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AI-pocalypse now: Automating the systematic literature review with SPARK (Systematic processing and automated review Kit) - gathering, organising, filtering, and scaffolding.
MethodsX
June 2025
School of Social Sciences, University of Tasmania, Tasmania 7005, Australia.
Researchers today face significant challenges reshaping the landscape of academic, government, and industry research due to the exponential growth of global research outputs and the advent of Generative Artificial Intelligence (GenAI). The annual increase in published works has made it difficult for traditional literature review and data analysis methods to keep pace, often rendering reviews outdated by the time of publication. In response, this methods article introduces a suite of new tools designed to automate a number of stages for systematic literature reviews.
View Article and Find Full Text PDFLongitudinal evolution of the transdiagnostic prodrome to severe mental disorders: a dynamic temporal network analysis informed by natural language processing and electronic health records.
Mol Psychiatry
January 2025
Early Psychosis: Interventions and Clinical-Detection (EPIC) Lab, Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK.
Modelling the prodrome to severe mental disorders (SMD), including unipolar mood disorders (UMD), bipolar mood disorders (BMD) and psychotic disorders (PSY), should consider both the evolution and interactions of symptoms and substance use (prodromal features) over time. Temporal network analysis can detect causal dependence between and within prodromal features by representing prodromal features as nodes, with their connections (edges) indicating the likelihood of one feature preceding the other. In SMD, node centrality could reveal insights into important prodromal features and potential intervention targets.
View Article and Find Full Text PDFESCARGOT: An AI Agent Leveraging Large Language Models, Dynamic Graph of Thoughts, and Biomedical Knowledge Graphs for Enhanced Reasoning.
Bioinformatics
January 2025
Department of Computational Biomedicine, Center for Artificial Intelligence Research and Education, Cedars Sinai Medical Center, 700 N. San Vicente Blvd., Pacific Design Center, Suite G-541H, West Hollywood, 90069, CA, USA.
Motivation: LLMs like GPT-4, despite their advancements, often produce hallucinations and struggle with integrating external knowledge effectively. While Retrieval-Augmented Generation (RAG) attempts to address this by incorporating external information, it faces significant challenges such as context length limitations and imprecise vector similarity search. ESCARGOT aims to overcome these issues by combining LLMs with a dynamic Graph of Thoughts and biomedical knowledge graphs, improving output reliability and reducing hallucinations.
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