Development, optimization, and preliminary evaluation of a novel artificial intelligence tool to promote patient health literacy in radiology reports: The Rads-Lit tool.

Radiology reports are an integral part of patient medical records; however, these reports often contain complex medical terminology that are difficult for patients to comprehend, potentially leading to anxiety, misunderstanding, and misinterpretation. The development of user-friendly instruments to improve understanding is thus critically important to enhance health literacy and empower patients. In this study, we introduce a novel artificial intelligence (AI) interface, the Rads-Lit Tool, which can simplify radiology reports for patients using natural language processing (NLP) techniques.

Food Insecurity Prevalence Among US Medical Students.

Importance: Food insecurity affects nearly 13.5% of US households and has worsened after the COVID-19 pandemic. However, the prevalence, associated factors, and impact on medical students is unclear, hindering targeted interventions.

Educational Debt Among US Medical Graduates Entering Radiology.

Objective: The aim of this study was to identify trends in medical educational debt for U.S. medical school graduates pursuing radiology and examine associations with socioeconomic factors.

Weight bias internalization, weight controllability beliefs, and probable eating disorder associations among medical students at a private university in the US.

Objective: To explore the association between weight bias measures and probable eating disorders screening among medical students, as they endure the stress of medical school as well as prepare to become future healthcare physicians.

Methods: Medical students (n = 378) completed a web-based survey assessing weight bias internalization (Modified Weight Bias Internalization Scale [WBIS-M]), beliefs about weight controllability (Beliefs About Obese Persons [BAOP]) scale, and probable eating disorder symptoms (SCOFF questionnaire). Multivariable logistic and linear models were used to examine the cross-sectional associations between weight bias measures and a positive probable eating disorder screening (SCOFF ≥ 2), adjusting for gender identity.

The Intersection of Disability, Race, Ethnicity, and Financial Background on Food Insecurity Among Medical Students.

Purpose: Food insecurity is negatively associated with college students' well-being and academic performance. Little is known about the prevalence of food insecurity among medical students. This study examined variations in food insecurity among medical students at 15 schools, analyzing differences by disability status, race, ethnicity, and financial background.

Combating Chronic Disease with Barbershop Health Interventions: A Review of Current Knowledge and Potential for Big Data.

Community-based participatory research (CBPR) using barbershop interventions is an emerging approach to address health disparities and promote health equity. Barbershops serve as trusted community settings for health education, screening services, and referrals. This narrative mini-review provides an overview of the current state of knowledge regarding CBPR employing barbershop interventions and explores the potential for big data involvement to enhance the impact and reach of this approach in combating chronic disease.

Long COVID Characteristics and Experience: A Descriptive Study From the Yale LISTEN Research Cohort.

Background: The experience of people with long COVID needs further amplification, especially with a comprehensive focus on symptomatology, treatments, and the impact on daily life and finances. Our intent is to describe the experience of people with long COVID symptomatology and characterize the psychological, social, and financial challenges they experience.

Methods: We collected data from individuals aged 18 and older reporting long COVID as participants in the Yale Listen to Immune, Symptom and Treatment Experiences Now study.

Assessing the Efficacy of Large Language Models in Health Literacy: A Comprehensive Cross-Sectional Study.

Enhanced health literacy in children has been empirically linked to better health outcomes over the long term; however, few interventions have been shown to improve health literacy. In this context, we investigate whether large language models (LLMs) can serve as a medium to improve health literacy in children. We tested pediatric conditions using 26 different prompts in ChatGPT-3.

Quantitative Evaluation of Large Language Models to Streamline Radiology Report Impressions: A Multimodal Retrospective Analysis.

Background The complex medical terminology of radiology reports may cause confusion or anxiety for patients, especially given increased access to electronic health records. Large language models (LLMs) can potentially simplify radiology report readability. Purpose To compare the performance of four publicly available LLMs (ChatGPT-3.

Post-Vaccination Syndrome: A Descriptive Analysis of Reported Symptoms and Patient Experiences After Covid-19 Immunization.

Introduction: A chronic post-vaccination syndrome (PVS) after covid-19 vaccination has been reported but has yet to be well characterized.

Methods: We included 241 individuals aged 18 and older who self-reported PVS after covid-19 vaccination and who joined the online Yale Listen to Immune, Symptom and Treatment Experiences Now (LISTEN) Study from May 2022 to July 2023. We summarized their demographics, health status, symptoms, treatments tried, and overall experience.

Artificial Intelligence to Improve Patient Understanding of Radiology Reports.

Diagnostic imaging reports are generally written with a target audience of other providers. As a result, the reports are written with medical jargon and technical detail to ensure accurate communication. With implementation of the 21st Century Cures Act, patients have greater and quicker access to their imaging reports, but these reports are still written above the comprehension level of the average patient.

Genomic Disorders in CKD across the Lifespan.

Significance Statement: Pathogenic structural genetic variants, also known as genomic disorders, have been associated with pediatric CKD. This study extends those results across the lifespan, with genomic disorders enriched in both pediatric and adult patients compared with controls. In the Chronic Renal Insufficiency Cohort study, genomic disorders were also associated with lower serum Mg, lower educational performance, and a higher risk of death.

Evaluation of ForenSeq™ Signature Prep Kit B on predicting eye and hair coloration as well as biogeographical ancestry by using Universal Analysis Software (UAS) and available web-tools.

This study examined 266 individuals from various populations including African American, East Asian, South Asian, European, and mixed populations to evaluate the ForenSeq™ Signature Prep Kit Primer Mix B. Focus was placed on phenotypic and biogeographical ancestry predictions by Illumina's Universal Analysis Software (UAS). These outcomes were compared to those obtained through web-tools developed at the Erasmus Medical Center (EMC) and available from the Forensic Resource/Reference on Genetics-knowledge base (FROG-kb), as well as to eye color predictions by the 8-plex system.