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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Physicians’ behaviors are nearly all AI needs to head off faulty drug prescriptions

Contrary to intuitive expectations, many errors in drug ordering are caused or worsened by the intricacies of the EHR.

AI helps uncover, illustrate the inner workings of cells

Researchers have used electron microscopes and machine learning to create detailed, high-resolution 3D images of subcellular structures called organelles.

AI finds forgotten surgical instruments in patients’ bodies

In hospitals where patients are routinely X-rayed following surgery, AI-equipped CAD software could screen for left-behinds automatically.

Algorithmic app could head off suicides of high-risk adolescents

Researchers have demonstrated the feasibility of AI-based smartphone interventions for young people who have attempted suicide and may try again.

3 steps toward setting and sustaining standards for medical AI

If AI for medical diagnostics is to lift the health status of populations—and thus fulfill its implicit global promise—it’s going to need stronger regulatory guidance than it’s gotten to date. 

Facial recognition AI measures success of browlifts

Machine learning can deliver an objective appraisal of cosmetic surgery’s success at making aging faces appear younger and happier, according to Mayo Clinic researchers.

Diabetes AI adjustable for surveilling other public health concerns

Researchers have used machine learning to track diabetes at the population level.

‘Smart’ EHR taps AI to guide not just info retrieval but also treatment planning

AI developers have worked with experts in human-computer interaction to design an EHR that shows clinicians all information pertinent to the patient case they’re working on—and only that info.

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