AI: Page 23

Can an open-source large language model make the grade in radiology?

By Erik L. Ridley

Meta’s Llama 3 70B open-source large language model offers comparable performance to proprietary models in answering multiple-choice radiology test questions.

August 13, 2024

Artificial Intelligence Ai Binary Code

The PACSman Pontificates: How can AI save itself?

By Michael J. Cannavo

In a new column, Michael J. Cannavo, aka the PACSman, shares his thoughts on how the radiology AI market can survive.

August 7, 2024

Michael J. Cannavo, aka the PACSman.

AI-powered ultrasound tool aids novices in obstetric imaging

By Amerigo Allegretto

An AI-powered tool can help novice sonographers achieve as high an accuracy as expert sonographers in measuring fetal gestational age.

August 2, 2024

Fetal Ultrasound 400

AI education is a necessity for newest class of radiologists

By Morris Panner

Intelerad President Morris Panner emphasizes the importance of education on radiology AI technology for new radiologists.

August 2, 2024

Artificial Intelligence Ai 400 66718ede18e05

ChatGPT makes echo reports readable for patients

By Amerigo Allegretto

ChatGPT may be on its way toward explaining echocardiography results to patients.

July 31, 2024

AI in radiology receives more positive coverage in U.S. news media

By Amerigo Allegretto

AI’s impact on radiology is “mostly” covered in a positive light in U.S. news media, though radiologists may not be included in such coverage.

July 29, 2024

CXR-Age algorithm validated in large cohort of Asian individuals

Researchers in Korea have validated a deep-learning AI model (CXR-Age) that estimates biological age from chest x-rays.

July 26, 2024

2024 07 24 Cxr Age Thumbnail

AdvaMed urges faster reimbursement pathway for AI-enabled medtech

By AuntMinnie.com staff writers

Reimbursement policies should keep pace rather than come years after FDA authorization, according to AdvaMed.

July 26, 2024

Capitol Hill Government Social

Podcast: Moving from a role in academic medicine to leading a company

Katherine High, MD, joins Saurabh (Harry) Jha, MD, and Mitchell Schnall, MD, for this episode of the "Keeping Up With the Radiologists" podcast series.

July 26, 2024

How imaging AI developers can avoid pitfalls when testing algorithms

By Amerigo Allegretto

AI developers can recognize and avoid pitfalls when creating models and tools for interpretative imaging performance.

July 25, 2024

CT images from a 24-year-old woman with history of ventriculopleural shunt placement who presented with chest pain show two small linear hyperattenuating structures (A, arrows), with larger hyperattenuating structure oriented parallel and very close to the rib. The AI algorithm interpreted the finding as a rib fracture. (B) An additional axial image from same exam indicates that the finding relates to the patient’s ventriculopleural shunt (arrow), passing alongside the rib. The radiologist made a correct interpretation upon assessment of the entire exam. Image courtesy of the ARRS.

GPT-4 can assist in management of glioblastoma patients

Research suggests GPT-4 could streamline longitudinal assessment process by providing a comprehensive overview of prior imaging.

July 23, 2024

Chat Gpt 667b17100916a

Mammo AI falls short to ultrasound in screening dense breasts

By Amerigo Allegretto

Mammography plus ultrasound yields better results than AI-enhanced mammography alone when it comes to screening of women with dense breasts.

July 23, 2024

Imaging in a 56-year-old woman with heterogeneously dense breasts (BIRADS C). (A) Left craniocaudal (left) and mediolateral oblique (right) digital mammograms show no abnormality. (B) Left craniocaudal (LCC, top) and left mediolateral oblique (LMLO, bottom) mammograms with retrospectively applied AI (Lunit Insight MMG, version 1.1.7.1; Lunit) show the lesion was given abnormality scores of 97% and 71%, respectively. The color bar indicates pixel-level abnormality scores corresponding to heatmap contour lines. Breast ultrasound performed the same day was considered negative (not shown). (C) Left craniocaudal (left) and mediolateral oblique (right) digital mammograms acquired nine months later show an irregular mass (arrows) in the left breast, which correlates with the heatmap areas scored as 97% and 71% by AI in the earlier screening mammogram (B). The radiopaque round marker denotes the site of the palpable abnormality indicated by the patient. (D) Transverse ultrasound imaging shows an irregular mass (arrow) in the left breast. The patient was treated with breast-conserving surgery, and the mass was proven to be invasive lobular carcinoma. Image courtesy of the RSNA.

Page 23 of 391