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. 

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Industry Watcher’s Digest

Buzzworthy developments of the past few days.

National Science Foundation AI Institutes

$140M in federal funds primed to advance AI across 7 realms

The National Science Foundation (NSF) is setting up seven new institutes for studying foundational AI. Two of the initiatives have healthcare as a prime focus.

ChatGPT chatbot

ChatGPT helps radiologist churn out 16 papers in 4 months

“Healthcare is going to change. Writing is going to change. Research is going to change. I’m just trying to publish now and show it so people can know about it and explore more.” 

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ChatGPT shows ‘remarkable ability’ to process thoracic surgery data, could help with training and patient care

A research lab led by a veteran cardiothoracic surgeon found that ChatGPT—particularly the GPT-4 model—could 'potentially revolutionize' training in the years ahead. 

Artificial Intelligence robotics

Industry Watcher’s Digest

Buzzworthy developments of the past few days.

MAUDE database medical device safety

Risk points revealed in US database of AI-powered medical devices

Researchers analyzed 266 safety events reported to the FDA’s Manufacturer and User Facility Device Experience (“Maude”) program.

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Experts highlight core concepts that could help standardize AI curriculum

As artificial intelligence continues to cement itself into radiology workflows, a new analysis offers a detailed look at how radiology programs are adapting curriculum to reflect the impending changes. 

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How 'mindlessly' following AI guidance impacts radiologist performance

Radiologists interpreting screening mammograms may be especially susceptible to falling victim to automation bias, as these exams are repetitive in nature.