Implementation Science Driven Identification of Barriers to AI Diagnostic Decision Support: A Stakeholder-Informed Concept Mapping Study
Abstract
The clinical translation of artificial intelligence (AI) is a critical step toward precision medicine. The implementation study presented here focuses specifically on barriers to AI adoption in the diagnostic medicine workflow. Successful clinical adoption of AI requires more than technical performance; it depends on addressing real-world organizational, ethical, infrastructural, and workflow-related barriers that may prevent promising systems from moving beyond research settings. To examine these barriers, we used structured concept mapping (CM) to operationalize stakeholder engagement and to integrate a knowledge translation (iKT) approach within the Knowledge-to-Action (KTA) framework. Stakeholders co-generated 96 statements describing barriers to AI implementation and organized them into 11 clusters, including infrastructure limitations, ethical and legal concerns, workflow integration challenges, and distrust in algorithmic reasoning. These findings provide actionable knowledge for supporting AI implementation in the diagnostic chain. We additionally position digital twins (DT) as a related, forward-looking context, as they are increasingly discussed as future extensions of AI-enabled care. As such, our findings offer preliminary insights for future DT deployment, as many DT systems are expected to rely on AI-driven prediction, multimodal data integration, and iterative model refinement. Addressing the identified barriers early may help AI-based clinical tools, and eventually DT-enabled applications, progress from prototypes toward evidence-based standards of care.
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BibTeX
@misc{jouzdani2026implementation,
title = {{Implementation Science Driven Identification of Barriers to AI Diagnostic Decision Support: A Stakeholder-Informed Concept Mapping Study}},
author = {Ali Fathi Jouzdani and Arman Gorji and Mohammad Zahedi and Morteza Alizadeh and Babak Saboury and Arman Rahmim and Mohammad R. Salmanpour and Ahmad Fayaz-Bakhsh},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.02554},
eprint = {2611.02554},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.02554},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - Implementation Science Driven Identification of Barriers to AI Diagnostic Decision Support: A Stakeholder-Informed Concept Mapping Study
AU - Ali Fathi Jouzdani
AU - Arman Gorji
AU - Mohammad Zahedi
AU - Morteza Alizadeh
AU - Babak Saboury
AU - Arman Rahmim
AU - Mohammad R. Salmanpour
AU - Ahmad Fayaz-Bakhsh
PY - 2026
DA - 2026/11/02
PB - arXiv
DO - 10.48550/arxiv.2611.02554
UR - https://arxiv.org/abs/2611.02554
AB - The clinical translation of artificial intelligence (AI) is a critical step toward precision medicine. The implementation study presented here focuses specifically on barriers to AI adoption in the diagnostic medicine workflow. Successful clinical adoption of AI requires more than technical performance; it depends on addressing real-world organizational, ethical, infrastructural, and workflow-related barriers that may prevent promising systems from moving beyond research settings. To examine these barriers, we used structured concept mapping (CM) to operationalize stakeholder engagement and to integrate a knowledge translation (iKT) approach within the Knowledge-to-Action (KTA) framework. Stakeholders co-generated 96 statements describing barriers to AI implementation and organized them into 11 clusters, including infrastructure limitations, ethical and legal concerns, workflow integration challenges, and distrust in algorithmic reasoning. These findings provide actionable knowledge for supporting AI implementation in the diagnostic chain. We additionally position digital twins (DT) as a related, forward-looking context, as they are increasingly discussed as future extensions of AI-enabled care. As such, our findings offer preliminary insights for future DT deployment, as many DT systems are expected to rely on AI-driven prediction, multimodal data integration, and iterative model refinement. Addressing the identified barriers early may help AI-based clinical tools, and eventually DT-enabled applications, progress from prototypes toward evidence-based standards of care.
ER -