AI Medical Compendium Journal:
Systematic reviews

Showing 11 to 20 of 49 articles

Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records.

Systematic reviews
BACKGROUND: Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact...

Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.

Systematic reviews
BACKGROUND: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy healt...

Are ChatGPT and large language models "the answer" to bringing us closer to systematic review automation?

Systematic reviews
In this commentary, we discuss ChatGPT and our perspectives on its utility to systematic reviews (SRs) through the appropriateness and applicability of its responses to SR related prompts. The advancement of artificial intelligence (AI)-assisted tech...

Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study.

Systematic reviews
OBJECTIVE: To investigate the usefulness and performance metrics of three freely-available softwares (Rayyan®, Abstrackr® and Colandr®) for title screening in systematic reviews.

The effect of machine learning tools for evidence synthesis on resource use and time-to-completion: protocol for a retrospective pilot study.

Systematic reviews
BACKGROUND: Machine learning (ML) tools exist that can reduce or replace human activities in repetitive or complex tasks. Yet, ML is underutilized within evidence synthesis, despite the steadily growing rate of primary study publication and the need ...

Machine learning algorithms to identify cluster randomized trials from MEDLINE and EMBASE.

Systematic reviews
BACKGROUND: Cluster randomized trials (CRTs) are becoming an increasingly important design. However, authors of CRTs do not always adhere to requirements to explicitly identify the design as cluster randomized in titles and abstracts, making retrieva...

PICO entity extraction for preclinical animal literature.

Systematic reviews
BACKGROUND: Natural language processing could assist multiple tasks in systematic reviews to reduce workflow, including the extraction of PICO elements such as study populations, interventions, comparators and outcomes. The PICO framework provides a ...

Reducing systematic review burden using Deduklick: a novel, automated, reliable, and explainable deduplication algorithm to foster medical research.

Systematic reviews
BACKGROUND: Identifying and removing reference duplicates when conducting systematic reviews (SRs) remain a major, time-consuming issue for authors who manually check for duplicates using built-in features in citation managers. To address issues rela...

Public views on ethical issues in healthcare artificial intelligence: protocol for a scoping review.

Systematic reviews
BACKGROUND: In recent years, innovations in artificial intelligence (AI) have led to the development of new healthcare AI (HCAI) technologies. Whilst some of these technologies show promise for improving the patient experience, ethicists have warned ...

Exponential growth of systematic reviews assessing artificial intelligence studies in medicine: challenges and opportunities.

Systematic reviews
The evidence-based medicine (EBM) movement is stepping up its efforts to assess medical artificial intelligence (AI) and data science studies. Since 2017, there has been a marked increase in the number of published systematic reviews that assess medi...