Factors influencing AI acceptance in radiology: a systematic review across the radiology workflow.

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Abstract

Despite AI's promise for radiology, clinical implementation remains limited. AI acceptance is a key factor in bridging the gap between technical validation and adoption. This systematic review identifies factors influencing the acceptance and use of clinical AI tools across the radiology workflow. A systematic search of Ovid Medline, Embase, and Web of Science Core was conducted using ASReview, a machine learning tool that prioritises relevant records for selection. Inclusion criteria targeted diverse stakeholders and studies that evaluate the acceptance of clinical AI models, rather than AI as a general concept. Extracted study characteristics included the AI model's intended purpose, subspecialty of radiology, imaging modality, stakeholder group, and technology readiness level. A narrative synthesis was performed according to the stages of the radiology workflow. Thirty-seven studies were included. Most studies investigated AI applications for image interpretation, with radiologists and patients as primary stakeholder groups. Twenty-two acceptance factors were identified and grouped into 6 overarching themes. Indicating that clinical accuracy is conditional for acceptance, however, challenges remain in AI literacy, addressing regulation and ethical concerns, limited user guidance, lacking transparency and unrepresentative training data. Resistance was strongest against fully automated tasks central to radiologists' core competencies, while supportive and lower-risk applications were more readily accepted. This review highlights two major evidence gaps: the underrepresentation of non-clinical stakeholders and limited studies outside image interpretation. Case-specific evaluations involving diverse stakeholders are needed to support the responsible implementation of AI in radiology. Question How does a broad range of stakeholders perceive AI acceptance along the radiology workflow? Findings AI acceptance is multidimensional, while clinical accuracy is essential, it alone is insufficient to increase implementation. Strongest resistance occurs toward automated image interpretation tasks. Clinical relevance To enable effective implementation of AI in radiology, algorithmic transparency, data representation, AI literacy, and regulation are essential. Engagement of diverse stakeholders is essential to advance from AI acceptance toward meaningful clinical integration.

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