Enhancing Oncological Surveillance Through Large Language Model-Assisted Analysis: A Comparative Study of GPT-4 and Gemini in Evaluating Oncological Issues From Serial Abdominal CT Scan Reports.

RATIONALE AND OBJECTIVES

We aimed to compare the capabilities of two leading large language models (LLMs), GPT-4 and Gemini, in analyzing serial radiology reports, to highlight oncological issues that require further clinical attention.

MATERIALS AND METHODS

This study included 205 patients, each with two consecutive radiological reports. We designed a prompt comprising a three-step task to analyze report findings using LLMs. To establish a ground truth, two radiologists reached a consensus on a six-level categorization, comprising tumor findings (categorized as improved, stable, or aggravated), "benign", "no tumor description," and "other malignancy." The performance of GPT-4 and Gemini was then compared based on their ability to match corresponding findings between two radiological reports and accurately reflect these categories.

RESULTS

In terms of accuracy in matching findings between serial reports, the proportion of correctly matched findings was significantly higher for GPT-4 (96.2%) than for Gemini (91.7%) (P < 0.01). For oncological issue identification, the precision for tumor-related finding determinations, recall, and F1-scores were 0.68 and 0.63 (P = 0.006), 0.91 and 0.80 (P < 0.001), and 0.78 and 0.70 for GPT-4 and Gemini, respectively. GPT-4 was more accurate than Gemini in determining the correct tumor status for tumor-related findings (P < 0.001).

CONCLUSION

This study demonstrated the potential of LLM-assisted analysis of serial radiology reports in enhancing oncological surveillance, using a carefully engineered prompt. GPT-4 showed superior performance compared to Gemini in matching corresponding findings, identifying tumor-related findings, and accurately determining tumor status.