.<n>This joint initiative aims to establish hybrid clouds as secure, efficient, and sustainable platforms.
  arXiv  Detail & Related papers  (2024-11-20T11:57:43Z)
• Clinical Evaluation of Medical Image Synthesis: A Case Study in Wireless Capsule Endoscopy [63.39037092484374]
  Synthetic Data Generation based on Artificial Intelligence (AI) can transform the way clinical medicine is delivered.<n>This study focuses on the clinical evaluation of medical SDG, with a proof-of-concept investigation on diagnosing Inflammatory Bowel Disease (IBD) using Wireless Capsule Endoscopy (WCE) images.<n>The results show that TIDE-II generates clinically plausible, very realistic WCE images, of improved quality compared to relevant state-of-the-art generative models.
  arXiv  Detail & Related papers  (2024-10-31T19:48:50Z)
• Artificial intelligence techniques in inherited retinal diseases: A review [19.107474958408847]
  Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults. Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges. This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs.
  arXiv  Detail & Related papers  (2024-10-10T03:14:51Z)
• Integration and Implementation Strategies for AI Algorithm Deployment with Smart Routing Rules and Workflow Management [0.37918614538294315]
  This paper reviews the challenges hindering the widespread adoption of artificial intelligence (AI) solutions in the healthcare industry. The absence of standardized frameworks for AI development pose significant barriers and require a new paradigm to address them. The role of interoperability is examined in this paper as a crucial factor in connecting disparate applications within healthcare.
  arXiv  Detail & Related papers  (2023-11-17T19:38:37Z)
• An interdisciplinary conceptual study of Artificial Intelligence (AI) for helping benefit-risk assessment practices: Towards a comprehensive qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
  This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence. The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
  arXiv  Detail & Related papers  (2021-05-07T12:01:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.