在LLMs背后的环境和经济成本
Pilar López-Úbeda1, Teodoro Martín-Noguerol2, Antonio Luna2
1NLP Department, HT Medica, Carmelo Torres nº2, 23007, Jaén, Spain. p.lopez@htmedica.com.
International journal of computer assisted radiology and surgery
|January 12, 2026
概括
大型语言模型 (LLM) 在放射学中提供了变革性的潜力,但也带来了重大的经济和环境挑战. 确保公平和可持续的采用需要仔细考虑成本和资源消耗.
科学领域:
- 放射学中的人工智能
- 医疗保健信息学 医疗保健信息学
- 可持续的计算 可持续的计算
背景情况:
- 大型语言模型 (LLM) 正在推进自然语言处理,在放射学中具有潜在的应用.
- 将LLM整合到临床工作流程中提出了独特的挑战,包括数据隐私和监管障碍.
研究的目的:
- 分析在放射学中实施LLMs的经济和环境影响.
- 突出在医疗保健中公平和可持续地采用LLM的挑战和机会.
主要方法:
- 分析目前的AI投资趋势,基础设施需求和LLM的运营成本.
- 评估与LLM部署相关的环境影响,包括能源和水的使用.
- 考虑与模型验证,维护,更新,集成,培训和网络安全相关的成本.
主要成果:
- 在放射学中部署LLM会产生大量的财务和环境成本,包括高性能计算,能源消耗和数据注释.
- 可持续性问题源于数据中心的用水和二氧化碳排放.
- 运营成本,订阅费和每个查询的定价可能会限制较小机构的访问,可能会增加医疗保健差距.
结论:
- 由于高昂的经济和环境成本,在放射学中广泛和公平地采用LLMs受到挑战.
- 负责任的使用,可持续的做法和支持性的政策框架至关重要.
- 人工智能驱动的创新不能加剧医疗保健获取和质量的现有差异.
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