AI Medical Compendium Journal:
Journal of the American Medical Informatics Association : JAMIA

Showing 61 to 70 of 493 articles

Mitigation of outcome conflation in predicting patient outcomes using electronic health records.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Artificial intelligence (AI) models utilizing electronic health record data for disease prediction can enhance risk stratification but may lack specificity, which is crucial for reducing the economic and psychological burdens associated w...

Development and validation of a multi-stage self-supervised learning model for optical coherence tomography image classification.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: This study aimed to develop a novel multi-stage self-supervised learning model tailored for the accurate classification of optical coherence tomography (OCT) images in ophthalmology reducing reliance on costly labeled datasets while mainta...

Expectations of healthcare AI and the role of trust: understanding patient views on how AI will impact cost, access, and patient-provider relationships.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Although efforts to effectively govern AI continue to develop, relatively little work has been done to systematically measure and include patient perspectives or expectations of AI in governance. This analysis is designed to understand pa...

MedBot vs RealDoc: efficacy of large language modeling in physician-patient communication for rare diseases.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: This study assesses the abilities of 2 large language models (LLMs), GPT-4 and BioMistral 7B, in responding to patient queries, particularly concerning rare diseases, and compares their performance with that of physicians.

Application of unified health large language model evaluation framework to In-Basket message replies: bridging qualitative and quantitative assessments.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Large language models (LLMs) are increasingly utilized in healthcare, transforming medical practice through advanced language processing capabilities. However, the evaluation of LLMs predominantly relies on human qualitative assessment, w...

Enhancing systematic literature reviews with generative artificial intelligence: development, applications, and performance evaluation.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: We developed and validated a large language model (LLM)-assisted system for conducting systematic literature reviews in health technology assessment (HTA) submissions.

Deciphering genomic codes using advanced natural language processing techniques: a scoping review.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: The vast and complex nature of human genomic sequencing data presents challenges for effective analysis. This review aims to investigate the application of natural language processing (NLP) techniques, particularly large language models (...

Long-term care plan recommendation for older adults with disabilities: a bipartite graph transformer and self-supervised approach.

Journal of the American Medical Informatics Association : JAMIA
BACKGROUND: With the global population aging and advancements in the medical system, long-term care in healthcare institutions and home settings has become essential for older adults with disabilities. However, the diverse and scattered care requirem...

Fast and interpretable mortality risk scores for critical care patients.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVE: Prediction of mortality in intensive care unit (ICU) patients typically relies on black box models (that are unacceptable for use in hospitals) or hand-tuned interpretable models (that might lead to the loss in performance). We aim to brid...

Evaluating robustly standardized explainable anomaly detection of implausible variables in cancer data.

Journal of the American Medical Informatics Association : JAMIA
OBJECTIVES: Explanations help to understand why anomaly detection algorithms identify data as anomalous. This study evaluates whether robustly standardized explanation scores correctly identify the implausible variables that make cancer data anomalou...