Medicolegal

From Keywords to Context: Bridging Expert Insight and Language Models for Multidimensional Sleep Health Classification in Clinical Notes

Accurate detection of multidimensional sleep health (MSH) information from electronic health records (EHRs) is critical for improving clinical decision-making but remains challenging due to sparse documentation and class imbalance. This study investigates whether integrating expert-guided annotations and keyword-based heuristics with large language models (LLMs) enhances the extraction of nuanced ...

Radiologist-AI workflow can be modified to reduce the risk of medical malpractice claims

Artificial Intelligence (AI) is rapidly changing the legal landscape of radiology. Results from a previous experiment suggested that providing AI error rates can reduce perceived radiologist culpability, as judged by mock jury members (4). The current study advances this work by examining whether the radiologist’s behavior also impacts perceptions of liability. Methods. Participants (n=282) read a...

RAGnosis: Retrieval-Augmented Generation for Enhanced Medical Decision Making

We present RAGnosis, a fully offline, retrieval-augmented framework for interpreting unstructured clinical text using open-weight large language model...

Urinary collagen peptides predict mortality

Organ fibrosis caused by the presence of excessive extracellular matrix (ECM) is strongly related to mortality. Urinary peptide signatures were report...

An LLM-Based Comparison of Ambient AI Scribes for Clinical Documentation

Ambient AI scribes have become an increasingly promising option for automating clinical documentation, with dozens of enterprise solutions available. ...

Integrating GWAS and Transcriptomic Data Using PrediXcan and Multimodal Deep Learning Reveals Genetic Basis and Drug Repositioning Opportunities for Alzheimer’s Disease

Alzheimer’s disease (AD), the leading cause of dementia, imposes a significant societal and economic burden; however, its complex molecular mechanisms...

Completeness and Quality of Neurology Referral Letters Generated by a Large Language Model for Standardized Scenarios

Large Language Models (LLMs) offer promising applications in healthcare, including drafting referral letters. However, access to LLMs specifically des...

Design and Implementation of an End-to-End AI-Driven Colonoscopy Recall Workflow at Scale

We present a real-world deployment of a large language model-powered colonoscopy recall pipeline that structured over 100,000 patient records during a...

Preserving Privacy, Increasing Accessibility, and Reducing Cost: An On-Device Artificial Intelligence Model for Medical Transcription and Note Generation

Clinical documentation represents a significant burden for healthcare providers, with physicians spending up to 2 hours daily on administrative tasks....

Priorities for AI Education: Clinicians’ Perspectives

Educating clinicians about Artificial Intelligence (AI) is an urgent need(1) as the UK General Medical Council (GMC) places liability with practitione...

A Randomized-Clinical Trial of Two Ambient Artificial Intelligence Scribes: Measuring Documentation Efficiency and Physician Burnout

Ambient artificial intelligence (AI) scribes record patient encounters and generate visit notes almost instantaneously, representing a promising solut...

Prematurity and Genetic Liability for Autism Spectrum Disorder

Autism Spectrum Disorder (ASD) is a neurodevelopmental condition characterized by diverse presentations and a strong genetic component. Environmental ...

Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care

Recent studies have found that stigmatizing terms can incline physicians to pursue punitive approaches to patient care. The intensive care unit (ICU) ...

Using discrete- and continuous-time machine learning models (Nnet, CoxNet, GLMnet) to explore sex and age differences in stroke prediction among hypertensive individuals

Stroke is one of the leading causes of death and long-term disability globally. Several studies have investigated the incidence and predictors of stro...

A Novel Playbook for Pragmatic Trial Operations to Monitor and Evaluate Ambient Artificial Intelligence in Clinical Practice

Ambient artificial intelligence (AI) offers the potential to reduce documentation burden and improve efficiency through clinical note generation. Wide...

Evaluating Large Language Models for Automatic Detection of In-Hospital Cardiac Arrest: Multi-Site Analysis of Clinical Notes

In-hospital cardiac arrest (IHCA) affects over 200,000 patients annually in the United States, yet its detection through manual chart review remains r...

Large Language Models Improve Cancer Survival Prediction Using Real-World Clinical Notes

In medical documentation, vast amounts of unstructured text are generated that are still underutilized in current prognostic models. We investigate th...

Responsible AI in Action: Planning through Implementation of a Mortality Model for Palliative Care

Interest in the use of prediction models to support referrals to palliative care is surging. Few high-performing models have been developed, implement...

Diagnostic Codes in AI prediction models and Label Leakage of Same-admission Clinical Outcomes

Artificial intelligence (AI) and statistical models designed to predict same-admission outcomes for hospitalized patients, such inpatient mortality, o...

A Case Study on Colposcopy-Based Cervical Cancer Staging Reveals an Alarming Lack of Data Sharing Hindering the Adoption of Machine Learning in Clinical Practice

The inbuilt ability to adapt existing models to new applications has been one of the key drivers of the success of deep learning models. Thereby, shar...