Large language models rely on complex technology, but a plain English tutorial makes it clear that they use math, not magic to render their impressive results.
“Filter Bubbles”: The Quest to Prevent Algorithms from Running Amok
It’s not just social media sites that reinforce users’ prejudices and belief systems by feeding them stories catering to their viewpoints; healthcare algorithms can also create “echo chambers,” according to Mayo Clinic Platform researchers, who examine the issue in this piece. Fortunately, there are alternatives.
Addressing “Shortcomings”: How AI Can Help Predict the Onset of Sepsis
“Like Herding Cats”: Making a Case for AI Assurance Labs
As AI continues to build momentum, a group of health systems, government agencies and tech companies are banding together to develop “a set of guidelines for safe, secure and trustworthy AI algorithms,” according to John Halamka, MD. In this blog, he outlines the five main objectives of the network.
Is the Future of AI in Good Hands?
“A Profound Impact”: How NLP Fits into the Digital Health Ecosystem
“A Step in the Right Direction”: How Prompt Engineering Can Improve GenAI
Generative AI Benefits: We’ve Barely Scratched the Surface
“Data Behind Glass”: Exploring a Federated Model for Data Management
“Untapped Power”: The Critical Role Digital Tools Can Play in Addressing Social Determinants
Addressing health inequities is going to require systemic changes in national policies. In the meantime, however, “there are measures that clinicians and technologists can implement that will have an impact,” according to John Halamka, MD, who discusses how Mayo Clinic is working to create AI platforms and algorithms to change outcomes.