AI: Page 17

Can LLMs help track interval changes on radiology reports?

By Erik L. Ridley

Large-language models (LLMs) show potential for tracking interval changes on longitudinal radiology reports.

April 14, 2025

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AI-guided POCUS bests experts in detecting TB in underserved areas

By Amerigo Allegretto

AI-guided POCUS can accurately detect tuberculosis (TB), according to research presented at the ESCMID conference in Vienna, Austria.

April 13, 2025

Véronique Suttels, PhD, performs an ultrasound scan on a patient in Benin, West Africa, using a POCUS scanner connected to a smartphone. Her team studied how an AI-guided POCUS system can help diagnose tuberculosis in patients with respiratory symptoms. Image courtesy of Véronique Suttels, PhD.

Mimicking human vision: The new standard for medical image fidelity

By Ravinder Singh and Vaibhavi Sonetha, PhD

Ravinder Singh and Vaibhavi Sonetha, PhD, of Citius Healthcare Consulting, discuss a new method for assessing medical image fidelity.

April 11, 2025

Senators introduce medical AI reimbursement bill

By Amerigo Allegretto

A bill introduced in the U.S. Congress aims to establish a reimbursement pathway for FDA-authorized medical devices that rely on AI.

April 10, 2025

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AI bests rads in finding breast cancer in women with personal history

By Amerigo Allegretto

AI may have a role in finding contralateral disease in women with a personal history of breast cancer.

April 8, 2025

Images depict a 65-year-old patient with contralateral second breast cancer 6.3 years after right mastectomy. (A) Left craniocaudal and (B) mediolateral oblique mammograms assessed as benign. (C) The AI algorithm outlined a lesion with a score of 31 on the craniocaudal view. (D) A supplemental axial ultrasound scan, which was obtained on the same day as the mammogram, shows an irregular hypoechoic mass (arrows) in the left upper central breast. (E) Preoperative sagittal contrast-enhanced T1-weighted MRI scan shows the corresponding enhancing mass (arrow) in the left upper center. The mass was proven to be ductal carcinoma in situ.

Generative AI model shows promise in chest x-ray study

By Amerigo Allegretto

A multimodal generative AI model achieved high diagnostic accuracy and showed clinical value in preliminary reporting of chest x-ray images.

March 25, 2025

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ML-based system predicts x-ray service times

A team in Italy has developed a real-time machine-learning (ML) system that can predict x-ray service times in emergency departments.

March 20, 2025

Radiologists less susceptible to visual illusions

Medical image experts demonstrated superior perceptual accuracy in response to visual illusions compared with a control group.

March 20, 2025

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Do radiologists trust mammography AI software?

By Erik L. Ridley

A large-scale prospective trial exploring the use of mammography AI software in a breast cancer screening program has found that recalls were lower when cases were flagged by AI.

March 18, 2025

Full-field digital screening mammograms in participants recalled by the consensus discussion after being flagged by artificial intelligence (AI) computer-aided detection (CAD) but not by either of the two radiologists. (A) Mammogram with AI CAD score of 73 for a 55-year-old woman diagnosed with grade 3 in situ cancer and T1 (9-mm) invasive cancer in the left breast and lymph node metastasis. (B) Mammogram with AI CAD score of 54 for a 49-year-old woman diagnosed with grade 2 in situ cancer and T1 (6-mm) invasive cancer in the right breast and without lymph node metastasis. All images and caption courtesy of the RSNA.

DL algorithm targeting prostate cancer demonstrates clinical impact

Per participant, a deep-learning (DL) algorithm demonstrated increased specificity of 64.8% compared with radiologists at 21.3%.

March 12, 2025

MRI scans show two areas of clinically significant prostate cancer in a 65-year-old man with a prostate-specific antigen level of 8.23 ng/mL. Images are (A, E) axial T2-weighted images, (B, F) axial diffusion-weighted images obtained with a b value of 1500 sec/mm2, (C, G) axial apparent diffusion coefficient images, and (D, H) lesion masks generated by the deep learning algorithm (DLA) on T2-weighted images. The DLA detected a lesion in the right posteromedial peripheral zone of the apex (D), which was assigned as Prostate Imaging Reporting and Data System (PI-RADS) 4. A radiologist also detected the lesion and assigned it PI-RADS 4 (arrow in A-C). A lesion in the right anterior transition zone (H) was detected by the DLA, which was assigned PI-RADS 4. The radiologist did not detect the lesion (arrow in E-G). Both lesions were confirmed as prostate cancer with Gleason grade group 2. Caption and image courtesy of RSNA.

AI boosts identification of incident vertebral fractures on chest CT

By Kate Madden Yee

An AI algorithm that measures 3D whole thoracic vertebral bone mineral density on conventional chest CT can help predict incident vertebral fractures.

March 11, 2025

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Deep-learning radiomics model aids in ovarian cancer diagnosis

By Amerigo Allegretto

An ensemble model combining clinical variables, O-RADS, and deep learning radiomics can diagnose ovarian tumors.

March 5, 2025

(A) SHAP summary plot shows the global contributions of variables to the model predictions. (B-E) Local explanation of the model for a single instance of ovarian endometrioid carcinoma: SHAP waterfall plot (B), ultrasound grayscale image (C), ultrasound color Doppler image (D), and 10×10 histopathological image (E). Images are available for republishing under a Creative Commons license (CC BY-NC-ND 4.0).

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