Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 14,241 to 14,250 of 211,815 articles

Evaluating Large Language Models for Extracting Clinical Recommendations from Practice Guidelines: A Preliminary Study.

Studies in health technology and informatics
CPGs (Clinical Practice Guidelines) contain the best care practices for clinicians to use and are created in many formats. The development of LLMs (Large Language Models) has led to their use in extracting or adapting CPG content to improve the ease ... read more 

Build and Query Indexes of Clinical Documents with Easy-to-Reuse Pipelines.

Studies in health technology and informatics
Electronic Health Records are a central source of healthcare data, containing structured data alongside unstructured clinical texts. The latter capture detailed reasoning, observations, treatment plans and clinical evolutions, which are crucial for p... read more 

Exploring a Large Language Model-Based Chatbot Use in Data Analysis: A Case Study of the Problems Related to the Do Not Attempt Resuscitation Order.

Studies in health technology and informatics
The interpretative framework was used to explore how healthcare professionals (HCPs), artificial intelligence (AI), and researchers construct and negotiate meaning, trust, and authority in critical, highly ethical medical contexts. This case study ai... read more 

Interpretable Feature Extraction from Clinical Notes for Sepsis Prediction: Comparing Rule-Based, LLM, and Hybrid Approaches.

Studies in health technology and informatics
Embedding-based approaches integrate clinical notes into sepsis prediction models but produce uninterpretable representations, obscuring which clinical findings drive predictions, and limiting both trust and regulatory acceptance. We compared three f... read more 

Evidence-Grounded LLM Validation of MIMIC-IV ICD Labels.

Studies in health technology and informatics
Automatically assigning ICD-10 diagnosis codes from discharge summaries is a central multi-label task in clinical NLP, yet widely used benchmarks such as MIMIC contain substantial label noise: many charted codes are not text-grounded in the note or a... read more 

Fine-Grained Mention-Level Analysis of Biomedical Entity Linking Models.

Studies in health technology and informatics
Biomedical Entity Linking (BEL) is essential for structuring knowledge from biomedical texts, yet global evaluation metrics often obscure systematic model weaknesses. We propose a fine-grained evaluation framework that analyzes performance across int... read more 

Advancing Pediatric Rehabilitation Documentation via Neuro-Symbolic AI.

Studies in health technology and informatics
For automated documentation systems to be meaningful in pediatric rehabilitation, they must accurately capture and summarize information about a child's or youth's involvement in daily life activities. We present attention-ASP, a neuro-symbolic frame... read more 

Can NLP Detect Loneliness in Electronic Health Records? A Proof-of-Concept Study.

Studies in health technology and informatics
Loneliness is clinically important but under-documented in electronic health records (EHRs), posing challenges for secondary use and computational phenotyping. This study evaluated whether natural language processing (NLP) methods can detect and clas... read more 

Evaluating Reasoning Effect for LLMs: Prompt Sensitivity and Text-Image Based Performance in Musculoskeletal Radiology.

Studies in health technology and informatics
Multimodal large language models (LLMs) are increasingly applied in radiology, but the effect of reasoning capabilities across text- and image-based tasks remains unclear. We evaluated four multimodal LLMs-two non-reasoning (ChatGPT-4, Gemini 1.5 Pro... read more 

Resource-Conscious Modeling for Next-Day Discharge Prediction Using Clinical Notes.

Studies in health technology and informatics
Timely discharge prediction is important for surgical unit operations. Using 3,928 postoperative patient notes (32.2% positive), we compared TF-IDF models, sentence embeddings, and LoRA-fine-tuned small language models (SLMs). TF-IDF with LightGBM pe... read more