Of all of the areas where artificial intelligence is expected to influence healthcare, imaging is one of the largest. This is due to the dependence on visual examination, demand for increased procedures and error / cost reduction, and the development of specific computing tools that make AI-guided decisions far more practical.
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Table of Contents
Untitled Chapter
Artificial Intelligence
AI Modality Analysis
Factors Affecting Future Outlook
Top-ranked Potential Uses for AI Applications
Current Trends
Investing in IT Interoperability Can Extinguish Radiologist Burnout
Imaging services hand-off
Radiologists Boost AI Severity Quantification of COVID-19
Can AI Help Detect Cancer on CT Lung Cancer Screening Exams?
Can AI Help Screen for Early Interstitial Lung Disease On X-Rays?
How Technology Can Drive Better Job Satisfaction in Radiology
How technology contributed to the problem
How technology can help solve the problem
Can AI Help Screen For Early Interstitial Lung Disease On X-Rays?
AI Cuts PET Scan Time In Alzheimer's Disease
What's the Best Way To Implement AI in a Community Hospital?
FDA Faces Research Hurdles in Regulating AI for Imaging
Research gaps
New assessment paradigms
Computer-aided triage
Adaptive algorithms
Strength in Numbers: Federated Learning Boosts AI for COVID-19
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Kalorama Information’s latest edition of our in vitro diagnostic market report represents the fourteenth time in two decades our analysts have fully assessed the in vitro diagnostic market from scratch and re-report numbers. Last year,…
