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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An example of an FDA cleared radiology AI algorithm to automatically take a cardiac CT scan and identify, contour and quantify soft plaque in the coronary arteries. The Cleerly software then generates an automated report with images, measurements and a risk assessment for the patient. This type of quantification is too time consuming and complex for human readers to bother with, but AI assisted reports like this may become a new normal over the next decade. Example from Cleerly Imaging at SCCT 2022.

Legal considerations for artificial intelligence in radiology and cardiology

There are now more than 520 FDA-cleared AI algorithms and the majority are for radiology and cardiology, raising the question of who is liable if the AI gets something wrong.

Surgeons Operating On Patient

AI model predicts risk of post-operative AFib

Post-operative atrial fibrillation was once viewed as a fairly insignificant issue, but more recent research suggests it can increase a patient’s risk of multiple adverse events. 

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AI program ChatGPT now has a published article in Radiology—is it any good?

The human author reviewing the article wrote about the benefits and inherent risks of utilizing AI in a medical publication setting, concluding that, overall, it could be “a powerful tool” used in the future of medical publishing—when used with caution.

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

VIDEO: Who gets sued when radiology AI fails?

Brent Savoie, MD, JD, vice chair for radiology informatics, section chief of cardiovascular imaging, Vanderbilt University, explains who will get sued when there is a misdiagnosis due to artificial intelligence (AI).

FDA greenlights ultrasound MSK software with AI

Clarius Mobile Health of Vancouver, B.C., has won FDA approval to market an AI model that works with the company’s handheld point-of-care ultrasound devices to identify and measure tendons of the foot, ankle and knee.

AI helps reading-room radiologists differentiate colon cancer from diverticulitis

The model augmented and significantly improved diagnostic performance for abdominal subspecialists as well as residents—a result researchers say has major clinical implications.

#CTA #acuteischemicstroke #AIS #radiomics

CTA-based radiomics can reliably estimate time since stroke onset

Radiomics features could be especially useful for determining TSS in scenarios where clinical data is lacking or unreliable, such as when patients have altered mental status.

The key to AI integration? Keeping it straightforward, says GE HealthCare CMO

“To drive adoption, it is important that the technology designed to help productivity doesn’t add more work and complexity,” Dr. Mathias Goyen, Chief Medical Officer for Europe, the Middle East and Africa (EMEA) at GE HealthCare, told Health Imaging.

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