AI Medical Compendium Topic

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Hospitals

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[Factors influencing the implementation of AI-based decision support systems for antibiotic prescription in hospitals: a qualitative analysis from the perspective of health professionals].

Gesundheitswesen (Bundesverband der Arzte des Offentlichen Gesundheitsdienstes (Germany))
BACKGROUND: Decision support systems based on artificial intelligence might optimize antibiotic prescribing in hospitals and prevent the development of antimicrobial resistance. The aim of this study was to identify impeding and facilitating factors ...

Deep Learning on Electrocardiograms for Prediction of In-hospital Intradialytic Hypotension in Patients with ESKD.

Kidney360
Intradialytic hypotension is common in patients who are on hemodialysis. We applied deep learning techniques to ECGs to predict patients at risk of IDH. The performance of the model was good with an AUC of 0.763 and AUPRC of 0.35.

New Robotic Platforms in General Surgery: What's the Current Clinical Scenario?

Medicina (Kaunas, Lithuania)
: Robotic surgery has been widely adopted in general surgery worldwide but access to this technology is still limited to a few hospitals. With the recent introduction of new robotic platforms, several studies reported the feasibility of different sur...

Proportionally Fair Hospital Collaborations in Federated Learning of Histopathology Images.

IEEE transactions on medical imaging
Medical centers and healthcare providers have concerns and hence restrictions around sharing data with external collaborators. Federated learning, as a privacy-preserving method, involves learning a site-independent model without having direct access...

How does the model make predictions? A systematic literature review on the explainability power of machine learning in healthcare.

Artificial intelligence in medicine
BACKGROUND: Medical use cases for machine learning (ML) are growing exponentially. The first hospitals are already using ML systems as decision support systems in their daily routine. At the same time, most ML systems are still opaque and it is not c...

GPT Technology to Help Address Longstanding Barriers to Care in Free Medical Clinics.

Annals of biomedical engineering
The implementation of technology in healthcare has revolutionized patient-centered decision making by providing contextualized information about a patient's healthcare journey, leading to increased efficiency (Keyworth et al. in BMC Med Inform Decis ...

Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0.

Sensors (Basel, Switzerland)
Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Hea...

Deep learning for deterioration prediction of COVID-19 patients based on time-series of three vital signs.

Scientific reports
Unrecognized deterioration of COVID-19 patients can lead to high morbidity and mortality. Most existing deterioration prediction models require a large number of clinical information, typically collected in hospital settings, such as medical images o...

Risk predictions of hospital-acquired pressure injury in the intensive care unit based on a machine learning algorithm.

International wound journal
Pressure injury (PI), or local damage to soft tissues and skin caused by prolonged pressure, remains controversial in the medical world. Patients in intensive care units (ICUs) were frequently reported to suffer PIs, with a heavy burden on their life...

A Methodology for a Scalable, Collaborative, and Resource-Efficient Platform, MERLIN, to Facilitate Healthcare AI Research.

IEEE journal of biomedical and health informatics
Healthcare artificial intelligence (AI) holds the potential to increase patient safety, augment efficiency and improve patient outcomes, yet research is often limited by data access, cohort curation, and tools for analysis. Collection and translation...