Artificial Intelligence Medical Compendium

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

Showing 13,641 to 13,650 of 211,153 articles

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

Studies in health technology and informatics May 21, 2026
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 May 21, 2026
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 May 21, 2026
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 May 21, 2026
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 May 21, 2026
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 

Evaluating Large Language Models for Extracting Social Determinants of Health in Substance Use Disorder Notes.

Studies in health technology and informatics May 21, 2026
Automated extraction of social determinants of health (SDoH) may support earlier identification of unmet social needs and inform substance use disorder (SUD) care. This study provides preliminary evidence that few-shot prompting improves large langua... read more 

Evaluation over Generalist Large Language Models and Specialised Models for Clinical Risk Prediction.

Studies in health technology and informatics May 21, 2026
Large language models (LLMs) show promise in clinical applications but face accuracy limitations in disease risk prediction, especially for rare conditions. We evaluated ChatGPT and DeepSeek against task-specific models for cholangiocarcinoma (rare) ... read more 

From Discharge Letters to Process Traces with LLMs: A Human-in-the-Loop Pipeline.

Studies in health technology and informatics May 21, 2026
We present a modular human-in-the-loop pipeline that converts unstructured discharge letters into process-mining-ready event traces. The pipeline uses task-specific Large Language Model prompting to extract temporally ordered clinical events, followe... read more 

Quantifying Fidelity and Utility in Synthetic Healthcare Data.

Studies in health technology and informatics May 21, 2026
The scarcity of high-quality clinical datasets and strict privacy regulations remain major barriers to developing robust predictive models in healthcare. Generative models offer a promising solution by producing synthetic data (SD) that replicate rea... read more 

CUI-Curate: A Framework for Automated Clinical Concept Selection from the Unified Medical Language System.

Studies in health technology and informatics May 21, 2026
INTRODUCTION: Manual construction of UMLS concept sets is time-consuming and inconsistent across users. METHODS: CUI-Curate, a GPT-5 and graph-based retrieval framework, was developed to automate clinical concept set generation from UMLS source vocab... read more 
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