Small Language Models (SLMs) have shown remarkable performance in general
domain language understanding, reasoning and coding tasks, but their
capabilities in the medical domain, particularly concerning radiology text, is
less explored. In this study, we investigate the application of SLMs for
general radiology knowledge specifically question answering related to
understanding of symptoms, radiological appearances of findings, differential
diagnosis, assessing prognosis, and suggesting treatments w.r.t diseases
pertaining to different organ systems. Additionally, we explore the utility of
SLMs in handling text-related tasks with respect to radiology reports within
AI-driven radiology workflows. We fine-tune Phi-2, a SLM with 2.7 billion
parameters using high-quality educational content from Radiopaedia, a
collaborative online radiology resource. The resulting language model,
RadPhi-2-Base, exhibits the ability to address general radiology queries across
various systems (e.g., chest, cardiac). Furthermore, we investigate Phi-2 for
instruction tuning, enabling it to perform specific tasks. By fine-tuning Phi-2
on both general domain tasks and radiology-specific tasks related to chest
X-ray reports, we create Rad-Phi2. Our empirical results reveal that Rad-Phi2
Base and Rad-Phi2 perform comparably or even outperform larger models such as
Mistral-7B-Instruct-v0.2 and GPT-4 providing concise and precise answers. In
summary, our work demonstrates the feasibility and effectiveness of utilizing
SLMs in radiology workflows both for knowledge related queries as well as for
performing specific tasks related to radiology reports thereby opening up new
avenues for enhancing the quality and efficiency of radiology practice.

本研究探讨了小型语言模型在医学领域的应用，特别是与放射学文本相关的问题回答、症状理解、放射学结果的表现、鉴别诊断、评估预后和建议治疗等疾病方面。通过对高质量的放射志教育内容进行微调，我们的实验结果表明，小型语言模型在放射学工作流程中具备可行性和有效性，能够提供简明准确的答案，从而提高放射学实践的质量和效率。