AI Co-Scientist for Knowledge Synthesis in Medical Contexts: A Proof of Concept

• URL: http://arxiv.org/abs/2601.11825v1
• Date: Fri, 16 Jan 2026 23:07:58 GMT
• Title: AI Co-Scientist for Knowledge Synthesis in Medical Contexts: A Proof of Concept
• Authors: Arya Rahgozar, Pouria Mortezaagha,
• Abstract summary: We present an AI for scalable and transparent knowledge synthesis based on explicit formalization of Population, Intervention, Comparator, Outcome, and Study design (PICOS)<n>The platform integrates relational storage, vector-based semantic retrieval, and a Neo4j knowledge graph.<n>Results show that PICOS-aware and explainable natural language processing can improve the scalability, transparency, and efficiency of evidence synthesis.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Research waste in biomedical science is driven by redundant studies, incomplete reporting, and the limited scalability of traditional evidence synthesis workflows. We present an AI co-scientist for scalable and transparent knowledge synthesis based on explicit formalization of Population, Intervention, Comparator, Outcome, and Study design (PICOS). The platform integrates relational storage, vector-based semantic retrieval, and a Neo4j knowledge graph. Evaluation was conducted on dementia-sport and non-communicable disease corpora. Automated PICOS compliance and study design classification from titles and abstracts were performed using a Bidirectional Long Short-Term Memory baseline and a transformer-based multi-task classifier fine-tuned from PubMedBERT. Full-text synthesis employed retrieval-augmented generation with hybrid vector and graph retrieval, while BERTopic was used to identify thematic structure, redundancy, and evidence gaps. The transformer model achieved 95.7% accuracy for study design classification with strong agreement against expert annotations, while the Bi-LSTM achieved 87% accuracy for PICOS compliance detection. Retrieval-augmented generation outperformed non-retrieval generation for queries requiring structured constraints, cross-study integration, and graph-based reasoning, whereas non-retrieval approaches remained competitive for high-level summaries. Topic modeling revealed substantial thematic redundancy and identified underexplored research areas. These results demonstrate that PICOS-aware and explainable natural language processing can improve the scalability, transparency, and efficiency of evidence synthesis. The proposed architecture is domain-agnostic and offers a practical framework for reducing research waste across biomedical disciplines.

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