📄 中文摘要
大型语言模型（LLMs）在癌症研究中并不能单独解决问题。尽管这些模型在数据处理和模式识别方面表现出色，但它们缺乏对生物学复杂性的深入理解。癌症的治疗需要多学科的合作，包括生物医学、化学和临床研究等领域的专业知识。LLMs可以辅助数据分析和生成假设，但不能替代实验和临床试验的重要性。有效的癌症治疗依赖于科学家的直觉和经验，以及对实验结果的深入分析。未来的研究应当结合LLMs的优势与传统科学方法，以推动癌症治疗的进展。
科学家与模拟器
出处: The Scientist and the Simulator
发布: 2026年2月11日
📄 English Summary
The Scientist and the Simulator
Large Language Models (LLMs) alone cannot solve cancer research challenges. While these models excel in data processing and pattern recognition, they lack a deep understanding of biological complexities. Effective cancer treatment requires interdisciplinary collaboration, involving expertise from biomedical, chemical, and clinical research fields. LLMs can assist in data analysis and hypothesis generation, but they cannot replace the importance of experiments and clinical trials. Successful cancer treatment relies on scientists' intuition and experience, as well as thorough analysis of experimental results. Future research should integrate the strengths of LLMs with traditional scientific methods to advance cancer treatment.