AI Medical Compendium Topic

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Delivery of Health Care

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Healthcare Facilities Redesign Using Multicriteria Decision-Making: Fuzzy TOPSIS and Graph Heuristic Theories.

Journal of healthcare engineering
BACKGROUND: Healthcare facilities are crucial assets that are necessary to be updated and evaluated regularly. One of the most pressing issues today is the renovation of healthcare facilities to match international standards. In large projects involv...

A Review of Emerging Electromagnetic-Acoustic Sensing Techniques for Healthcare Monitoring.

IEEE transactions on biomedical circuits and systems
Conventional electromagnetic (EM) sensing techniques such as radar and LiDAR are widely used for remote sensing, vehicle applications, weather monitoring, and clinical monitoring. Acoustic techniques such as sonar and ultrasound sensors are also used...

APLUS: A Python library for usefulness simulations of machine learning models in healthcare.

Journal of biomedical informatics
Despite the creation of thousands of machine learning (ML) models, the promise of improving patient care with ML remains largely unrealized. Adoption into clinical practice is lagging, in large part due to disconnects between how ML practitioners eva...

Dental innovations which will influence the oral health care of baby boomers.

Special care in dentistry : official publication of the American Association of Hospital Dentists, the Academy of Dentistry for the Handicapped, and the American Society for Geriatric Dentistry
From the widespread use of smartphones and tablets to the multitude of applications available, older adults are showing an interest in utilizing technology to maintain their independence and to improve their quality of life. As technology continues t...

Natural Language Processing in Electronic Health Records in relation to healthcare decision-making: A systematic review.

Computers in biology and medicine
BACKGROUND: Natural Language Processing (NLP) is widely used to extract clinical insights from Electronic Health Records (EHRs). However, the lack of annotated data, automated tools, and other challenges hinder the full utilisation of NLP for EHRs. V...

The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making.

Current oncology (Toronto, Ont.)
Clinical applications of artificial intelligence (AI) in healthcare, including in the field of oncology, have the potential to advance diagnosis and treatment. The literature suggests that patient values should be considered in decision making when u...

Assistive Robots for Healthcare and Human-Robot Interaction.

Sensors (Basel, Switzerland)
Assistive robots are still mostly prototypes that only remotely recall human interactive dynamics [...].

Defining the distance between diseases using SNOMED CT embeddings.

Journal of biomedical informatics
Characterizing disease relationships is essential to biomedical research to understand disease etiology and improve clinical decision-making. Measurements of distance between disease pairs enable valuable research tasks, such as subgrouping patients ...

Interpretability of Clinical Decision Support Systems Based on Artificial Intelligence from Technological and Medical Perspective: A Systematic Review.

Journal of healthcare engineering
BACKGROUND: Artificial intelligence (AI) has developed rapidly, and its application extends to clinical decision support system (CDSS) for improving healthcare quality. However, the interpretability of AI-driven CDSS poses significant challenges to w...

Socially Assistive Devices in Healthcare-a Systematic Review of Empirical Evidence from an Ethical Perspective.

Science and engineering ethics
Socially assistive devices such as care robots or companions have been advocated as a promising tool in elderly care in Western healthcare systems. Ethical debates indicate various challenges. An important part of the ethical evaluation is to underst...