How to use AI to assist in interdisciplinary literature retrieval?

Artificial intelligence significantly enhances interdisciplinary literature retrieval by employing natural language processing to identify connections across domains. This approach enables comprehensive discovery beyond manual keyword mapping. Core methodologies involve semantic search engines recognizing conceptual parallels rather than exact terminology matches. Researchers must explicitly define their interdisciplinary scope and leverage specialized tools like domain-specific databases alongside cross-disciplinary platforms. Key limitations include vocabulary discrepancies between fields and the necessity of iterative query refinement to mitigate AI's interpretational constraints. Implementation begins with formulating a multifaceted research question and deploying AI-powered platforms (e.g., Semantic Scholar, Dimensions). Employ filters such as publication date and interdisciplinary relevance scores. Cross-reference seminal papers identified by AI with citation network tools like Connected Papers for validation. Finally, manually verify extracted themes for scholarly coherence, ensuring identified literature bridges disciplines effectively while covering essential theories and methodologies.