Imaging Informatics: Page 2

Radiologists, LLMs fail at spotting AI-generated chest x-rays
By Will Morton
Neither humans or LLMs appear well suited to serve as the primary safeguard.
October 15, 2026
Digital illustration of an AI face with blue light and binary code representing artificial intelligence and technology
Manual, DL-generated segmentations differ in radiomic features
By Amerigo Allegretto
DL models showed poor or fair agreement with manual segmentation, as well as bias, on abdominal CT and MRI.
October 14, 2026
Panoramic view of person in white clothing inside purple-walled room with photo display
HPI: Evidence contradicts broad claims about AI, radiology workforce
By Amerigo Allegretto
A team from the Neiman HPI expressed concern about comments published in JAMA Health Forum on the radiology workforce.
October 13, 2026
Professional businessman with holographic brain and digital network visualization representing AI and technology innovation
AI leads to lower turnaround times for high-volume imaging
By Amerigo Allegretto
Along with lower turnaround times, a study revealed that most latency issues from AI use are due to data routing.
October 8, 2026
Healthcare professional in blue scrubs examining digital analytics on tablet in modern medical laboratory
Chat messaging platform could reduce phone call disruptions
By Amerigo Allegretto
A chat platform integrated within the EMR and targeted education reduced phone call volume by half in a radiology department.
October 1, 2026
Radiologist reviewing CT and X-ray scans on dual monitors in medical imaging workstation
Unified imaging systems are a C-suite imperative
By Scott Miller
Scott Miller of GEHC discusses the trend toward unified imaging platforms in a new column.
October 1, 2026
Professional headshot of a man in dark blazer and striped blue shirt with red tie
Making AI matter at the point of care
By Kelly Londy
In a new column, Kelly Londy of GE HealthCare describes what's needed to unlock the benefits of AI in radiology.
September 30, 2026
Professional headshot of a smiling woman with auburn hair wearing dark clothing against an urban background
CT-based AI model predicts pneumonitis in lung cancer patients
By Amerigo Allegretto
The algorithm outperformed clinical and radiomics models in these patients.
September 25, 2026
Axial CT scan of chest showing bilateral lungs with contrast enhancement and anatomical detail
PACS workstations add to power consumption in radiology departments
By Amerigo Allegretto
PACS workstations consume about 25 kW when active while also consuming power cumulatively in powered down states.
September 23, 2026
Medical professional reviewing X-ray images on dual monitors in clinical workspace
MRI-based model shows changes over time in multiple myeloma patients
By Amerigo Allegretto
Whole-body MRI could serve as a scalable biomarker for risk stratification in multiple myeloma patients.
September 4, 2026
Blood smear microscopy showing purple-stained white blood cells and hematopoietic cells on light background
eConsult systems may benefit radiology-provider communication
By Amerigo Allegretto
Electronic consult (eConsult) platforms provide documented pathway for imaging questions and support radiologists as consultants.
September 3, 2026
Doctor in white coat compassionately holding patient's hand during medical consultation
AI cuts chest CT reporting time
By Will Morton
Exploratory modeling suggested an approximate reduction of 0.5 full-time equivalent radiologist workload.
September 1, 2026
Example of AI-assisted pulmonary nodule detection and longitudinal volumetric assessment in a 65-year-old woman with peripheral arterial disease, a 24-pack-year smoking history, emphysema, and a family history of lung cancer. An incidental 8.5-mm right lower lobe pulmonary nodule is identified at CT angiography of the abdomen in July 2022 but does not receive follow-up. Subsequent imaging demonstrates interval growth, and CT-guided biopsy confirms stage IA lung adenocarcinoma. Contrast-enhanced axial chest CT images obtained on February 2, 2024, before robotic-assisted thoracoscopic segmentectomy, show a 12.1-mm spiculated right lower lobe pulmonary nodule. (A) Conventional contrast-enhanced axial chest CT image without AI assistance shows a 12.1-mm spiculated right lower lobe pulmonary nodule. (B) Corresponding CT image with AI-assisted pulmonary nodule detection, automated segmentation, and quantitative nodule assessment identifies a solid pulmonary nodule (nodule #1) measuring 13 × 8 (11) mm, with a volume of 739 mm3, a 45% volumetric increase compared with the previous contrast-enhanced chest CT performed on October 9, 2023 (510 mm3), and an estimated volume doubling time (VDT) of 216 days. (C) AI-generated longitudinal nodule analysis displays automated segmentation of the current (February 2, 2024) and previous (October 9, 2023) CT examinations, 3D nodule reconstruction, and quantitative volumetric assessment for longitudinal follow-up. The AI interface is displayed in the original software language (Dutch).
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