Large language models (LLMs) demonstrate significant potential in healthcare applications, but clinical deployment is limited by privacy concerns and insufficient medical domain training. This study investigated whether retrieval-augmented generation (RAG) can improve locally deployable…
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Self-confessed “100% health nerd” Dr Jessica Morley is a leading voice on ethics in digital healthcare and one of the most exciting speakers on the health tech conference circuit.
Based in the US, where…
Large language models like ChatGPT are finding their way into healthcare, business, and everyday life. The study of mechanistic interpretability may help us create safer, more trustworthy algorithms by “looking under the hood.”
By Paul…
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AI isn’t a magic fix. It’s a tool that demands training, understanding and human oversight.In this video, HIMSS members Chris Ahn and Edwin Lau explain why healthcare leaders must be equipped to monitor the…
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Welcome to our exclusive digital health interview with Jennifer Owens, Senior AI Program Administrator at Cleveland Clinic. In this in-depth conversation, Jennifer opens up about her personal journey, detailing her background and how she…
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1. What Is Retrieval-Augmented Generation?
RAG is a way to leverage already-trained large language models such as GPT-4, Gemini, Bard and Llama when building applications powered by artificial intelligence. By adding local knowledge (such as…
Foundation models with the ability to process and generate multi-modal data have transformed AI’s role in medicine. Nevertheless, researchers discovered that a major limitation of their reliability is hallucinations, where inaccurate or fabricated information…
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Large language models (LLMs) can answer expert-level questions in medicine but are prone to hallucinations and arithmetic errors. Early evidence suggests LLMs cannot reliably perform clinical calculations, limiting their potential integration into clinical workflows….
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By Alan Payne, Group Product and Engineering Director, The Access GroupAs we know, AI is transforming health and care. It presents a significant opportunity to streamline decision-making, improve data analysis and insights, introduce more…
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Red teaming, the practice of adversarially exposing unexpected or undesired model behaviors, is critical towards improving equity and accuracy of large language models, but non-model creator-affiliated red teaming is scant in healthcare. We convened…
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