Fundamentals of Data Governance – Lessons from UNC Health, Part 3
Artificial Intelligence in Healthcare: Best Practices and Success Stories Introduction In this episode of HIMSSCast, Greg Kuhnen, System Director for...
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A New Way to Create Realistic 3D Shapes Using Generative AI
Creating Realistic 3D Models Just Got Easier A New Technique for Generating 3D Shapes Creating realistic 3D models for applications...
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Former U.S. Cyber Commander urges healthcare to think differently about AI
The Future of Healthcare: Embracing AI and Cybersecurity Adapting to the Rapidly Evolving Landscape of Technology Change Former U.S. National...
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Want to design the car of the future? Here are 8,000 designs to get you started.
Revolutionizing Car Design with AI-Powered Aerodynamics Car design is an intricate and proprietary process. Carmakers can spend years on the...
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Citation tool offers a new approach to trustworthy AI-generated content
Chatbots: The Quest for Trustworthy Information The Problem Chatbots can wear many hats, from dictionary to therapist to poet to...
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John Scott Award
MIT's Daniela Rus Wins Prestigious John Scott Award for Robotics Research Award Celebrates Scientific Innovation and Human Potential Daniela Rus,...
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Athenahealth on its AI-Enabled Products and HIMSS25
The Future of Clinical Documentation: Athenahealth's Ambient Notes Introducing Ambient Notes In 2024, athenahealth introduced Ambient Notes, a revolutionary solution...
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Explaining AI Predictions in Plain Language
Unlocking the Secrets of AI Explanations Machine-Learning Models Can Make Mistakes Machine-learning models can make mistakes and be difficult to...
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Robotic helper making mistakes? Just nudge it in the right direction
Correcting Robot Behavior with Intuitive User Feedback Imagine you're using a robot to help with household chores, like cleaning the...
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Researchers reduce bias in AI models while preserving or improving accuracy
Machine Learning Models Can Fail When They Try to Make Predictions for Individuals Who Were Underrepresented in the Datasets They...
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