The In-depth Comparative Analysis of Four Large Language AI Models for Risk Assessment and Information Retrieval from...

Information retrieval (IR) and risk assessment (RA) from multi-modality imaging and pathology reports are critical to prostate cancer (PC) treatment. This study aims to evaluate the performance of four general-purpose large language model (LLMs) in IR and RA tasks.

We conducted a study using simulated text reports from computed tomography, magnetic resonance imaging, bone scans, and biopsy pathology on stage IV PC patients. We assessed four LLMs (ChatGPT-4-turbo, Claude-3-opus, Gemini-Pro-1.0, ChatGPT-3.5-turbo) on three RA tasks (LATITUDE, CHAARTED, TwNHI) and seven IR tasks. It included TNM staging, and the detection and quantification of bone and visceral metastases, providing a broad evaluation of their capabilities in handling diverse clinical data. We queried LLMs with multi-modality reports using zero-shot chain-of-thought prompting via application programming interface. With three adjudicators' consensus as the gold standard, these models' performances were assessed through repeated single-round queries and ensemble voting methods, using 6 outcome metrics.

Among 350 stage IV PC patients with simulated reports, 115 (32.9%), 128 (36.6%), and 94 (26.9%) belonged to LATITUDE, CHAARTED, and TwNHI high-risk, respectively. Ensemble voting, based on three repeated single-round queries, consistently enhances accuracy with a higher likelihood of achieving non-inferior results compared to a single query. Four models showed minimal differences in IR tasks with high accuracy (87.4%-94.2%) and consistency (ICC>0.8) in TNM staging. However, there were significant differences in RA performance, with the ranking as follows: ChatGPT-4-turbo, Claude-3-opus, Gemini-Pro-1.0, and ChatGPT-3.5-turbo, respectively. ChatGPT-4-turbo achieved the highest accuracy (90.1%, 90.7%,91.6%), and consistency (ICC 0.86, 0.93, 0.76) across 3 RA tasks.

ChatGPT-4-turbo demonstrated satisfactory accuracy and outcomes in RA and IR for stage IV PC, suggesting its potential for clinical decision support. However, the risks of misinterpretation impacting decision-making cannot be overlooked. Further research is necessary to validate these findings in other cancers.