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

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.