Artificial Intelligence

Artificial intelligence (AI) is becoming a crucial component of healthcare to help augment physicians and make them more efficient. In medical imaging, it is helping radiologists more efficiently manage PACS worklists, enable structured reporting, auto detect injuries and diseases, and to pull in relevant prior exams and patient data. In cardiology, AI is helping automate tasks and measurements on imaging and in reporting systems, guides novice echo users to improve imaging and accuracy, and can risk stratify patients. AI includes deep learning algorithms, machine learning, computer-aided detection (CAD) systems, and convolutional neural networks.

AI automation could 'revolutionize' Crohn's Disease severity CT assessments
Machine learning models could help create a more standardized, reproducible and efficient way of grading Crohn’s disease severity in the small bowel based on CT imaging.
New publicly available deep learning model for CT organ segmentation in children shows promise
The model was developed and validated specifically for liver, spleen and pancreas segmentation, and outperformed a publicly available segmentation model already in use.
New research offers reminder of why ChatGPT should not be used for second opinions
Although these tools have proven themselves valuable in numerous settings, they must be used with caution, especially by patients and nonradiologist providers who may be seeking clarification on imaging reports.
GPT-4 confidently struggles on radiology exam
Image-based questions were significantly more challenging for the large language model to answer, despite the latest version now being capable of accepting image prompts.
AI improves CT assessments, boosts care for real-world heart patients
Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

AI reads digital pathology slides to help improve cancer outcomes

The tool, detailed in an EBioMedicine study published last month, can sift through the multitudes of cells in a tissue sample and identify tumors’ growth patterns, along with other highly useful information for predicting health outcomes.

December 10, 2019

FDA announces public workshop on AI in radiology

The public workshop will take place this upcoming February and will discuss computer-aided detection and diagnosis software, computer-aided triage systems and image quality enhancement algorithms, among other topics.

December 10, 2019

AI predicts patients' future healthcare costs from chest x-rays

The technique combines AI with patient-specific health and cost information for a rough estimate on an individual's five-year healthcare expenditures.

December 2, 2019

RSNA names winners of intracranial hemorrhage AI challenge

The challenge tasked teams with developing an algorithm capable of identifying and classifying subtypes of hemorrhages on head CT scans.

December 2, 2019

Machine learning creates image ‘atlas' to improve disease diagnoses

Massachusetts Institute of Technology researchers harnessed machine learning to create conditional atlases that can help clinicians diagnose a wider subset of patients.

November 27, 2019

Historic medical objects imaged for insights

Duke researchers have used micro CT to peer deep into medicine’s past.

November 27, 2019

fMRI reveals prenatal opioid exposure changes brain connectivity in babies

Infants exposed to such drug use suffer from withdrawal when they are born, also called neonatal abstinence syndrome. The condition requires lengthy hospital stays with severe situations calling for opioid treatment.

November 25, 2019

ACR releases repository of FDA-cleared AI imaging algorithms

The models encompass a wide variety of diagnostic tasks, including pneumothorax detection on chest x-rays and highlighting brain segments on MRI scans.

November 22, 2019

Around the web

Cardiovascular Business

AI improves CT assessments, boosts care for real-world heart patients

Automated AI-generated measurements combined with annotated CT images can improve treatment planning and help referring physicians and patients better understand their disease, explained Sarah Jane Rinehart, MD, director of cardiac imaging with Charleston Area Medical Center.

Cardiovascular Business

AI predicts cardiovascular risk during CT scans—no invasive tests or contrast required

Two advanced algorithms—one for CAC scores and another for segmenting cardiac chamber volumes—outperformed radiologists when assessing low-dose chest CT scans.

Radiology Business

Google teams with Bayer to address radiologist burnout via AI

"Gen AI can help tackle repetitive tasks and provide insights into massive datasets, saving valuable time," Thomas Kurian, CEO of Google Cloud, said Tuesday.