AI Transparency: Why We Should Label Algorithms Like Food Products
October, 20 2021
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Mark Sendak; Christopher Roth; Suresh Balu
What if radiologists asked algorithm creators “Where can we find the product label and instruction manual, so that what’s inside the packa...
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The Seemingly Simple Task of Exploring FDA-Cleared Algorithms for Medical ImagingFebruary, 1 2021 | Sheela AgarwalIf you are thinking about purchasing an AI product, the ACR DSI catalog is a great place to start but you will still have some homework ahead of you.
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DSI’s AI Survey Shows ACR Tools in Sync with Member Needs for Deploying AIJune, 9 2021 | Bibb AllenWhile a modest amount of AI is currently in clinical practice, many members plan to purchase AI soon, making ACR’s support valuable for AI integ...
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Don't Be Afraid to Talk to Medical Students About AIMarch, 15 2021 | Bibb AllenMedical students today want to hear radiology’s value proposition in an AI-enabled era. We must be prepared to explain why radiology is a solid ...
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Build vs. Buy: The Conundrum of Radiology AIJanuary 13, 2022 | Daniel S. Chow, MD, MBA; Justin Glavis-Bloom, MDHow do you decide whether it’s better to build AI systems internally or to purchase applications from an external vendor?
Navigating Medical AI Deployment and Continuous EvaluationDecember 17, 2021 | Collin Howser, MD; Brent Savoie, MD, JDWhat lurks in the innovation “valley of death” to prevent your medical AI project from surviving?
AI-LAB Federated Learning to Expand AI Opportunities to More RadiologistsNovember 16, 2021 | Kendall Schmidt, PhDRadiologists can now get involved and develop expertise by registering their sites to take part in the ACR DSI AI federated learning program on AI-LAB...
Drifting Away: When Your A+ Decision-Making AI Machine Falls to Average … or WorseMay 12, 2021 | J. Raymond Geis, MDIt is up to you to be sure your AI model continues to work as expected — and the jury is still out on the best way to do it.
You've Purchased an AI Model. Now What?January 11, 2021 | Walter Wiggins, MD, PhDNow that AI is becoming more accessible, hearing from those who have put a program into place can provide valuable insights.
Democratizing AIDecember 15, 2020 | Dan Cohen-Addad, MDA multi-site federated learning approach to AI algorithm training can protect patient privacy and help make AI development more generalizable to wides...
How ACR Is Making AI Technology Accessible to RadiologistsNovember 17, 2020 | J. Raymond Geis, MDACR is building tools and infrastructure that are secure, user friendly, and radiologist focused.
Addressing Bias in AIAugust 18, 2020 | Monica J. Wood, MDA culture of fairness ensures protection for vulnerable or underrepresented populations.
9 Pillars of Clinical AI: Lessons Learned While Building an AI-Enabled Enterprise Radiology DepartmentJuly 14, 2020 | Panagiotis Korfiatis, PhD; Daniel Blezek, PhD; Sadia Choudhery, MDFollowing these best practices to plan and implement an AI strategy helped Mayo realize AI’s benefits.
How to Make Image-Labeling Projects SuccessfulMarch 21, 2020 | Prasanth Prasanna, MD; Arjun Sharma. MDIn this blog on implementing machine learning projects, we highlight the importance of planning your image-labeling process in advance and discuss con...
My Department's Experience with AIJuly 8, 2019 | Eric Weinberg, MDHow we found an AI technology partner, brought AI to our institution and chose our first project.
Four Barriers to Integrating AI into Radiology PracticeMarch 19, 2019 | Jeff Rudie, MD, PhDThese challenges adversely impact our progress with AI and prevent us from moving forward.
Initial Experiences of a Radiology Data Science TeamFebruary 19, 2019 | Michael Moore, MDBy setting realistic project expectations, even a small data science team can achieve measurable results with AI.
How to Share Big Data for Artificial IntelligenceSeptember 18, 2018 | Juan C. Batlle, MD, MBAAs one of the handful of companies partnering with IBM's artificial intelligence project (Watson), Baptist Health navigated several challenges to main...