Imaging Informatics

Radiologists can identify three out of four AI-generated images
By Amerigo Allegretto
Radiologists found it particularly difficult to distinguish between real and AI-generated CT and MR images.
July 17, 2026
Top eight incorrectly classified synthetic images (Most convincing fake images). Images A, B, C, D, E, F, G and H were the most infrequently correctly classified images at 24%, 43%, 59%, 64%, 69%, 71%, 71% and 73% respectively. All images are AI-generated. Images A, B and C are head CTs. Images D and E are chest CTs. Image F is a cardiac CT. Image G is a musculoskeletal MRI. Image H is a head MRI. Images are republished under a Creative Commons license (CC BY 4.0).
Radiology dominates thirty years of FDA AI device approvals
By Will Morton
The total annual authorization volume increased from a mean of 1.8 per year between 1995 and 2014 to 264 per year between 2023 and 2025.
July 16, 2026
Annual and cumulative FDA authorizations of AI/ML-enabled medical devices, 1995–2025.
Incorrect AI suggestions influence reader performance on mammography
By Amerigo Allegretto
The biggest impact on reader performance stemmed from false-negative AI suggestions.
July 14, 2026
A normal mammogram confirmed by three-year radiologic follow-up illustrates reader-marked regions of interest (ROIs) during (A) unaided (round 1) and (B) artificial intelligence (AI)–assisted (round 2) reading. Each colored dot represents an ROI for recall by a human reader. Readers could mark more than one ROI per case, represented by multiple dots of the same color. During AI-assisted reading, the AI system displayed three visible prompts: two with suspicion of malignancy scores of 35% (left mediolateral oblique [L MLO] and craniocaudal [L CC]) and one with a suspicion of malignancy score of 10% (right craniocaudal [R CC]), shown as polygonal overlays. Without AI, six of 10 readers (60%) marked a false-positive ROI. With AI assistance, this fell to two of 10 (20%). R MLO = right mediolateral oblique.
AR-guided localization may sub for CT for early-stage lung cancer
By Amerigo Allegretto
AR-guided localization was noninferior to conventional CT guidance and led to better patient experiences.
July 8, 2026
Lungs 3 D Research 400
Most patients want to know if AI being used for imaging reports
By Amerigo Allegretto
Most patients want disclosure about whether AI is being used for their imaging reports.
June 30, 2026
Patient Cancer Doctor 400 (1)
Advancing precision medicine through quantitative imaging
By Liz Carey
Coalition develops international QI technical standards across all imaging modalities, envisions credentialed centers.
June 22, 2026
Precision Medicine Concept As 2061010022 Marta P Milacroft
SIIM: CVMs on par with LLMs for labeling incidental breast findings
By Amerigo Allegretto
CVMs can accurately label incidental breast abnormalities found on chest CT exams.
June 19, 2026
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Women's Imaging MinnieCast, Episode 11: Could AI be the future of BI-RADS?
By Amerigo Allegretto
Linda Moy, MD, shares how AI could further improve BI-RADS and predicting breast cancer risk.
June 19, 2026
Minnie Cast Thumbnail (11)
Radiology, AI, and the shift from curiosity to accountability
By Rob Carfagno
Radiology executives are beginning to focus on AI performance in practice operations.
June 19, 2026
Rob Carfagno
Using RIS system log data cuts CT scan times, reduces staff overtime
By Kate Madden Yee
The findings could translate to workflow improvements in radiology departments struggling with overtime and staff fatigue.
June 17, 2026
Am 2020 08 20 15 29 8929 Ct Scanner Control Room 400
SIIM: Supplemental PACS can help validate AI imaging workflows
By Amerigo Allegretto
A presentation given at SIIM 2026 showed how a supplemental PACS could help with testing and validating AI imaging workflows.
June 17, 2026
Img 6956
Podcast: The PACSMan Pontificates, Episode 3: How real is AI?
By Michael J. Cannavo
In the latest episode of The PACSMan Pontificates, Michael Cannavo assesses a fascinating time in AI.
June 15, 2026
Pacsman Pontification
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