New AI Monitoring Method Helps Convey When to Trust AI Predictions and When to Exercise Caution
A new study from the Stanford Radiology AI Development and Evaluation (AIDE) Lab, published October 16 in npj Digital Medicine, illustrates how the Ensembled Monitoring Model (EMM) framework can act like a real-time second opinion system for deployed AI tools. EMM evaluates how much confidence can be placed in the the AI prediction, helping physicians decide whether to rely on the result or take a closer look.
Foundation Models in Radiology: What, How, Why, and Why Not?
In a new study published in Radiology, our team successfully leveraged large language models (LLMs) to automatically assess the completeness of clinical histories accompanying imaging orders.
Using LLMs to Improve Quality Assessments of Clinical Histories
In a new study published in Radiology, our team successfully leveraged large language models (LLMs) to automatically assess the completeness of clinical histories accompanying imaging orders.
AIDE Lab Awarded Stanford HAI Seed Research Grant to Improve Patient Experience with Radiology Reports using LLMs
The Stanford Institute for Human-Centered Artificial Intelligence (HAI) and the Center for Digital Health (CDH) recently awarded the AIDE lab a research grant to leverage large language models (LLMs) and develop an automated framework to generate and evaluate patient-friendly clinical reports.
RSNA Daily Bulletin highlights work on assessing completeness of clinical histories using LLMs
The Tuesday, December 3, 2024, issue of the RSNA Daily Bulletin highlighted the AIDE lab’s work in training open-source, large language models (LLMs) to assess the completeness of clinical histories that accompany imaging orders.
Stanford Medicine Magazine feature emphasizes need for robust AI evaluation and validation methods.
Stanford Medicine Magazine feature emphasizes need for robust AI evaluation and validation methods. August 30, 2024 The second issue of the Artificial Intelligence series of the Stanford Medicine Magazine focuses on co-director Akshay Chaudhari’s work around developing synthetic data. The article describes how synthetic data can supplement incomplete datasets for underrepresented demographics to help minimize […]