Henry Ford said, “If I had asked people what they wanted, they would have said faster horses.” This sums up the current understanding of artificial intelligence in imaging, a customer can easily describe a problem they’re having — in this case, wanting to get somewhere faster — but not the best solution.
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Human-centered artificial intelligence deployment takes into consideration the system design to bring ambient intelligence to a project.
Stanford University has opened a Center for Human-Centered Artificial Intelligence. I think this is a testament that one of the…
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Researchers are training an artificial intelligence (AI) system that can potentially diagnose dementia after a single brain scan.
Adhering to the radiologist follow-up recommendations is a long-standing problem that impacts outcomes, liabilities and revenue. Unfortunately, less than 40% of the referring providers adhere to the radiologist recommendations around incidental findings.
For radiologists it is difficult to appreciate all that is visible. When they are looking for one abnormality they can completely miss another. Researchers call this phenomenon “inattentional blindness.”
Recent breakthroughs in artificial intelligence (AI) and machine learning are enabling doctors to see and also predict previously unidentified patterns within medical and biological data that can inform individualized disease prevention and care. It can also be used for biomedical discovery.
AI and machine learning algorithms will need to be monitored and the risk of “drift” must be acknowledged to promote trust and confidence that 10×10 will equal 100!
Useful, adaptive technology, innovative change management processes, value added in stream workflow will lead to a breakdown of the current antiquated power structures and unleash an AI imaging revolution.
