TL;DR
Artificial intelligence-driven tools performed well for title/abstract screening and general data extraction but were less accurate for full-text screening and interpretation of modeling choices.
Systematic literature reviews are widely acknowledged to be at the top of most evidence hierarchies but are very time-consuming and labour-intensive. The advent of artificial intelligence (AI) offers possibilities to accelerate the review process. We propose a systematic literature review protocol that incorporates A...
The models reproduced human screening tendencies despite the small dataset size, demonstrating the technical feasibility of LLM-assisted article selection and providing the first demonstration of LLM-assisted identification of EQ-5D data in biomedical literature.
Use of artificial intelligence to improve accuracy of risk assessment for CVD demonstrates the technological capacity to merge known sociodemographic predictors with biologic variables and examine non-linear interactions among these.
Background Machine learning models have been proposed for identifying prediabetes, but their methodological quality, generalisability, and readiness for primary care implementation remain uncertain. Methods We conducted a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-compliant systema...
Abstract Background A growing body of literature leverages large language models (LLMs) to make mental health predictions. However, these models are prone to bias, and studies to validate their clinical utility are lacking. Objective This scoping review aims to uncover bias and clinical utility limitations stemming fro...
This review synthesises studies on ML-based prediction using EHR or routinely collected electronic clinical data, with direct LMIC evidence analysed alongside high-income country (HIC) studies, included as methodological benchmarks.
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