Early in a career as a system leader you are asked to make decisions that seem like a 50/50 bet at best. I was in the process of crafting a corporate imaging data retention guideline for a 19-hospitals system. The question was: “Do we save the CAD overlay box on mammogram studies or not?” I said, “No, let the radiologists see them in the moment but delete them from permanent storage.” What would you have done?
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I’m always on the lookout for opportunities to help you elevate your expertise and connect with the brightest minds in our field. Mark your calendars because SIIM 2025 in Portland, Oregon, from June 11-14, is shaping up to be an unmissable event!
We’ve all had those awkward conversations. The ones where you’re not sure what to say, or the other person seems completely lost. Now, imagine having that same frustrating experience with an AI. That’s where prompt engineering comes in. It’s the art of crafting effective prompts that guide AI models to produce the results you need.
The field of radiology, crucial for diagnosing and treating a vast range of medical conditions, faces a growing challenge: a significant and projected shortage of radiologists. This scarcity threatens patient care, increases workloads for existing professionals, and potentially delays critical diagnoses.
This may be the right time to apply AI in your healthcare organization. You could be faced with care coordination challenges unlike any other. A new payment structure calls for a new AI-driven workflow.
For decades, radiologists were at the forefront of cardiac imaging, pioneering techniques and collaborating with cardiologists, physicists and engineers to develop modalities such as radiography, echocardiography and coronary arteriography.
This article delves into the potential of AI in AAA rupture forecasting, exploring how biomechanical and structural analysis can augment the predictive capabilities beyond maximum diameter.
Large language models (LLM) have the potential to transform health care. But when will this transformation occur? In my experience working in a large health care organization, there’s a clear caution against the use of LLMs for tasks like translation: “Never use AI for translation!”
All hospitals have falls. I will cover what an effective fall prevention program should take into consideration and how AI could be applied to provide assessments and alerts before the head hits the floor.
This is a well-understood experience of interacting with the U.S. health care system. We know that 20 percent of the cost of health care in the U.S. is related to administration. This is an opportunity area we should focus on when we brainstorm on artificial intelligence’s role in health care.
