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-powered CCTA assessments show ‘close agreement’ with IVUS
Advanced AI software developed by Cleerly consistently produced plaque assessments comparable to intravascular ultrasound.
US doctors scoot toward 100% AI adoption
A new Doximity survey shows 94% of American physicians using AI or at least keen on it. Just as tellingly, the pace of adoption is brisk and accelerating.
New real-world evidence supports the use of AI in lung cancer screening
Though the study of AI in lung cancer screening is not new, prior research has been retrospective in nature, making it challenging to determine the impact.
Not all AI is created equal—experts caution against 'useless' applications
“If we are not careful, we run the risk of spending more time admiring these novelty-driven tools than advancing the real frontiers of radiology.”
AI turns wearable device data into actionable insights for heart failure patients
Afnan Tariq, MD, discusses early data on a passive, device-agnostic AI platform for heart failure monitoring. “When clinicians are empowered with insights and able to act earlier, you're able to have a durable impact," he said.

Example of an automated artificial intelligence (AI) assessment of soft coronary plaque from a CT scan from the vendor Cleerly. This image shows the AI's reconstruction of a patient's coronary tree and color codes the vessel segments by the amount of overall plaque burden. The AI gives a very detailed report of all the plaque in all the coronary vessels. Some cardiology experts believe this may be the way of the future in screening patients for early coronary disease and monitoring the impact of prevention

AI-powered CCTA assessments show ‘close agreement’ with IVUS

Advanced AI software developed by Cleerly consistently produced plaque assessments comparable to intravascular ultrasound.

US doctors scoot toward 100% AI adoption

A new Doximity survey shows 94% of American physicians using AI or at least keen on it. Just as tellingly, the pace of adoption is brisk and accelerating.

New real-world evidence supports the use of AI in lung cancer screening

Though the study of AI in lung cancer screening is not new, prior research has been retrospective in nature, making it challenging to determine the impact.

AI artificial intelligence stethoscope doctor

Not all AI is created equal—experts caution against 'useless' applications

“If we are not careful, we run the risk of spending more time admiring these novelty-driven tools than advancing the real frontiers of radiology.”

Afnan Tariq, MD, JD, FSCAI, FACC, an interventional cardiologist, assistant clinical professor of medicine at University of California, Irvine, presented first-in-man data on a passive, device-agnostic artificial intelligence (AI) platform for heart failure monitoring using consumer wearables at the recent Technology and Heart Failure Therapeutics (THT) 2026 meeting. The study showed the AI could help lower repeat hospitalizations.

AI turns wearable device data into actionable insights for heart failure patients

Afnan Tariq, MD, discusses early data on a passive, device-agnostic AI platform for heart failure monitoring. “When clinicians are empowered with insights and able to act earlier, you're able to have a durable impact," he said.

AI model could help providers choose more appropriate imaging exams

The model was trained on guideline-adherent data specific to clinical scenarios that would prompt a provider to order medical imaging.

Umar Ahmed from Signify Research explains key trends in radiology AI.

Radiology AI vendors shift focus to workflow integration and enterprise value

AI in medical imaging market analyst Umar Ahmed from Signify Research explains some of the key trends seen in radiology artificial intelligence.

Researchers develop program that can spot falsified radiology reports written by AI

“With generative AI becoming more capable of producing remarkably convincing radiology reports, there’s a greater risk of fabricated reports being used to falsify medical histories and support fraudulent claims,” the team cautioned.