Exploring the accuracy of embedded ChatGPT-4 and ChatGPT-4o in generating BI-RADS scores: a pilot study in radiologic clinical support.

This study evaluates the accuracy of ChatGPT-4 and ChatGPT-4o in generating Breast Imaging Reporting and Data System (BI-RADS) scores from radiographic images. We tested both models using 77 breast cancer images from radiopaedia.org, including mammograms and ultrasounds.

Racial, ethnic, and sex bias in large language model opioid recommendations for pain management.

Understanding how large language model (LLM) recommendations vary with patient race/ethnicity provides insight into how LLMs may counter or compound bias in opioid prescription. Forty real-world patient cases were sourced from the MIMIC-IV Note dataset with chief complaints of abdominal pain, back pain, headache, or musculoskeletal pain and amended to include all combinations of race/ethnicity and sex. Large language models were instructed to provide a subjective pain rating and comprehensive pain management recommendation.

Diagnostic Accuracy of a Custom Large Language Model on Rare Pediatric Disease Case Reports.

Accurately diagnosing rare pediatric diseases frequently represent a clinical challenge due to their complex and unusual clinical presentations. Here, we explore the capabilities of three large language models (LLMs), GPT-4, Gemini Pro, and a custom-built LLM (GPT-4 integrated with the Human Phenotype Ontology [GPT-4 HPO]), by evaluating their diagnostic performance on 61 rare pediatric disease case reports. The performance of the LLMs were assessed for accuracy in identifying specific diagnoses, listing the correct diagnosis among a differential list, and broad disease categories.

Pilot Study of Large Language Models as an Age-Appropriate Explanatory Tool for Chronic Pediatric Conditions.

There exists a gap in existing patient education resources for children with chronic conditions. This pilot study assesses large language models' (LLMs) capacity to deliver developmentally appropriate explanations of chronic conditions to pediatric patients. Two commonly used LLMs generated responses that accurately, appropriately, and effectively communicate complex medical information, making them a potentially valuable tool for enhancing patient understanding and engagement in clinical settings.

Transforming Health Care Landscapes: The Lever of Radiology Research and Innovation on Emerging Markets Poised for Aggressive Growth.

Advances in radiology are crucial not only to the future of the field but to medicine as a whole. Here, we present three emerging areas of medicine that are poised to change how health care is delivered-hospital at home, artificial intelligence, and precision medicine-and illustrate how advances in radiological tools and technologies are helping to fuel the growth of these markets in the United States and across the globe.

Temporary Declines and Demand Resurgence: Gender-Affirming Surgery Volume and Complication Trends During and After the COVID-19 Pandemic.

Background: The COVID-19 pandemic prompted surgical volume reductions due to lockdown measures. This study evaluates COVID-19's impact on gender-affirming surgery (GAS) volume and complications from the pandemic onset through the recovery period.

Methods: The 2019-2021 National Surgical Quality Improvement Program databases were queried for transgender or gender-diverse patients using ICD-10 codes.

Orthopedic Surgery Volume Trends During the COVID-19 Pandemic and Postvaccination Era: Implications for Healthcare Planning.

Background: The Coronavirus Disease 2019 (COVID-19) pandemic decreased surgical volumes, but prior studies have not investigated recovery through 2022, or analyzed specific procedures or cases of urgency within orthopedic surgery. The aims of this study were to (1) quantify the declines in orthopedic surgery volume during and after the pandemic peak, (2) characterize surgical volume recovery during the postvaccination period, and (3) characterize recovery in the 1-year postvaccine release period.

Methods: We conducted a retrospective cohort study of 27,476 orthopedic surgeries from January 2019 to December 2022 at one urban academic quaternary referral center.

Trends in non-focal neurological chief complaints and CT angiography utilization among adults in the emergency department.

Prudent imaging use is essential for cost reduction and efficient patient triage. Recent efforts have focused on head and neck CTA in patients with emergent concerns for non-focal neurological complaints, but have failed to demonstrate whether increases in utilization have resulted in better care. The objective of this study was to examine trends in head and neck CTA ordering and determine whether a correlation exists between imaging utilization and positivity rates.

A Novel Design-Thinking, Hospital Innovation Core Certificate Curriculum for Radiologists and Trainees: Creation, Implementation, and Multiyear Results.

Rationale And Objectives: Innovation is a crucial skill for physicians and researchers, yet traditional medical education does not provide instruction or experience to cultivate an innovative mindset. This study evaluates the effectiveness of a novel course implemented in an academic radiology department training program over a 5-year period designed to educate future radiologists on the fundamentals of medical innovation.

Materials And Methods: A pre- and post-course survey and examination were administered to residents who participated in the innovation course (MESH Core) from 2018 to 2022.

Preventing inadvertent drain removal using a novel catheter securement device.

Percutaneous drains have provided a minimally invasive way to treat a wide range of disorders from abscess drainage to enteral feeding solutions to treating hydronephrosis. These drains suffer from a high rate of dislodgement of up to 30% resulting in emergency room visits, repeat hospitalizations, and catheter repositioning/replacement procedures, which incur significant morbidity and mortality. Using ex vivo and in vivo models, a force body diagram was utilized to determine the forces experienced by a drainage catheter during dislodgement events, and the individual components which contribute to drainage catheter securement were empirically collected.

Assessing the Utility of ChatGPT Throughout the Entire Clinical Workflow: Development and Usability Study.

Background: Large language model (LLM)-based artificial intelligence chatbots direct the power of large training data sets toward successive, related tasks as opposed to single-ask tasks, for which artificial intelligence already achieves impressive performance. The capacity of LLMs to assist in the full scope of iterative clinical reasoning via successive prompting, in effect acting as artificial physicians, has not yet been evaluated.

Objective: This study aimed to evaluate ChatGPT's capacity for ongoing clinical decision support via its performance on standardized clinical vignettes.

Evaluating GPT as an Adjunct for Radiologic Decision Making: GPT-4 Versus GPT-3.5 in a Breast Imaging Pilot.

Objective: Despite rising popularity and performance, studies evaluating the use of large language models for clinical decision support are lacking. Here, we evaluate ChatGPT (Generative Pre-trained Transformer)-3.5 and GPT-4's (OpenAI, San Francisco, California) capacity for clinical decision support in radiology via the identification of appropriate imaging services for two important clinical presentations: breast cancer screening and breast pain.

Justice and Innovation in Radiology.

Imaging is a central determinant of health outcomes, and radiologic disparities can cascade throughout a patient's illness course. Innovative efforts in radiology are constant, but innovation that is driven by short-term profit-making incentives without explicit regard for principles of justice can lead to exclusion of the vulnerable from potential benefits and widening of inequities. Accordingly, we must consider the ways in which the field of radiology can shape innovative efforts to ensure that innovation ameliorates injustice instead of exacerbating it.

Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification?

Objectives: Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for smaller radiology report datasets.

Methods: We compared the performance of BioBERT, a deep-learning-based transformer model pre-trained on biomedical text, and three traditional machine-learning models (gradient boosted tree, random forest, and logistic regression) on seven classification tasks given free-text radiology reports.