Harnessing Large Language Models for Structured Reporting in Breast Ultrasound: A Comparative Study of Open AI (GPT-4.0) and Microsoft Bing (GPT-4).

Objectives: To assess the capabilities of large language models (LLMs), including Open AI (GPT-4.0) and Microsoft Bing (GPT-4), in generating structured reports, the Breast Imaging Reporting and Data System (BI-RADS) categories, and management recommendations from free-text breast ultrasound reports.

Materials And Methods: In this retrospective study, 100 free-text breast ultrasound reports from patients who underwent surgery between January and May 2023 were gathered.

An interpretable two-branch bi-coordinate network based on multi-grained domain knowledge for classification of thyroid nodules in ultrasound images.

Computer-aided diagnosis (CAD) for thyroid nodules has been studied for years, yet there are still reliability and interpretability challenges due to the lack of clinically-relevant evidence. To address this issue, inspired by Thyroid Imaging Reporting and Data System (TI-RADS), we propose a novel interpretable two-branch bi-coordinate network based on multi-grained domain knowledge. First, we transform the two types of domain knowledge provided by TI-RADS, namely region-based and boundary-based knowledge, into labels at multi-grained levels: coarse-grained classification labels, and fine-grained region segmentation masks and boundary localization vectors.

Transforming free-text radiology reports into structured reports using ChatGPT: A study on thyroid ultrasonography.

Purpose: The importance of structured radiology reports has been fully recognized, as they facilitate efficient data extraction and promote collaboration among healthcare professionals. Our purpose is to assess the accuracy and reproducibility of ChatGPT, a large language model, in generating structured thyroid ultrasound reports.

Methods: This is a retrospective study that includes 184 nodules in 136 thyroid ultrasound reports from 136 patients.

Performance of large language models on benign prostatic hyperplasia frequently asked questions.

Background: Benign prostatic hyperplasia (BPH) is a common condition, yet it is challenging for the average BPH patient to find credible and accurate information about BPH. Our goal is to evaluate and compare the accuracy and reproducibility of large language models (LLMs), including ChatGPT-3.5, ChatGPT-4, and the New Bing Chat in responding to a BPH frequently asked questions (FAQs) questionnaire.

Long-term follow-up ultrasonography surveillance in a large cohort of patients with papillary thyroid carcinoma.

Objective: This study aimed to investigate the predictive factors as well as the time and age course of recurrence/persistence in a large cohort of postoperative patients with papillary thyroid carcinoma (PTC) based on the long-term ultrasonography (US) follow-up data.

Methods: Between January 2007 and December 2016, 3106 patients underwent surgery for PTC and at least two postoperative US follow-up examination over more than three years. Tumor recurrence/persistence was confirmed based on the follow-up US data and histopathological results.

Using Deep Neural Network to Diagnose Thyroid Nodules on Ultrasound in Patients With Hashimoto's Thyroiditis.

Objective: The aim of this study is to develop a model using Deep Neural Network (DNN) to diagnose thyroid nodules in patients with Hashimoto's Thyroiditis.

Methods: In this retrospective study, we included 2,932 patients with thyroid nodules who underwent thyroid ultrasonogram in our hospital from January 2017 to August 2019. 80% of them were included as training set and 20% as test set.

In-situ reaction for recycling indium from waste liquid crystal display panels by vaccum reduction with pyrolytic carbon as reductant.

In the past, the pretreatment and indium extracting were conducted in independent disposal system to recycle indium from waste liquid crystal display (LCD), which make the recycling process inefficient and costly. In this study, an efficient and environmental friendly indium recycling process was proposed by an in-situ reaction process. The carbon residue generated in the pretreatment stage (organic removing stage) was used as the reductant to extract indium in the same reaction system.

[Astragali Radix and Hedysari Radix molecular identification of SSR primers screening and fingerprints code].

Leguminous related SSR primers were collected, core primers used for Astragali Radix and Hedysari Radix identification were screened and validated by using molecular marker techniques. 6 core primers were selected from 101 pairs of primers, the molecular weight of PCR products was 100-500 bp, which formed 7-12 electrophoresis bands with 55 amplified loci. The percentage of polymorphic loci was 100%, and the average polymorphism information content was 0.