Tailoring glaucoma education using large language models: Addressing health disparities in patient comprehension.

This study evaluates the efficacy of GPT-4, a Large Language Model, in simplifying medical literature for enhancing patient comprehension in glaucoma care. GPT-4 was used to transform published abstracts from 3 glaucoma journals (n = 62) and patient education materials (Patient Educational Model [PEMs], n = 9) to a 5th-grade reading level. GPT-4 was also prompted to generate de novo educational outputs at 6 different education levels (5th Grade, 8th Grade, High School, Associate's, Bachelor's and Doctorate).

Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification: Recommendations for the Future.

Background: This study assesses the effectiveness of large language models (LLMs) in simplifying complex language within orthopaedic patient education materials (PEMs) and identifies predictive factors for successful text transformation.

Methods: We transformed 48 orthopaedic PEMs using GPT-4, GPT-3.5, Claude 2, and Llama 2.

Unraveling the complexity of HRD assessment in ovarian cancer by combining genomic and functional approaches: translational analyses of MITO16-MaNGO-OV-2 trial.

Background: Ovarian cancer (OvC) constitutes significant management challenges primarily due to its late-stage diagnosis and the development of resistance to chemotherapy. The standard treatment regimen typically includes carboplatin and paclitaxel, with the addition of poly (ADP-ribose) polymerase inhibitors for patients with high-grade serous ovarian cancer (HGSOC) harboring BRCA1/2 mutations. However, the variability in treatment responses suggests the need to investigate factors beyond BRCA1/2 mutations, such as DNA repair mechanisms and epigenetic alterations.

From the 100 Day Mission to 100 lines of software development: how to improve early outbreak analytics.

Since the COVID-19 pandemic, considerable advances have been made to improve epidemic preparedness by accelerating diagnostics, therapeutics, and vaccine development. However, we argue that it is crucial to make equivalent efforts in the field of outbreak analytics to help ensure reliable, evidence-based decision making. To explore the challenges and key priorities in the field of outbreak analytics, the Epiverse-TRACE initiative brought together a multidisciplinary group of experts, including field epidemiologists, data scientists, academics, and software engineers from public health institutions across multiple countries.