White paper: Advancing Breast Imaging: How Smart Reporting and AI Empower Radiologists

Introduction

Breast imaging is at the heart of early cancer detection, risk assessment, treatment planning, and follow-up care. With large scale screening programs now in place, radiologists face increasing pressure: more cases to read, more complexity to manage, and higher expectations for accuracy and consistency.

At the same time, reporting must be standardised, guideline-compliant, and increasingly transparent, both for clinicians and for patients who may read their own reports.

This white paper looks at how Smart Reporting supports breast imaging practices: helping radiologists keep up with high workloads, ensuring BI-RADS compliance, integrating with registries, and enabling the seamless use of AI and multimodal documentation.

Key challenges in breast imaging

High volume, tight timelines: breast screening programs generate massive imaging volumes that require efficient, standardised interpretation and reporting.
Protocol compliance and legal risk: reports must strictly adhere to protocols like BI-RADS, with zero tolerance for inconsistency or omission due to medico-legal implications.
Multimodal reporting: breast imaging typically involves mammography, ultrasound, MRI, and increasingly tomosynthesis. Synchronising insights across these modalities is critical.
Data and registry demands: national and regional breast cancer registries require structured, accurate data extraction from reports to monitor quality metrics and outcomes.
Integrating AI safely: radiologists must now interpret not only native imaging but also AI-generated scores or alerts, requiring seamless integration into the reporting interface. AI systems have demonstrated efficacy in detecting breast cancer, with detection rates of 81.4% and substantial concordance with experienced radiologists. AI systems can also identify mammographically occult cases, highlighting their potential in enhancing diagnostic accuracy [4]. In addition, AI systems can aid radiologists in classifying screening mammograms more accurately without increasing interpretation time. The use of AI improved the average area under the receiver operating characteristic curve (AUC) from 0.74 to 0.77, indicating enhanced diagnostic performance [1]. AI has also been shown to improve diagnostic performance in breast MRI, helping radiologists differentiate benign from malignant lesions [2].
Clear communication with patients: reports increasingly serve patients directly, particularly in screening contexts, necessitating clarity and transparency.

Smart Reporting’s adaptive breast imaging workflow

Smart Reporting is designed with breast radiologists in mind. It supports different scenarios with tailored workflows:

Screening workflow: streamlined for high-volume settings, guiding radiologists through BI-RADS classification with automated recommendations and built-in consistency checks.
Diagnostic workflow: supports detailed, multimodal reporting for recalled patients or symptomatic exams. Mammography, ultrasound, and MRI can all be documented in a single structured report.
Follow-up and surveillance workflow: ensures consistency across longitudinal studies by highlighting changes over time and pulling in prior comparisons.

Key capabilities

Standardised BI-RADS reporting: fully integrated BI-RADS templates for all modalities ensure guideline compliance, including support for calcification morphology, mass descriptors, asymmetries, and associated findings.
AI score embedding: Smart Reporting can ingest and present AI-generated outputs, such as risk scores, density measurements, and lesion detection flags, directly within the structured report, ensuring transparency and traceability, while reducing the dictation time.
Multimedia-enhanced reporting: radiologists can embed annotated images, sketches, and diagrams into reports to clarify complex findings or biopsy locations, improving clinician communication.
Multimodal documentation: allows simultaneous structured documentation across imaging types (eg, mammogram + ultrasound), enabling comprehensive, single-report outputs for holistic assessment.
Registry integration and data export: seamless export of structured data into national breast cancer registries supports audit, research, and quality benchmarking. Flexible APIs ensure compatibility with local or international systems [3].
Real-time recommendation support: the system tracks required BI-RADS components in real time, flagging missing elements and automatically suggesting follow-up intervals or procedures based on findings.
SmartAssist SummarAIze for breast imaging: auto-generates impression summaries aligned with BI-RADS categories, highlighting malignancy risk and next steps.
Interdisciplinary communication: integrated breast conference templates allow rapid preparation for tumor boards, including image references, histology integration, and treatment history.
Efficiency gains without compromise: structured reporting goes beyond unifying terminology, it streamlines workflows while elevating the quality of clinical documentation. Evidence from other imaging domains illustrates how these benefits extend to breast imaging: in head and neck ultrasound, structured reporting improved report completeness from 26.4% to 95.6%, while simultaneously reducing reporting time and enhancing user satisfaction [5]. Likewise, in DXA, Smart Reporting’s solution cut reporting time by more than half (from 6.1 minutes to 2.7 minutes) and improved quality from 79% to 96% [6]. These results highlight how Smart Reporting consistently enables faster, higher-quality documentation, essential in breast imaging, where radiologists must manage high volumes without compromising diagnostic precision.

Conclusion

Breast imaging demands a reporting infrastructure that is accurate, efficient, interoperable, and ready to embrace multimodal and AI-assisted practices. Smart Reporting delivers an adaptive, intelligent platform tailored to the distinct needs of breast imaging, supporting everything from screening workflows to complex diagnostic assessments, AI integration, and longitudinal care coordination.

By drawing on proven evidence that the reporting solution improves both efficiency and quality, such as doubling report completeness in ultrasound and halving reporting times in DXA, Smart Reporting ensures breast radiologists can keep pace with rising workloads without compromising accuracy or clarity. The result is higher-quality care, delivered more efficiently, with optimized data utility across the healthcare ecosystem.

References

[1] https://pubmed.ncbi.nlm.nih.gov/35763243/

[2] https://pubmed.ncbi.nlm.nih.gov/33078996/

[3] https://pubmed.ncbi.nlm.nih.gov/35371885/

[4] https://pubmed.ncbi.nlm.nih.gov/40502479/

[5] https://pubmed.ncbi.nlm.nih.gov/31612337/

[6] https://pubmed.ncbi.nlm.nih.gov/32299400/