Exploring the Boundaries of GPT-4 in Radiology
- Qianchu Liu ,
- Stephanie Hyland ,
- Shruthi Bannur ,
- Kenza Bouzid ,
- Daniel Coelho de Castro ,
- Maria Teodora Wetscherek ,
- Robert Tinn ,
- Harshita Sharma ,
- Fernando Pérez-García ,
- Anton Schwaighofer ,
- Pranav Rajpurkar ,
- Sameer Tajdin Khanna ,
- Hoifung Poon ,
- Naoto Usuyama ,
- Anja Thieme ,
- Aditya Nori ,
- Matthew P Lungren ,
- Ozan Oktay ,
- Javier Alvarez-Valle
EMNLP 2023 |
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 (≈ 10% absolute improvement) over radiology models in temporal sentence similarity classification (accuracy) and natural language inference (F1). 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.