Reader's digest version of scientific writing: comparative evaluation of summarization capacity between large language models and medical students in analyzing scientific writing in sleep medicine.

Introduction: As artificial intelligence systems like large language models (LLM) and natural language processing advance, the need to evaluate their utility within medicine and medical education grows. As medical research publications continue to grow exponentially, AI systems offer valuable opportunities to condense and synthesize information, especially in underrepresented areas such as Sleep Medicine. The present study aims to compare summarization capacity between LLM generated summaries of sleep medicine research article abstracts, to summaries generated by Medical Student (humans) and to evaluate if the research content, and literary readability summarized is retained comparably.

A precision oncology-focused deep learning framework for personalized selection of cancer therapy.

Precision oncology matches tumors to targeted therapies based on the presence of actionable molecular alterations. However, most tumors lack actionable alterations, restricting treatment options to cytotoxic chemotherapies for which few data-driven prioritization strategies currently exist. Here, we report an integrated computational/experimental treatment selection approach applicable for both chemotherapies and targeted agents irrespective of actionable alterations.

Slab-Specific Projection-Resolved Optical Coherence Tomography Angiography for Enhancing En Face Polyp Detection in Polypoidal Choroidal Vasculopathy.

Purpose: A projection-resolved optical coherence tomography angiography (PR-OCTA) algorithm with slab-specific strategy was applied in polypoidal choroidal vasculopathy (PCV) to differentiate between polyp and branching vascular network (BVN) and improve polyp detection by en face OCTA.

Methods: Twenty-nine participants diagnosed with PCV by indocyanine green angiography (ICGA) and 30 participants diagnosed with typical neovascular age-related macular degeneration (nAMD) were enrolled. Polyps were classified into three categories after using the slab-specific PR algorithm.

Responsible research in health disparities using the Adolescent Brain Cognitive Development (ABCD) study.

Purpose: The Adolescent Brain Cognitive Development (ABCD) Study is the largest longitudinal study on brain development and adolescent health in the United States. The study includes a sociodemographically diverse cohort of nearly 12,000 youth born 2005-2009, with an open science model of making data rapidly available to the scientific community. The ABCD Study® data has been used in over 1100 peer-reviewed publications since its first data release in 2018.