Imaging Informatics

Most FDA-cleared AI medical devices not tested on patient outcomes
By Amerigo Allegretto
Of the 1,059 FDA-cleared devices used in radiology, three had a registered trial.
August 19, 2026
Artificial Intelligence Ai Global Communication Network 400
AI framework could improve MRI quality control in prostate cancer
By Amerigo Allegretto
The framework uses a DL model trained on low- and high-quality MR images and helped improve radiologist accuracy.
August 19, 2026
Low-quality axial T2-weighted MRI scans with gradient-weighted class activation mapping. The low-quality MRI scans (top row) show blurred structures and artifacts making zonal and capsule boundaries indistinguishable. On the same scans with gradient-weighted class activation map overlays (bottom row), red indicates regions that were most heavily weighted by the deep learning model toward the low-quality prediction.
Longitudinal DBT could help improve long-term cancer risk prediction
By Amerigo Allegretto
A longitudinal DBT model outperformed other models based on mammography and clinical factors in predicting five-year cancer risk.
August 13, 2026
Breast Dbt 400
AI tools may not meet breast radiologists' expectations in practice
By Amerigo Allegretto
AI tools in breast imaging may have more modest clinical impact on recall and biopsy rates.
August 12, 2026
Breast Mammo Computer 400 (3)
Model confidence, reader expertise affect reader-LLM collaboration
By Amerigo Allegretto
Successful human-LLM collaboration is associated with model confidence and reader expertise in chest imaging.
August 11, 2026
Example cases illustrate the double-edged role of large language model (LLM) rationale quality. (A) A 50-year-old female patient with a palpable anterior chest wall mass (correct diagnosis: solitary fibrous tumor of the pleura). The LLM incorrectly suggested synovial sarcoma but provided a persuasive rationale (quality score: 4 of 5), describing a well-defined nodule at axial contrast-enhanced CT spanning the pleura and intercostal space with heterogeneous strong enhancement. Four of five readers (80%) accepted the incorrect suggestion. FDG = fluorodeoxyglucose. (B) A 61-year-old male patient with diabetes presented with cough, sputum, and dyspnea (correct diagnosis: mucormycosis). The LLM correctly suggested mucormycosis with an excellent rationale (quality score: 5 of 5), integrating the reverse halo sign on serial axial CT images and rapidly progressive cavitary lesions. Three of five readers initially selected incorrect diagnoses; after LLM output, all five selected the correct diagnosis (100% acceptance). Blue and orange reader icons denote high- and low-expertise readers, respectively.
Many women interested in AI-based risk assessment for breast cancer
By Amerigo Allegretto
Survey findings also show that most women at higher risk are interested in additional screening tests.
August 10, 2026
Breast Cancer 400
DBT-based foundational model classifies breast density
By Amerigo Allegretto
DBT-DINO showed strong performance in classifying breast density from DBT images.
August 4, 2026
(A) Free-response receiver operating characteristic (FROC) curve for DBT-DINO and DINOv2 (Meta AI) on the detection task. DINOv2 achieved higher sensitivity than DBT-DINO across most false-positive thresholds. (B) Detection rates of DBT-DINO and DINOv2 for lesions that were ultimately found to be benign (n = 70) and lesions that were found to be cancerous (n = 66). Error bars represent 95% CIs estimated via patient-level bootstrapping with 1000 repetitions. Both models showed similar detection rates for cancerous lesions, whereas DINOv2 detected more benign lesions than DBT-DINO. (C) Example bounding box predictions for DBT-DINO and DINOv2 on the detection test set. Each image is a single two-dimensional section from a three-dimensional digital breast tomosynthesis volume; the first column shows a mediolateral oblique view, and the second and third columns show craniocaudal views. Both models struggled to detect very small lesions, such as the 3-mm-diameter lesion shown in the first column. FP = false positive.
Can LLMs fill the communication gap with referring clinicians?
By Will Morton
LLMs could potentially help address multiple persistent pain points in radiology workflow.
August 4, 2026
Ai Head
MRI-based AI model maps local brain aging
By Amerigo Allegretto
The model showed differences in brain structures for normal cognition, mild cognitive impairment, and Alzheimer’s disease.
August 3, 2026
Mri Scan Brain
DeepHealth lands FDA nod for breast ultrasound AI software
By AuntMinnie.com staff writers
The software automates lesion detection, characterization, and reporting, according to the RadNet subsidiary.
July 31, 2026
Deephealth White On Purple
Podcast: The PACSMan Pontificates, Episode 4: The perfect PACS
By Michael J. Cannavo
In episode 4 of his The PACSMan Pontificates podcast series, Michael J. Cannavo shares his thoughts on the quest for the perfect PACS.
July 30, 2026
Pacs Man Pontification
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).
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