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Patient Safety

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I-SIRch: AI-powered concept annotation tool for equitable extraction and analysis of safety insights from maternity investigations.

International journal of population data science
BACKGROUND: Maternity care is a complex system involving treatments and interactions between patients, healthcare providers, and the care environment. To enhance patient safety and outcomes, it is crucial to understand the human factors (e.g. individ...

Exploratory Analysis of Nationwide Japanese Patient Safety Reports on Suicide and Suicide Attempts Among Inpatients With Cancer Using Large Language Models.

Psycho-oncology
OBJECTIVE: Patients with cancer have a high risk of suicide. However, evidence-based preventive measures remain unclear. This study aimed to investigate suicide prevention strategies for hospitalized patients with cancer by analyzing nationwide patie...

Safety and Precision AI for a Modern Digital Health System.

Yearbook of medical informatics
Artificial intelligence (AI) promises to revolutionize healthcare. Currently there is a proliferation of new AI applications that are being developed and beginning to be deployed across many areas in healthcare to streamline and make healthcare proce...

A machine learning-based clinical predictive tool to identify patients at high risk of medication errors.

Scientific reports
A medication error is an inadvertent failure in the drug therapy process that can cause serious harm to patients by increasing morbidity and mortality and are associated with significant economic costs to the healthcare system. Medication reconciliat...

Ethical Application of Generative Artificial Intelligence in Medicine.

Arthroscopy : the journal of arthroscopic & related surgery : official publication of the Arthroscopy Association of North America and the International Arthroscopy Association
Generative artificial intelligence (AI) may revolutionize health care, providing solutions that range from enhancing diagnostic accuracy to personalizing treatment plans. However, its rapid and largely unregulated integration into medicine raises eth...

Development of a Preliminary Patient Safety Classification System for Generative AI.

BMJ quality & safety
Generative artificial intelligence (AI) technologies have the potential to revolutionise healthcare delivery but require classification and monitoring of patient safety risks. To address this need, we developed and evaluated a preliminary classificat...

AI for image quality and patient safety in CT and MRI.

European radiology experimental
Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to enhance radiological procedures, in particular algorithms designed to minimize radiation exposure and...

Radiation oncology at crossroads: Rise of AI and managing the unexpected.

Journal of applied clinical medical physics
Integrating artificial intelligence (AI) into radiation oncology has revolutionized clinical workflows, enhancing efficiency, safety, and quality. However, this transformation comes with a price of increased complexity and the emergence of unpredicta...

Developing a deep learning model for the automated monitoring of acupuncture needle insertion: enhancing safety in traditional acupuncture practices.

BMC complementary medicine and therapies
BACKGROUND: Acupuncture is a widely practiced traditional therapy, yet safety concerns, particularly needle breakage and retention, remain critical issues that can lead to complications such as infections, organ injury, or chronic pain. This study ai...

Legal Implication in Utilizing Automated Robots: A Written Informed Consent Form Proposal.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: Robotic systems enhance physicians' capabilities by replicating hand movements in real-time, ensuring precise control and a quick return to conventional procedures if patient safety is compromised. Physicians performing robot-assisted pro...