Artificial Intelligence-assisted Biomedical Literature Knowledge Synthesis to Support Decision-making in Precision Oncology.

Abstract

The delivery of effective targeted therapies requires comprehensive analyses of the molecular profiling of tumors and matching with clinical phenotypes in the context of existing knowledge described in biomedical literature, registries, and knowledge bases. We evaluated the performance of natural language processing (NLP) approaches in supporting knowledge retrieval and synthesis from the biomedical literature. We tested PubTator 3.0, Bidirectional Encoder Representations from Transformers (BERT), and Large Language Models (LLMs) and evaluated their ability to support named entity recognition (NER) and relation extraction (RE) from biomedical texts. PubTator 3.0 and the BioBERT model performed best in the NER task (best F1-score 0.93 and 0.89, respectively), while BioBERT outperformed all other solutions in the RE task (best F1-score 0.79) and a specific use case it was applied to by recognizing nearly all entity mentions and most of the relations. Our findings support the use of AI-assisted approaches in facilitating precision oncology decision-making.