Thank you RSNA for featuring our study on LLMs in pancreatic cancer in the Daily Bulletin, including our prelim resectability tool that automatically applies NCCN criteria on reports. University Medical Imaging Toronto (UMIT) UofT Medical Imaging Satheesh Krishna #RSNA2023 dailybulletin.rsna.org/db23/index.cfm…

Thank you, AJR! Reported incidental imaging findings are commonly mismanaged. LLMs can accurately identify reported clinically important incidentals -- like adrenal nodules, pancreatic cysts. As LLMs become incorporated into EMR software, our findings suggest they

New AJR Accepted Manuscript: "Use of GPT-4 With Single-Shot Learning to Identify Incidental Findings in Radiology Reports" By Dr Rajesh Bhayana & team UofT Medical Imaging ajronline.org/doi/10.2214/AJ…

Large language models demonstrate human-level performance in medicine & radiology.​ Multimodal LLMs can interpret data from text and images at the same time, mirroring how humans process & learn from real-world information.​ Rajesh Bhayana bit.ly/3SjAzLh

Today, we're announcing Claude 3, our next generation of AI models. The three state-of-the-art models—Claude 3 Opus, Claude 3 Sonnet, and Claude 3 Haiku—set new industry benchmarks across reasoning, math, coding, multilingual understanding, and vision.

Honored to be part of this AJR Expert Panel alongside renowned LLM experts: Paul Yi Woojin Kim Judy Gichoya Felipe Kitamura Tessa S. Cook MD PhD CIIP FSIIM FCPP Som Biswas, MD(Rad), MS, MBA! Our Narrative Review helps radiologists navigate the complex landscape of LLMs in radiology. Available products are promising to

Outpatient imaging report turnaround times can matter... Shown here: splenic infarct and LV thrombus on a single image.

Default GPT-3.5 and GPT-4 were accurate at answering radiology board multiple-choice questions but had poor repeatability and robustness and were frequently overconfident, limiting usability without domain-specific optimization. bit.ly/3wJrEdZ

We are excited to host Dr. Rajesh Bhayana Rajesh Bhayana at our monthly NAIMS-AIMS working group meeting on June 12 (next Monday)! Dr. Bhayana will discuss "Large Language Models: Applications in Radiology" Zoom link: buffalo.zoom.us/j/91717765112

Large Language Models for Automated Synoptic Reports and Resectability Categorization in Pancreatic Cancer | Radiology pubs.rsna.org/doi/10.1148/ra…

🧵 Structured radiology reports for pancreatic ductal adenocarcinoma (PDAC) helps surgical planning, yet it is not universally used by Rads. Can large language models (LLMs) help? A new study from Radiology explored this further #RadInTraining #TWEETORIAL Rad In Training Editor

Large #Language #Models for Automated #Synoptic Reports and #Resectability Categorization in #PancreaticCancer | Radiology pubs.rsna.org/doi/10.1148/ra…

GPT-4 enabled near-perfect creation of pancreatic ductal adenocarcinoma synoptic reports from original reports and accurate resectability categorization using chain-of-thought prompting. University Medical Imaging Toronto (UMIT) Rajesh Bhayana Satheesh Krishna bit.ly/4cqQ8I2

3/ On the other hand, such criticism of the limited evaluations of LLMs in the radiology AI literature is valid. This Radiology paper by Rajesh Bhayana et al. shows the limitations of simple prompts and how one can improve LLM performance in complex medical tasks.

Doctors please remember For you it's just another appointment. Just another rounds visit. But for patients those 10 minutes are the most important visit of their day. Sometimes of their lives.