Artificial Intelligence Medical Compendium

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

Showing 12,991 to 13,000 of 210,785 articles

Generating Human-Readable Labels for SNOMED CT Expressions with LLMs: A Study on Model Performance and Rater Subjectivity.

Studies in health technology and informatics
Post-coordinated SNOMED CT expressions lack human-readable labels, hindering their use in analytics and representation learning. To address this issue, we evaluated labels for 100 SNOMED CT expressions generated by a large language model (LLM), which... read more 

THERA-IE: An AI-Enabled System for Therapeutic Indication Identification and Extraction from Biomedical Literature.

Studies in health technology and informatics
Drug-indication knowledge underpins evidence-based prescribing but remains challenging to maintain manually. Existing resources incompletely represent off-label uses that may be captured within artificial intelligence models or described in the biome... read more 

Auto Ontology: Towards Automated Term-to-Concept Assignment in Microbiology Analytics.

Studies in health technology and informatics
Maintaining clinical ontologies requires continuous addition of new synonyms from laboratory information systems, which is currently a manual and time-consuming process. This study evaluates whether text embedding models can support semi-automated sy... read more 

Interpreting ECG Images with Multimodal Large Language Models.

Studies in health technology and informatics
Cardiac diseases are one of the leading causes of death worldwide. Electrocardiography (ECG) is one of the major diagnostic methods to detect cardiac diseases, including historical and acute myocardial infarction and arrhythmias. Recent advancements ... read more 

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