[2511.07436] Analysing Environmental Efficiency in AI for X-Ray Diagnosis

Computer Science > Artificial Intelligence arXiv:2511.07436 (cs) COVID-19 e-print Important: e-prints posted on arXiv are not peer-reviewed by arXiv; they should not be relied upon without context to guide clinical practice or health-related behavior and should not be reported in news media as established information without consulting multiple experts in the field. [Submitted on 31 Oct 2025 (v1), last revised 26 Mar 2026 (this version, v2)] Title:Analysing Environmental Efficiency in AI for X-Ray Diagnosis Authors:Liam Kearns View a PDF of the paper titled Analysing Environmental Efficiency in AI for X-Ray Diagnosis, by Liam Kearns View PDF Abstract:The integration of AI tools into medical applications has aimed to improve the efficiency of diagnosis. The emergence of large language models (LLMs), such as ChatGPT and Claude, has expanded this integration even further despite a concern for their environmental impact. Because of LLM versatility and ease of use through APIs, these larger models are often utilised even though smaller, custom models can be used instead. In this paper, LLMs and small discriminative models are integrated into a Mendix application to detect Covid-19 in chest X-rays. These discriminative models are also used to provide knowledge bases for LLMs to improve accuracy. This provides a benchmark study of 14 different model configurations for comparison of diagnostic accuracy and environmental impact. The findings indicated that while smaller models redu...