An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| author | Rahgozar, Arya Mortezaagha, Pouria Edwards, Jodi Manuel, Douglas McGowen, Jessie Zwarenstein, Merrick Fergusson, Dean Tricco, Andrea Cobey, Kelly Sampson, Margaret King, Malcolm Richards, Dawn Bodnaruc, Alexandra Moher, David |
| author_facet | Rahgozar, Arya Mortezaagha, Pouria Edwards, Jodi Manuel, Douglas McGowen, Jessie Zwarenstein, Merrick Fergusson, Dean Tricco, Andrea Cobey, Kelly Sampson, Margaret King, Malcolm Richards, Dawn Bodnaruc, Alexandra Moher, David |
| contents | The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17181 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis Rahgozar, Arya Mortezaagha, Pouria Edwards, Jodi Manuel, Douglas McGowen, Jessie Zwarenstein, Merrick Fergusson, Dean Tricco, Andrea Cobey, Kelly Sampson, Margaret King, Malcolm Richards, Dawn Bodnaruc, Alexandra Moher, David Artificial Intelligence Computation and Language Digital Libraries Information Retrieval The Brain-Heart Interconnectome (BHI) combines neurology and cardiology but is hindered by inefficiencies in evidence synthesis, poor adherence to quality standards, and research waste. To address these challenges, we developed an AI-driven system to enhance systematic reviews in the BHI domain. The system integrates automated detection of Population, Intervention, Comparator, Outcome, and Study design (PICOS), semantic search using vector embeddings, graph-based querying, and topic modeling to identify redundancies and underexplored areas. Core components include a Bi-LSTM model achieving 87% accuracy for PICOS compliance, a study design classifier with 95.7% accuracy, and Retrieval-Augmented Generation (RAG) with GPT-3.5, which outperformed GPT-4 for graph-based and topic-driven queries. The system provides real-time updates, reducing research waste through a living database and offering an interactive interface with dashboards and conversational AI. While initially developed for BHI, the system's adaptable architecture enables its application across various biomedical fields, supporting rigorous evidence synthesis, efficient resource allocation, and informed clinical decision-making. |
| title | An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis |
| topic | Artificial Intelligence Computation and Language Digital Libraries Information Retrieval |
| url | https://arxiv.org/abs/2501.17181 |