Evidence

RSNA: Podium Presentation
McCabe et al. 2025

Interpretive performance in screening mammograms (SMGs) varies widely. An AI-driven workflow was deployed at scale, and performance before and after implementation was compared between radiologists with and without fellowship training in breast or women’s imaging. The Multistage AI-Driven workflow significantly improved CDR and PPV1 for General radiologists to levels comparable with Fellowship-trained radiologists.

RSNA: Poster Presentation
Louis et al. 2025

This study assesses the cancer subtypes detected in a large-scale deployment of a multistage AI-driven workflow compared to the standard of care (SOC). Employing the multistage AI-driven workflow significantly improved clinical outcomes in terms of CDR in a cohort of over 2400 cancers, with the majority of cancers detected being clinically relevant and no corresponding increase in the proportion of DCIS, showing benefits for screening mammography.

Nature Health
Louis et al. Nov 2025

Artificial intelligence shows promising results for improving early breast cancer detection and overall screening outcomes. Here the AI-Supported Safeguard Review Evaluation (ASSURE) study evaluates an AI workflow on digital breast tomosynthesis exams from women across four states to optimize early cancer detection. Implementation of the AI workflow improved screening effectiveness with equitable benefits.

DeepHealth
Kaminer. Nov 2024

The workflows of two centers were analyzed before and after the installation of DeepHealth’s prostate solution software. Time was reduced in the scan evaluation process (radiologists’ read-time) by 14.1% over two centers with 8 out of 9 radiologists decreasing their evaluation time. With the software’s automatic targeting for fusion biopsies, time was also reduced in the overall workflow for biopsy-recommended cases by 232 hours (6 weeks), or 37%.

BTOG: Poster Presentation
Palmer et al. 2025

Clinical outcomes from the SWAG TLHC programme were reviewed to better understand potential harms, including over-investigation, missed diagnoses and cancers developing between screening rounds. Of the scans reviewed, 4098/4198 (97.6%) were negative (confirmed true negatives) and 80/4198 (1.9%) were abnormal (confirmed true positives). One person was diagnosed with lung cancer 18 months after a reportedly normal TLC scan, giving a false negative rate of 0.02%.

RSNA: Poster Presentation
Kim et al. 2024

An AI-driven safeguard review process was implemented prospectively, and its custom-built AI algorithm was used to flag the most suspicious screening DBT exams that had not been recalled by the initial interpreting radiologist. An expert breast imaging specialist performed a second, safeguard review of the 2,296 flagged exams. This resulted in the detection of 41 additional cancers, mostly invasive, 22.0% of which were deemed aggressive.

Ipsos
Mouland et al. Nov 2024

The TLHC programme (2019–2024) tested lung health checks in real-world settings to diagnose lung cancers earlier, following promising pilot results. Across more than 25 sites, 1.22 million invitations were issued, with 44% uptake. Of 163,000 CT scans, 2,748 cancers were diagnosed (1.7% conversion), around three-quarters at stage 1–2. Impact analysis showed an additional 781 early-stage cancers diagnosed in pilot areas, replicating small-scale outcomes and demonstrating real-world effectiveness while identifying demographic and operational challenges.

European Journal of Radiology
Masci et al. Oct 2024

The performance of artificial intelligence (AI) in the preoperative detection of lung metastases on CT was evaluated. Patients who underwent lung metastasectomy were enrolled. Their preoperative CT scans were retrospectively processed by AI. The results indicated that AI significantly increases the sensitivity of preoperative detection of lung metastases and enables earlier detection, with a significant potential benefit for patient management.

European Urology Focus
Guenzel et al. Sep 2024

Accurate magnetic resonance imaging (MRI) reporting is essential for transperineal prostate biopsy (TPB) planning. This study documented the diagnostic utility of using PI-RADS and CAD for biopsy planning compared with PI-RADS alone. A total of 262 consecutive men scheduled for TPB were analyzed. The tested CAD tool for TPB planning improved csPCa detection at the cost of an increased number of lesions sampled and false positives. This may enable more personalized biopsy planning depending on urological and patient preferences.

Radiology Artificial Intelligence
Kim et al. Feb 2024

The performance of 18 general radiologists and breast imaging specialists (9 generalists, 9 specialists) was evaluated with and without the aid of a custom-built categorical AI system. The categorical AI system helped improve overall radiologist interpretation performance of DBT screening mammograms for both general radiologists and breast imaging specialists and across various patient subgroups and breast cancer characteristics.