Artificial Intelligence Medical Compendium

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

Showing 15,061 to 15,070 of 212,565 articles

Integrating Multi-Source Biomedical Data for Adverse Drug Reaction Analysis: A Knowledge Graph Approach.

Studies in health technology and informatics May 21, 2026
Understanding adverse drug reaction mechanisms requires integrating information from multiple heterogeneous sources. We developed a pipeline for combining drug-target data (DrugBank), biochemical pathways (Reactome), and adverse drug reactions identi... read more 

The SURROGATOR Framework for Context-Aware Surrogation of Privacy Sensitive Information in Medical Text.

Studies in health technology and informatics May 21, 2026
Clinical text cannot be shared freely due to data protection regulations. This paper introduces SURROGATOR, an open-source framework that replaces personal identifiable information with fictitious, high-fidelity synthetic surrogates to maintain text ... read more 

Recovering Sensitive Medical Text in Federated Learning.

Studies in health technology and informatics May 21, 2026
Federated Learning (FL) allows institutions to train shared models without exchanging raw data, making it a promising approach for healthcare applications that involve sensitive electronic health records (EHRs). However, despite this distributed desi... read more 

The Somnolink-Hub: A Central Infrastructure That Unites Sleep Data at Point of Care.

Studies in health technology and informatics May 21, 2026
Obstructive sleep apnea (OSA) is a sleeping disorder which can lead to severe health consequences. Information gaps in the patient's diagnostic path may result in duplicate examinations, while patients wait long for sleep diagnostics. We propose an a... read more 

Federated Deception Intelligence and the ASKOS Solution for Cyber-Resilient Healthcare Infrastructures.

Studies in health technology and informatics May 21, 2026
Healthcare infrastructures face escalating cybersecurity risks caused by legacy systems, connected IoMT devices, and complex data-sharing environments. Conventional intrusion detection and SIEM systems provide limited protection against zero-day and ... read more 

Time to Care-AI Supported Nursing Documentation.

Studies in health technology and informatics May 21, 2026
This study conducted a needs assessment to evaluate contextual factors, adoption barriers, and overall readiness of municipal healthcare services to implement and utilize AI-supported documentation solutions. The study was carried out by a multidisci... read more 

Water-MAS: A multi-agent LLM framework with instruction-data decoupling for smart water management.

Water research May 21, 2026
Sustainable water management demands transforming heterogeneous, long-sequence water data into timely, data-driven insights. While single-agent systems can perform basic tasks through tool integration, they suffer from low accuracy, limited adaptabil... read more 

Navigating the "Valley of Death": An Open-Source, Modular Framework for Generative AI in Healthcare.

Studies in health technology and informatics May 21, 2026
Generative AI (GenAI) adoption remains limited, particularly in under-resourced settings, despite its many applications. This disparity arises from fragmented integration, privacy concerns, and the translational gap (often called the "Valley of Death... read more 

On the Ethical Aspect of Artificial Intelligence-Based Decision Process for Transplantation.

Studies in health technology and informatics May 21, 2026
Liver transplantation is a complex process including organ donors, recipients, as well key stakeholders. We propose a new data workflow able to capture, merge and monitor information regarding several aspects of the transplantation process towards be... read more 

Integration and Harmonization of Multi-Source Obstetric Data Using Rule-Based NLP for Fetomaternal Risk Modelling.

Studies in health technology and informatics May 21, 2026
More than 80% of pregnancies in Germany are classified as high risk, leading to inefficient resource allocation. Although data-driven approaches could improve fetomaternal risk prediction, limited standardization across clinical databases remains a m... read more 
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