Quality, Accuracy, and Bias in ChatGPT-Based Summarization of Medical Abstracts.

Purpose: Worldwide clinical knowledge is expanding rapidly, but physicians have sparse time to review scientific literature. Large language models (eg, Chat Generative Pretrained Transformer [ChatGPT]), might help summarize and prioritize research articles to review. However, large language models sometimes "hallucinate" incorrect information.

Assessing the Immunization Information System and electronic health record interface accuracy for COVID-19 vaccinations.

Objective: Our objective is to assess the accuracy of the COVID-19 vaccination status within the electronic health record (EHR) for a panel of patients in a primary care practice when manual queries of the state immunization databases are required to access outside immunization records.

Materials And Methods: This study evaluated COVID-19 vaccination status of adult primary care patients within a university-based health system EHR by manually querying the Kansas and Missouri Immunization Information Systems.

Results: A manual query of the local Immunization Information Systems for 4114 adult patients with "unknown" vaccination status showed 44% of the patients were previously vaccinated.

Investigating Visual Crowding of Objects in Complex Real-World Scenes.

Visual crowding, the impairment of object recognition in peripheral vision due to flanking objects, has generally been studied using simple stimuli on blank backgrounds. While crowding is widely assumed to occur in natural scenes, it has not been shown rigorously yet. Given that scene contexts can facilitate object recognition, crowding effects may be dampened in real-world scenes.