Environmental and economic costs behind LLMs.

PURPOSE: To discuss the economic and environmental implications of implementing large language models (LLMs) in radiology, highlighting both their transformative potential and the challenges they pose for equitable and sustainable adoption.

METHODS

Current trends in AI investment, infrastructure requirements, operational costs, and environmental impact associated with LLMs are analyzed, highlighting the specific challenges of integrating LLMs into radiological workflows, including data privacy, regulatory compliance, and cost barriers for healthcare institutions. The analysis also considers the costs of model validation, maintenance, and updates, as well as investments in system integration, staff training, and cybersecurity for clinical implementation.

RESULTS

LLMs have revolutionized natural language processing and offer promising applications in radiology, such as improved diagnostic support and workflow optimization. However, their deployment involves substantial financial and environmental costs. Training and operating these models require high-performance computing infrastructure, significant energy consumption, and large volumes of annotated data. Water usage and CO₂ emissions from data centers further raise sustainability concerns, while ongoing operational costs add to the financial burden. Subscription fees and per-query pricing may restrict access for smaller institutions, widening existing inequalities.

CONCLUSION

While LLMs offer significant benefits for radiology, their high economic and environmental costs present challenges to widespread and equitable adoption. Responsible use, sustainable practices, and policy frameworks are essential to ensure that AI-driven innovations do not exacerbate existing disparities in healthcare access and quality.