Artificial Intelligence Medical Compendium

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

Showing 13,561 to 13,570 of 211,153 articles

Biases in Healthcare AI and Their Impact on Patient Empowerment: A Scoping Review.

Studies in health technology and informatics May 21, 2026
BACKGROUND: Artificial Intelligence (AI) is increasingly applied in healthcare and is often linked to patient empowerment. However, biases in data, algorithms, and design may hinder empowerment by reinforcing inequalities and limiting autonomy. OBJEC... read more 

Moral MedTech: Exploring Ethics-by-Design for Artificial Intelligence in Healthcare via Serious Gaming.

Studies in health technology and informatics May 21, 2026
Artificial Intelligence (AI) is reshaping healthcare through data-driven medical technologies. While offering efficiency and personalization, these systems also pose ethical challenges around fairness, transparency, accountability, and patient autono... read more 

Keep it Simple, Stupid! Towards PySTRIPA for Medication Reviews.

Studies in health technology and informatics May 21, 2026
We revisit and reimagine STRIPA, the STRIP Assistant. The Systematic Tool to Reduce Inappropriate Prescribing Assistant is a medication review system at TRL7 for collaborative decision making about polypharmacy patients. We report on the first steps ... read more 

Evaluation of Graph-Based Algorithms for Early Detection of In-Hospital Mortality.

Studies in health technology and informatics May 21, 2026
Enhancing patient care quality depends on preventing health-related adverse events (HAEs), including in-hospital mortality, right from the start of hospitalization. Today, machine learning tools offer innovative solutions for predicting such events. ... read more 

Designing a Multimodal Data Structure for Cognitive Decline Using All of Us Resources and Gemini Flash Thinking.

Studies in health technology and informatics May 21, 2026
Cognitive decline is not easily defined, as it can manifest across multilayered and heterogeneous data. Its understanding requires the design of data structures that integrate all relevant dimensions. In this study, we address the challenge of implem... read more 

Guideline-Aligned Machine Learning for Predicting Ondansetron Administration at the End of Anaesthesia: Explainable Decision Support for PONV Prophylaxis.

Studies in health technology and informatics May 21, 2026
Artificial Intelligence (AI) and Clinical Practice Guidelines (CPGs) both aim to support clinical decision-making but may provide conflicting suggestions. This manuscript presents a Guideline-Aligned Machine Learning (GAML) model to predict ondansetr... read more 

Improving Explainability in Clinical Mortality Prediction Using Stacking Classifiers over Annotated Clinical Notes.

Studies in health technology and informatics May 21, 2026
Heart failure, a leading cause of hospitalization and death, requires early identification of high-risk patients to improve care. This study focuses on predicting 3-month mortality after hospital discharge using real-world clinical notes. For such pr... read more 

Explainable Machine Learning for Parkinson's Disease Screening Using Spiral Drawing Test.

Studies in health technology and informatics May 21, 2026
Parkinson's disease (PD) is a common neurological disorder that can severely affect the patient's quality of life. The Archimedean spiral drawing test is a widely used diagnostic tool for PD detection. Given the advancements in computer vision techni... read more 

National Collaboration on AI Deployment in Norway.

Studies in health technology and informatics May 21, 2026
In Norway, a national AI platform for radiology was introduced to support coordinated procurement, deployment, and scaling across all health regions. This study examines how this platform-based approach enables and constrains national AI deployment. ... read more 

A Durable Backdoor Attack on Medical Imaging via Federated Learning.

Studies in health technology and informatics May 21, 2026
Federated Learning (FL) enables multiple healthcare institutions to jointly train models without sharing raw patient data, making it a natural fit for privacy-sensitive medical applications. However, its distributed and partially trusted nature expos... read more 
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