AIMC Topic: Delivery of Health Care

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DACBT: deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment.

Scientific reports
The classification of brain tumors (BT) is significantly essential for the diagnosis of Brian cancer (BC) in IoT-healthcare systems. Artificial intelligence (AI) techniques based on Computer aided diagnostic systems (CADS) are mostly used for the acc...

The regulation of artificial intelligence for health in Brazil begins with the General Personal Data Protection Law.

Revista de saude publica
Artificial intelligence develops rapidly and health is one of the areas where new technologies in this field are most promising. The use of artificial intelligence can modify the way health care and self-care are provided, besides influencing the org...

Smart systems and data-driven services in healthcare.

Computers in biology and medicine
The modern development of Medicine and Healthcare is primarily based on the automation of various processes to support the correct and timely medical decisions. A doctor or other medical staff's fast, accurate, reliable diagnosis, prevention, and tre...

Adverse events in the digital age and where to find them.

Pharmacoepidemiology and drug safety
Exponential growth of health-related data collected by digital tools is a reality within pharmaceutical and medical device research and development. Data generated through digital tools may be categorized as relevant to efficacy and/or safety. The en...

FHIR-Ontop-OMOP: Building clinical knowledge graphs in FHIR RDF with the OMOP Common data Model.

Journal of biomedical informatics
BACKGROUND: Knowledge graphs (KGs) play a key role to enable explainable artificial intelligence (AI) applications in healthcare. Constructing clinical knowledge graphs (CKGs) against heterogeneous electronic health records (EHRs) has been desired by...

Explainable, trustworthy, and ethical machine learning for healthcare: A survey.

Computers in biology and medicine
With the advent of machine learning (ML) and deep learning (DL) empowered applications for critical applications like healthcare, the questions about liability, trust, and interpretability of their outputs are raising. The black-box nature of various...

Competencies for the Use of Artificial Intelligence-Based Tools by Health Care Professionals.

Academic medicine : journal of the Association of American Medical Colleges
PURPOSE: The expanded use of clinical tools that incorporate artificial intelligence (AI) methods has generated calls for specific competencies for effective and ethical use. This qualitative study used expert interviews to define AI-related clinical...

Federated machine learning for a facilitated implementation of Artificial Intelligence in healthcare - a proof of concept study for the prediction of coronary artery calcification scores.

Journal of integrative bioinformatics
The implementation of Artificial Intelligence (AI) still faces significant hurdles and one key factor is the access to data. One approach that could support that is federated machine learning (FL) since it allows for privacy preserving data access. F...

Quality of reporting of randomised controlled trials of artificial intelligence in healthcare: a systematic review.

BMJ open
OBJECTIVES: The aim of this study was to evaluate the quality of reporting of randomised controlled trials (RCTs) of artificial intelligence (AI) in healthcare against Consolidated Standards of Reporting Trials-AI (CONSORT-AI) guidelines.