  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.
