Background: Artificial intelligence (AI) studies show promise in enhancing accuracy and efficiency in mammographic screening programs worldwide. However, its integration into clinical workflows faces several challenges, including unintended errors, the need for professional training, and ethical concerns. Notably, specific frameworks for AI imaging in breast cancer screening are still lacking. Objective: This study aims to identify the challenges associated with implementing AI in breast screening programs and to apply the Consolidated Framework for Implementation Research (CFIR) to discuss a practical governance framework for AI in this context. Methods: Three electronic databases (PubMed, Embase, and MEDLINE) were searched using combinations of the keywords “artificial intelligence,” “regulation,” “governance,” “breast cancer,” and “screening.” Original studies evaluating AI in breast cancer detection or discussing challenges related to AI implementation in this setting were eligible for review. Findings were narratively synthesized and subsequently mapped directly onto the constructs within the CFIR. Results: A total of 1240 results were retrieved, with 20 original studies ultimately included in this systematic review. The majority (n=19) focused on AI-enhanced mammography, while 1 addressed AI-enhanced ultrasound for women with dense breasts. Most studies originated from the United States (n=5) and the United Kingdom (n=4), with publication years ranging from 2019 to 2023. The quality of papers was rated as moderate to high. The key challenges identified were reproducibility, evidentiary standards, technological concerns, trust issues, as well as ethical, legal, societal concerns, and postadoption uncertainty. By aligning these findings with the CFIR constructs, action plans targeting the main challenges were incorporated into the framework, facilitating a structured approach to addressing these issues. Conclusions: This systematic review identifies key challenges in implementing AI in breast cancer screening, emphasizing the need for consistency, robust evidentiary standards, technological advancements, user trust, ethical frameworks, legal safeguards, and societal benefits. These findings can serve as a blueprint for policy makers, clinicians, and AI developers to collaboratively advance AI adoption in breast cancer screening. Trial Registration: PROSPERO CRD42024553889; https://tinyurl.com/mu4nwcxt

Background: In recent years, Artificial Intelligence (AI) has shown transformative potential in advancing breast cancer care globally. This scoping review seeks to provide a comprehensive overview of AI applications in breast cancer care, examining how they could reshape diagnosis, treatment, and management on a worldwide scale and discussing both the benefits and challenges associated with their adoption. Methods: In accordance with PRISMA-ScR and ensuing guidelines on scoping reviews, PubMed, Web of Science, Cochrane Library, and Embase were systematically searched from inception to end of May 2024. Keywords included “Artificial Intelligence” and “Breast Cancer”. Original studies were included based on their focus on AI applications in breast cancer care and narrative synthesis was employed for data extraction and interpretation, with the findings organized into coherent themes. Results: Finally, 84 articles were included. The majority were conducted in developed countries (n = 54). The majority of publications were in the last 10 years (n = 83). The six main themes for AI applications were AI for breast cancer screening (n = 32), AI for image detection of nodal status (n = 7), AI-assisted histopathology (n = 8), AI in assessing post-neoadjuvant chemotherapy (NACT) response (n = 23), AI in breast cancer margin assessment (n = 5), and AI as a clinical decision support tool (n = 9). AI has been used as clinical decision support tools to augment treatment decisions for breast cancer and in multidisciplinary tumor board settings. Overall, AI applications demonstrated improved accuracy and efficiency; however, most articles did not report patient-centric clinical outcomes. Conclusions: AI applications in breast cancer care show promise in enhancing diagnostic accuracy and treatment planning. However, persistent challenges in AI adoption, such as data quality, algorithm transparency, and resource disparities, must be addressed to advance the field.

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Background/Objectives: Artificial intelligence (AI) is increasingly applied in breast imaging, with potential to improve diagnostic accuracy and reduce workload in mammographic breast cancer screening. However, real-world integration of AI into national screening programs remains limited, and little is known about radiologists’ perspectives in Asian settings. This study aimed to explore radiologists’ perceptions of AI adoption in Singapore’s breast screening program, focusing on perceived benefits, barriers, and requirements for safe integration. Methods: We conducted a mixed methods study involving a cross-sectional survey of 17 radiologists with prior experience using AI-assisted mammography, followed by semi-structured interviews with 10 radiologists across all three public healthcare clusters. The survey measured confidence in AI, attitudes toward its diagnostic role, and integration preferences. Interviews were analyzed thematically, guided by the Unified Theory of Acceptance and Use of Technology (UTAUT) framework. Results: Among survey respondents, 64.7% recommended AI as a companion reader, though only 29.4% rated its performance as comparable to humans. Confidence was highest when AI was validated on local datasets (mean 9.3/10). Interviews highlighted AI’s strengths in routine, fatigue-prone tasks, but skepticism for complex cases. Concerns included false positives, workflow inefficiencies, medico-legal accountability, and long-term costs. Radiologists emphasized the importance of national guidelines, local validation, and clear role definition to build trust. Conclusions: Radiologists support AI as an adjunct to, but not a replacement for, human readers in breast cancer screening. Adoption will require robust regulatory frameworks, seamless workflow integration, transparent validation on local data, and structured user training to ensure safe and effective implementation.