Imaging Informatics: Page 22

Podcast: Florence Doo on how AI is affecting radiology
By Kate Madden Yee
Florence Doo, MD, discusses how AI is affecting radiology and how best to put it to use in patient care.
June 4, 2025
Doo Thumbnail
AI-directed breast MRI scanning may lead to shorter scan times
By Amerigo Allegretto
AI-directed stratified breast scanning could help decrease scan times in breast imaging by triaging women to abbreviated MRI protocols.
June 3, 2025
2025 06 03 Rsna Ai Breast Mri Triage Thumbnail
If AI finds an abnormality that a radiologist misses, who's at fault?
By Maryam Payne
Radiologists who miss an abnormality on an image are more likely to be seen as legally culpable if AI detects the abnormality.
June 3, 2025
Artificial Intelligence Ai And Law
The PACSman Pontificates: Should AI be free?
By Michael J. Cannavo
In a new column, consultant Michael J. Cannavo, aka the PACSman, shares his take on whether radiology AI should be free.
June 2, 2025
Michael J. Cannavo.
MRI radiomics helps predict liver cancer treatment response
By AuntMinnie.com staff writers
A study presented at ASCO 2025 highlighted the benefits of MRI radiomics analysis in predicting treatment response in patients with advanced liver cancer.
June 1, 2025
Pxl 20250531 175048475~2
Explainable DL MRI model shows promise diagnosing liver cancer
By Will Morton
A deep-learning (DL) MRI model not only classifies liver lesions but also provides visual explanations for its decisions.
May 30, 2025
AI deep learning model analyzing liver MRI scans to detect hepatocellular carcinoma with intensity projection technique
German researchers tout U-Net for pixel-level lung thickness maps
By Liz Carey
The deep-learning model may be helpful for diagnosing and preventing the progression of pulmonary diseases during routine chest x-ray screenings.
May 28, 2025
Qualitative results of eight frontal chest radiographs from the synthetic public test set, where each column represents one participant. Participants one through four are from the Luna16 test set, and participants five through eight are from the pulmonary embolism test set. (A) Simulated input radiographs, (B) predicted thickness maps, (C) reference standard thickness maps, and (D) absolute difference between the reference standard and predicted maps. The color bars for B and C indicate lung thickness in millimeters (mm). The color bar for D indicates the pixel-level estimation error in mm.
The PACSMan’s SIIM 2025 recap: A long way from home
By Michael J. Cannavo
The PACSMan, aka consultant Michael J. Cannavo, shares his takeaway points after remotely attending SIIM 2025.
May 28, 2025
Michael J. Cannavo.
SIIM: Are consumer-grade displays suitable for remote radiologists?
By Erik L. Ridley
Research presented at SIIM 2025 compared the performance of consumer-grade displays with diagnostic-grade displays for radiologists in hybrid work environments.
May 23, 2025
Katie Hulme of the Cleveland Clinic.
SIIM: Posters illustrate ways to improve radiology workflows
By Will Morton
A poster presentation at SIIM on May 23 illustrated simple ways to improve workflows.
May 23, 2025
Research poster on PACS-integrated web platform for radiologists with presenter
Video from SIIM: Nabile Safdar on integrating AI into radiology training
By Will Morton
We caught up with Nabile Safdar, MD, to discuss best practices for the integration of AI into radiology training.
May 23, 2025
Professional in business attire smiling against colorful geometric background
SIIM: Deep-learning model aids lumbar spine MRI analysis
By Erik L. Ridley
In a presentation at SIIM 2025, researchers shared the performance of a deep-learning algorithm on lumbar spine MRI exams.
May 22, 2025
Kay Wu, MD, presents the research group's results with DeepSpine at SIIM 2025.
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