AIMC Topic: Patient Safety

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A Machine Learning Approach with Human-AI Collaboration for Automated Classification of Patient Safety Event Reports: Algorithm Development and Validation Study.

JMIR human factors
BACKGROUND: Adverse events refer to incidents with potential or actual harm to patients in hospitals. These events are typically documented through patient safety event (PSE) reports, which consist of detailed narratives providing contextual informat...

Technical and ethical considerations in telesurgery.

Journal of robotic surgery
Telesurgery, a cutting-edge field at the intersection of medicine and technology, holds immense promise for enhancing surgical capabilities, extending medical care, and improving patient outcomes. In this scenario, this article explores the landscape...

Psychotherapy, artificial intelligence and adolescents: ethical aspects.

Journal of preventive medicine and hygiene
Artificial intelligence (AI) has rapidly advanced in various domains, including its application in psychotherapy. AI-powered psychotherapy tools present promising solutions for increasing accessibility to mental health care. However, the integration ...

Patient safety classifications, taxonomies and ontologies, part 2: A systematic review on content coverage.

Journal of biomedical informatics
BACKGROUND: Content coverage of patient safety ontology and classification systems should be evaluated to provide a guide for users to select appropriate ones for specific applications. In this review, we identified and compare content coverage of pa...

A Novel Two-Stage Induced Deep Learning System for Classifying Similar Drugs with Diverse Packaging.

Sensors (Basel, Switzerland)
Dispensing errors play a crucial role in various medical errors, unfortunately emerging as the third leading cause of death in the United States. This alarming statistic has spurred the World Health Organization (WHO) into action, leading to the init...

Detection of Pacemaker and Identification of MRI-conditional Pacemaker Based on Deep-learning Convolutional Neural Networks to Improve Patient Safety.

Journal of medical systems
With the increased availability of magnetic resonance imaging (MRI) and a progressive rise in the frequency of cardiac device implantation, there is an increased chance that patients with implanted cardiac devices require MRI examination during their...

Use of real-time immersive digital training and educational technologies to improve patient safety during the processing of reusable medical devices: Quo Vadis?

The Science of the total environment
Hospital acquired infections stemming from contaminated reusable medical devices are of increasing concern. This issue is exaggerated with the introduction of complex medical devices like endoscopes and robotic instrumentation. Although medical devic...

Large language models encode clinical knowledge.

Nature
Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to a...

Potentiality of algorithms and artificial intelligence adoption to improve medication management in primary care: a systematic review.

BMJ open
OBJECTIVES: The aim of this study is to investigate the effect of artificial intelligence (AI) and/or algorithms on drug management in primary care settings comparing AI and/or algorithms with standard clinical practice. Second, we evaluated what is ...