Natural language processing (NLP) is the embodiment of computer intelligence in acquiring knowledge, understanding, processing and expressing consciously and actively. It is the scientific key to promoting the informatization of medical practice and research. This paper reviews the development history and research basis of NLP, and focuses on the current application of NLP and large language models in biomedicine and traditional Chinese medicine (TCM), including the intelligent reading, information extraction and feedback of medical texts and ancient books of TCM, as well as the construction of medical knowledge graph and question-answering system. NLP is the technical support to explore the treasure house of TCM, which is of great practical significance to further promote the development of efficient and high-quality core values of TCM and to improve the service capacity.
As one of the hottest artificial intelligence technologies currently, ChatGPT, as one of the hottest artificial intelligence technologies today, plays a significant role in advancing the field of evidence-based medicine, particularly in expanding the sources of original evidence, enhancing the efficiency of evidence acquisition, aiding in shared decision-making between doctors and patients, and promoting education in evidence-based medicine and public science education. Presently, ChatGPT is in its "technological budding phase" and it is crucial to be wary of the risks it brings, such as "evidence contamination", algorithmic black boxes, security vulnerabilities, and the digital divide. To balance the positive effects and potential risks of ChatGPT in the realm of evidence-based medicine, we offer countermeasures and suggestions from the perspectives of ChatGPT's ethical standards, evidence sources, expert verification, and usage norms.
Systematic reviews and meta-analyses are essential methods in evidence-based medicine for integrating research evidence and guiding clinical decision-making. However, with the rapid expansion of medical research data, traditional approaches face significant challenges in terms of efficiency, accuracy, and reliability. In recent years, the rapid advancement of artificial intelligence (AI) technologies, particularly in natural language processing (NLP), machine learning (ML), and large language models (LLMs), has provided robust support for automating and intelligentizing systematic reviews and meta-analyses. This paper systematically reviews the progress of AI applications in these fields, tracing the evolution from traditional tools to intelligent platforms, and analyzes the functional characteristics, application scenarios, and limitations of existing AI-driven tools. Furthermore, it explores the challenges posed by AI in terms of adaptation to the medical field, multimodal data processing, and ethical transparency, while offering potential solutions and optimization strategies. Looking ahead, with the continuous optimization of technology, enhanced data sharing, and the establishment of industry standards, AI is expected to significantly improve the efficiency and quality of systematic reviews and meta-analyses, driving the transition from "tool-driven" to "intelligent collaboration." The deep integration of AI not only injects innovative momentum into evidence-based medicine but also reshapes its methodological foundation, laying a solid basis for a more intelligent, equitable, and efficient future.