Exploring the Boundaries of GPT-4 in Radiology
Authors: name: Qianchu Liu; name: Stephanie Hyland; name: Shruthi Bannur; name: Kenza Bouzid; name: Daniel C. Castro; name: Maria Teodora Wetscherek; name: Robert Tinn; name: Harshita Sharma; name: Fernando Pérez-García; name: Anton Schwaighofer; name: Pranav Rajpurkar; name: Sameer Tajdin Khanna; name: Hoifung Poon; name: Naoto Usuyama; name: Anja Thieme; name: Aditya V. Nori; name: Matthew P. Lungren; name: Ozan Oktay; name: Javier Alvarez-Valle
Published: 2023-10-23 05:13:03

Abstract: The recent success of general-domain large language models (LLMs) has
significantly changed the natural language processing paradigm towards a
unified foundation model across domains and applications. In this paper, we
focus on assessing the performance of GPT-4, the most capable LLM so far, on
the text-based applications for radiology reports, comparing against
state-of-the-art (SOTA) radiology-specific models. Exploring various prompting
strategies, we evaluated GPT-4 on a diverse range of common radiology tasks and
we found GPT-4 either outperforms or is on par with current SOTA radiology
models. With zero-shot prompting, GPT-4 already obtains substantial gains
(approx 10% absolute improvement) over radiology models in temporal sentence
similarity classification (accuracy) and natural language inference (F_1).
For tasks that require learning dataset-specific style or schema (e.g. findings
summarisation), GPT-4 improves with example-based prompting and matches
supervised SOTA. Our extensive error analysis with a board-certified
radiologist shows GPT-4 has a sufficient level of radiology knowledge with only
occasional errors in complex context that require nuanced domain knowledge. For
findings summarisation, GPT-4 outputs are found to be overall comparable with
existing manually-written impressions.