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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Deep learning applied to chest radiographs efficiently identifies early interstitial lung disease

The results could lead to expanded use of chest radiography in interstitial lung disease clinics, experts reported in the American Journal of Roentgenology.

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New AI technology developments consistently recognize medical image anomalies

Promising new research methods have been able to train AI to more accurately spot image anomalies.

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Why deep learning trained on radiologist-labeled data may be worth added time, costs

This proved true for detecting pneumothorax, as networks developed on hand-labeled images outperformed tools refined via natural language processing.

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AI in radiology is just getting started, but these 4 lessons can help practices prepare

Massachusetts General Hospital gathered reports from those on the front lines of the coming AI revolution, sharing their tips for success in JACR.

Artificial intelligence deters one-sixth of medical students from pursuing radiology

By comparison, more than 20% ranked radiology as their first choice when not considering AI's potential impact on the specialty.

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AI-based free-text image ordering may save federal CDS mandate from overburdening providers

Clinicians opted to use the AI tool nearly 60% of the time compared to directly searching for structured exam indications in 41% of cases.

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Screening mammography ‘signals’ predict women’s breast cancer risk

Stratifying exams according to risk can reduce unnecessary imaging and downstream costs of care, Hawaiian researchers reported in Radiology.

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AI-driven breast cancer screening ‘a long way off’ from replicating radiologists

Ninety-four percent of systems were less accurate than a single radiologist and all fell short when put up against at least two rads.

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