AI

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
CMS approves Medicare add-on payment for Aidoc CT triage AI
By AuntMinnie.com staff writers
Qualifying Medicare Fee-For-Service inpatient cases using the device will be eligible for reimbursement.
August 13, 2026
Artificial Intelligence Ai Medical Healthcare Tools 400
Nanox AI optimizes CT imaging framework for Intel processors
By AuntMinnie.com staff writers
The company said this enables on-premise deployment of its CT imaging AI software within healthcare facilities.
August 13, 2026
Ct Scanner Control Room 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.
Twelve health systems form Diagnostic AI Consortium with Aidoc
By AuntMinnie.com staff writers
The consortium will co-design AI-enabled diagnostic workflows and develop shared implementation and governance practices.
August 11, 2026
Artificial Intelligence Ai 400
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
READ-DVT model performs well in finding DVT from ultrasound reports
By Amerigo Allegretto
The AI model accurately identified the presence or absence of deep vein thrombosis on unstructured ultrasound reports.
August 7, 2026
Radiological Report 400
Nanox raises $8M in registered direct offering
By AuntMinnie.com staff writers
The company has entered into a securities purchase agreement for the sale of 8 million ordinary shares and accompanying warrants.
August 6, 2026
Money Bills 400
RSNA launches 2026 Knee Abnormality Detection AI Challenge
By AuntMinnie.com staff writers
The challenge will focus on musculoskeletal MRI and combine medical images with multilingual radiology reports.
August 6, 2026
New Rsna Signature Logo Rgb
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
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