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, politics and rising demand for imaging—how rad techs are faring amid an evolving professional landscape
Compared to other healthcare workers, technologists may have a more positive outlook of their career trajectories and how outside forces may affect them.
Newswatch: FDA’s AI plan ‘ahead of schedule and under budget,’ AI fears vs. comforts, AI ROI, more
We’re either teetering at the edge of AI overlordship—or just working with new tech tools that do the same old computer-y things we’ve always done. So which is it?
5 attributes every AI system will soon need to make hay in healthcare
“Dr. AI” will never replace a single human physician. But its many makers’ relentless pursuit of perfect performance all but guarantees the technology a prominent role in healthcare.
AMA brainstorms on key topics impacting healthcare
"With significantly strengthened penalties, standardized reporting requirements and unprecedented enforcement commitments, the regulatory landscape has dramatically shifted," the AMA wrote.
ChatGPT excels at assessing breast pain symptoms, deciding if patients require imaging
This task could potentially be performed by a member of the radiology scheduling team with oversight by a clinical nurse navigator.

Walmart launches AI tool to steer Medicare Advantage members toward ‘healthy’ foods

Walmart is highlighting food and products eligible for purchase using Medicare Advantage supplemental funds through its app, while also using AI to algorithmically help enrollees build diet-appropriate shopping lists.

Samsung invests in ultrasound imaging startup Exo, which has now raised $320M

Founded in 2015, Exo offers a portable platform that combines novel silicon-based hardware, “tightly coupled AI,” and “easy-to-use” collaborative software.

AI startup seeking to eliminate need for pre-op imaging raises $33M

See All AI says its platform transforms standard 2D C-arm fluoroscopic X-rays into “navigable” 3D volumes, reducing the need for CTs and MRIs before surgery.

artificial intelligence AI in healthcare

AI vs. medication errors, Health Tech Investment Act preps, employment apocalypse fears, more

Remember when comprehensive checklists and human vigilance were going to end medical error? That hasn’t happened yet. AI might be able to help.

Q&A: Universities, tech collaborate to predict opioid overdose with wearables

The Digital Medicine Society has partnered with organizations such as Duke University and Google Fitbit to leverage wearable technology and smartphone data in reducing opioid relapse and overdose deaths. As the pilot study gets underway, HealthExec speaks with Candice Taguibao, associate program director at the Digital Medicine Society, to learn more about the initiative.

How to let AI simplify the complexities of care (rather than allowing it to do the opposite)

Healthcare AI can add considerable value to patient care practices, but it can also “just add noise to an already complex system.”

Video of neurologist Lucy He, MD, with the Alaska Stoke Coalition, discussing how they use AI and CT to revolutionize stroke care in the state.

Revolutionizing stroke care in rural Alaska with AI and CT imaging

Lucy He, MD, a neurosurgeon with the Alaska Stroke Coalition, explains how the state created an AI-based alert system to streamline the timely imaging diagnosis and transfer of stroke patients across the largest and most rural U.S. state.

Predictive model helps identify malignancy in thyroid nodules.

Predictive model uses 6 ultrasound features to differentiate between benign and malignant thyroid nodules

Once the model is further validated, it could help guide providers in determining how to manage thyroid nodules.

Artificial Intelligence

AI, politics and rising demand for imaging—how rad techs are faring amid an evolving professional landscape

Newswatch: FDA’s AI plan ‘ahead of schedule and under budget,’ AI fears vs. comforts, AI ROI, more

5 attributes every AI system will soon need to make hay in healthcare

AMA brainstorms on key topics impacting healthcare

ChatGPT excels at assessing breast pain symptoms, deciding if patients require imaging