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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2412.03531 |
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| _version_ | 1866917857035550720 |
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| author | Garcia, Gabriel Lino Manesco, João Renato Ribeiro Paiola, Pedro Henrique Miranda, Lucas de Salvo, Maria Paola Papa, João Paulo |
| author_facet | Garcia, Gabriel Lino Manesco, João Renato Ribeiro Paiola, Pedro Henrique Miranda, Lucas de Salvo, Maria Paola Papa, João Paulo |
| contents | The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_03531 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences Garcia, Gabriel Lino Manesco, João Renato Ribeiro Paiola, Pedro Henrique Miranda, Lucas de Salvo, Maria Paola Papa, João Paulo Computation and Language Machine Learning The rapid advancement of large language models (LLMs) has opened new boundaries in the extraction and synthesis of medical knowledge, particularly within evidence synthesis. This paper reviews the state-of-the-art applications of LLMs in the biomedical domain, exploring their effectiveness in automating complex tasks such as evidence synthesis and data extraction from a biomedical corpus of documents. While LLMs demonstrate remarkable potential, significant challenges remain, including issues related to hallucinations, contextual understanding, and the ability to generalize across diverse medical tasks. We highlight critical gaps in the current research literature, particularly the need for unified benchmarks to standardize evaluations and ensure reliability in real-world applications. In addition, we propose directions for future research, emphasizing the integration of state-of-the-art techniques such as retrieval-augmented generation (RAG) to enhance LLM performance in evidence synthesis. By addressing these challenges and utilizing the strengths of LLMs, we aim to improve access to medical literature and facilitate meaningful discoveries in healthcare. |
| title | A Review on Scientific Knowledge Extraction using Large Language Models in Biomedical Sciences |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2412.03531 |