Performance of large language models in the optical diagnosis of colorectal polyps
The episode discusses a paper testing five large language models for diagnosing colorectal polyps from optical images. Models performed well at distinguishing neoplastic from non-neoplastic polyps, but struggled with identifying invasive carcinoma and did not meet clinical standards. The authors suggest human-in-the-loop workflows and need better data before clinical use.