An AI-Driven Live Systematic Reviews in the Brain-Heart Interconnectome: Minimizing Research Waste and Advancing Evidence Synthesis

2025-01-25

AI Summary

Systematic reviews in the brain–heart interconnection (BHI) domain suffer from low synthesis efficiency, inconsistent methodological quality, and redundant research efforts. Method: We developed the first AI-driven, dynamic systematic review platform for BHI, integrating (i) automated PICOS extraction via a Bi-LSTM model (87% precision, 95.7% recall), (ii) knowledge graph–based semantic retrieval, (iii) LDA topic modeling, and (iv) RAG-augmented generation—where RAG-GPT-3.5 outperforms GPT-4 on graph- and topic-based queries. Contribution/Results: The platform fully automates literature screening, risk-of-bias assessment, evidence synthesis, and knowledge gap identification. It supports real-time updating, redundancy detection, and interactive exploration—enhancing timeliness, transparency, and clinical decision support. Its modular architecture ensures cross-domain adaptability across biomedical disciplines.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation

Abstract

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.

Problem

• Brain-Heart Interface
• Data Management
• Quality Control

Innovation

• Artificial Intelligence
• Bi-LSTM and GPT-3.5
• BHI and Biomedical Research Optimization