Medicolegal

Benchmarking pathology foundation models for non-neoplastic pathology in the placenta

Machine learning (ML) applications within diagnostic histopathology have been extremely successful. While many successful models have been built using general-purpose models trained largely on everyday objects, there is a recent trend toward pathology-specific foundation models, trained using histopathology images. Pathology foundation models show strong performance on cancer detection and subtypi...

Evaluating Genetic-Based Disease Prediction Approaches Through Simulation

Common diseases exhibit substantial heritability, and GWAS of these diseases have revealed hundreds of thousands of high-frequency disease susceptibility variants throughout the genome. These studies offer the prospect of using genomic data to improve disease prediction and diagnosis, however, the relative performance of different predictive modeling approaches is not well-characterized. To invest...

Real-World Evaluation of Large Language Models in Healthcare (RWE-LLM): A New Realm of AI Safety & Validation

The deployment of artificial intelligence (AI) in healthcare necessitates robust safety validation frameworks, particularly for systems directly inter...

Socio-Demographic Modifiers Shape Large Language Models’ Ethical Decisions

Large language models’ (LLMs) alignment with ethical standards is unclear. We tested whether LLMs shift medical ethical decisions when given socio-dem...

Evaluating Enhanced LLMs for Precise Mental Health Diagnosis from Clinical Notes

Anxiety, depression, and other mental health conditions are affecting millions of people worldwide each year. However, limited access to mental health...

Modeling the Spread of Misfolded Proteins in Alzheimer’s Disease using Higher-Order Simplicial Complex Contagion

Neurodegenerative diseases are characterized by complex proteins misfolded that propagate within the brain. For instance, current findings highlight t...

A Bilingual On-premise AI agent for Clinical Drafting: Seamless EHR integration in the Y-KNOT Project

Large Language Models (LLMs) have shown promise in reducing clinical documentation burden, yet their real-world implementation faces significant chall...

The gSOS Polygenic Score is Associated with Bone Density and Fracture Risk in Childhood

The polygenic risk score genetic quantitative ultrasound speed of sound (gSOS) was developed using machine learning algorithms in adults of European a...

Towards Inpatient Discharge Summary Automation via Large Language Models: A Multidimensional Evaluation with a HIPAA-Compliant Instance of GPT-4o and Clinical Expert Assessment

Large language models (LLMs) have demonstrated potential to automate clinical documentation tasks that may reduce clinician burden, such as generation...

Optimized BERT-based NLP outperforms Zero-Shot Methods for Automated Symptom Detection in Clinical Practice

Large Language Nodels (LLMs) have raised broad expectations for clinical use, particularly in the processing of complex medical narratives. However, i...

Extracting TNFi Switching Reasons and Trajectories From Real-World Data Using Large Language Models

Tumor necrosis factor inhibitors (TNFi) are widely used for auto-immune conditions. Despite their efficacy, many patients switch TNFis due to lack of ...

Resident physician perspectives on ambient AI scribing in academic family medicine

While ambient artificial intelligence (AI) scribes have been received positively by primary care physicians, the perceptions of resident physicians ar...

VADEr: Vision Transformer-Inspired Framework for Polygenic Risk Reveals Underlying Genetic Heterogeneity in Prostate Cancer

Polygenic risk scores (PRSs) serve as quantitative metrics of genetic liability for various conditions. Traditionally calculated as an effect size wei...

Changes in psychiatric documentation and treatment in primary care with artificial intelligence scribe use

Despite increasingly widespread use of artificial intelligence-driven ambient scribes in medicine, the extent to which they may impact clinician pract...

Artificial Intelligence in Outpatient Primary Care: A Scoping Review on Applications, Challenges, and Future Directions

Artificial intelligence (AI) has the potential to revolutionize clinical decision-making and significantly improve patient outcomes in outpatient prim...

AI-Driven Pharmacovigilance and Molecular Profiling of Fluoroquinolone-Associated Cardiotoxicity in the UAE: A Geospatial and Machine Learning Analysis with Structural Modification Strategies (2018-2023)

Fluoroquinolones, while clinically indispensable, carry underappreciated cardiovascular risks, particularly QT prolongation and life-threatening arrhy...

Towards Richer AI-Assisted Psychotherapy Note-Making and Performance Benchmarking

Psychotherapy note-making is crucial for effective patient care. However, traditional formats such as SOAP (Subjective, Objective, Assessment, and Pla...

Large Language Models in Real-World Clinical Workflows: A Systematic Review of Applications and Implementation

Large language models (LLMs) offer promise for enhancing clinical care by automating documentation, supporting decision-making, and improving communic...

Deep-Learning Based Contrast Boosting Improves Lesion Visualization and Image Quality: A Multi-Center Multi-Reader Study on Clinical Performance with Standard Contrast Enhanced MRI of Brain Tumors

Gadolinium-based Contrast Agents (GBCAs) are used in brain MRI exams to improve the visualization of pathology and improve the delineation of lesions....

Enhancing Privacy-Preserving Deployable Large Language Models for Perioperative Complication Detection: A Targeted Strategy with LoRA Fine-tuning

Perioperative complications represent a major global health concern affecting millions of surgical patients annually, yet manual detection methods suf...