AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.

ABSTRACT:

Background

Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.

Objective

This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.

Methods

A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 ind

SUBMITTER: Chan PZ 

PROVIDER: S-EPMC11615544 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature