Imaging Informatics

MRI-specific AI algorithm reads cardiac scans with up to 99% accuracy
By Kate Madden Yee
The algorithm beat existing general-purpose AI models by up to 35%, researchers reported.
May 21, 2026
Cardiac Mri Adobe Samunella
Podcast: The PACSMan Pontificates, Episode 2 -- RFPs
By Michael J. Cannavo
Are RFPs still needed for buying a PACS? Michael J. Cannavo, aka the PACSMan, tackles this question in episode 2 of The PACSMan Pontificates podcast series.
May 20, 2026
Pacsman Pontification
Human-AI collaboration beats AI alone for identifying PE
By Kate Madden Yee
Study findings highlight the continued central role of radiologists for this indication.
May 20, 2026
Case example of a false-negative AI result. An 85-year-old female presented to the emergency department with dyspnea. Axial 0.625-mm from CT pulmonary angiography examination with contrast (CTPA) images demonstrate a thin, linear right-sided filling defect extending from the right interlobar pulmonary artery (A and B, red arrows) into the right middle lobar artery (B, yellow arrow), consistent with acute pulmonary embolism (PE). The finding was described in the radiology report; however, the AI result was negative for PE. Adjudication was consistent with acute PE.
AI model predicts 10-year breast cancer risk
By Amerigo Allegretto
A mammography-based AI model showed strong performance in predicting 10-year breast cancer risk over clinical and other AI models.
May 20, 2026
Breast Cancer3
AI devices vary widely in lung cancer detection
By Will Morton
Three devices helped detect more cancerous tumors, whereas the other four devices helped detect fewer tumors.
May 19, 2026
Cropped secondary capture examples. These are illustrative and not intended to imply superiority or inferiority of any device. (A) Posteroanterior radiograph in a 46-year-old female patient. The device correctly identified a right lower lobe nodule projected below the right hemidiaphragm and hilar lymphadenopathy. (B) Posteroanterior radiograph in an 86-year-old female patient with a classic Golden S sign highly suggestive of cancer. Three devices did not identify any findings. (C) The output from one device for the same radiograph as in B. The device placed a contour around the area of abnormality but mislabeled it as segmental collapse, and there are no other elements in the output to raise suspicion of cancer. (D) Posteroanterior radiograph in a 60-year-old male patient -- a case of confirmed lung cancer that was not deemed visible in retrospect. The device identified multiple false-positive abnormalities. (E) Posteroanterior radiograph in a 77-year-old female patient with two right lower lobe nodules. The device mislabeled the abnormality as infection -- a diagnostic term that could incorrectly influence clinical management. (F) Posteroanterior radiograph in a 77-year-old female patient with a right hilar tumor. Most of the lungs have been labeled by the device, with excessive overlap of the abnormality that pragmatically represents an incorrect result. All annotations shown were produced by the devices. LL = lung lesion, LO = lung opacity, PO = pleural other, TBC = tuberculosis.
MRI-based ML model shows promise in colorectal cancer subtyping
By Amerigo Allegretto
A machine-learning model based on MRI radiomics achieved high performance in predicting colorectal cancer subtypes.
May 19, 2026
Overview of the study design, including data collection, radiomics model construction, and biologic interpretation. (A) The primary cohort for model development and evaluation in the training and internal test sets includes 168 patients with colorectal cancer (CRC) from center 1 (Fudan University Shanghai Cancer Center), whereas external testing was performed using data from 85 patients from centers 2 (Huashan Hospital, Fudan University) and 3 (Zhongshan Hospital, Fudan University). For model interpretation, RNA sequencing (RNA-seq) data from 18 patients with CRC in center 1 and single-cell data from 35 patients from the Gene Expression Omnibus (GEO) database are incorporated. Mismatch repair (MMR) protein expression, obtained from pathologic reports in electronic medical records (EMRs), was used to identify patients with consensus molecular subtype 1 (CMS1) CRC. Tissue microarrays (TMAs) from surgically obtained CRC tissues were analyzed for immunohistochemical staining of caudal-type homeobox 2 (CDX2), 5-hydroxytryptamine receptor 2B (HTR2B), FERM domain containing 6 (FRMD6), and zinc finger E-box binding homeobox 1 (ZEB1), and the results were subsequently entered into an online classification tool that assigned patients to CMS2, 3, or 4 subtypes. (B) Radiomics workflow for CMS4 prediction. Lesions were manually delineated on T2-weighted imaging (T2WI) and contrast-enhanced (CE) T1-weighted imaging (T1WI), followed by radiomics feature extraction. Feature selection and model development were performed in the training set using machine-learning algorithms with fivefold cross-validation for model selection, and the final model was evaluated in the internal and external test sets using receiver operating characteristic (ROC) curve analysis. (C) Transcriptomic interpretation workflow. Using the radiomics-predicted CMS4 classification, bulk RNA sequencing (RNA-seq) data were used to perform differential expression analysis between predicted CMS4 and non-CMS4 groups, followed by pathway enrichment analyses to assess biologic relevance. Public single-cell RNA sequencing data from the Gene Expression Omnibus database were then used for cell-type–level interrogation to support interpretation of the associated pathways.
ISMRM: Researchers highlight advancements in MRI foundation models
By Liz Carey
Standardization is key to effective large-scale studies.
May 14, 2026
OmniMRI in the context of reporting.
Women's Imaging MinnieCast, Episode 8: New NCCN guidance on AI and breast cancer risk
By Amerigo Allegretto
Bethany Niell, MD, PhD, talks new guidance on breast cancer screening by the NCCN, which includes image-based AI risk assessment.
May 8, 2026
Minnie Cast Thumbnail (4) (1)
Imaging fellow tries vibe coding to solve radiology challenges
By Liz Carey
Vibe coding outside the radiology department led to exploring real-world use cases.
May 6, 2026
Kaustav Bera, MD, discussed the rise of the radiologist-developer with AuntMinnie. In June, Bera will complete an abdominal imaging fellowship at Case Western Reserve University and plans to continue as an abdominal imager at Case Western.
MRI reveals how hidden muscle fat increases risk to heart, metabolism
By Kate Madden Yee
The results suggest that MRI exams could "augment traditional risk factor screening," researchers noted.
May 5, 2026
Fat Tissue
DL model outperforms breast density in measuring women's cancer risk
By Amerigo Allegretto
A DL model outperformed breast density in estimating future breast cancer risk.
May 4, 2026
Breast Mammo Computer 400 (3)
Preventative screening and the future of endometriosis care
In a column, SimonMed chief medical officer and chief innovation officer Sean Raj, MD, MBA, shares how radiology is poised to lead the transformation of endometriosis care.
April 30, 2026
Sean Raj, MD, MBA
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