Exploring the potential of large language models in identifying metabolic dysfunction-associated steatotic liver disease: A comparative study of non-invasive tests and artificial intelligence-generated responses.

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This study sought to assess the capabilities of large language models (LLMs) in identifying clinically significant metabolic dysfunction-associated steatotic liver disease (MASLD). We included individuals from NHANES 2017-2018. The validity and reliability of MASLD diagnosis by GPT-3.5 and GPT-4 were quantitatively examined and compared with those of the Fatty Liver Index (FLI) and United States FLI (USFLI). A receiver operating characteristic curve was conducted to assess the accuracy of MASLD diagnosis via different scoring systems. Additionally, GPT-4V's potential in clinical diagnosis using ultrasound images from MASLD patients was evaluated to provide assessments of LLM capabilities in both textual and visual data interpretation. GPT-4 demonstrated comparable performance in MASLD diagnosis to FLI and USFLI with the AUROC values of .831 (95% CI .796-.867), .817 (95% CI .797-.837) and .827 (95% CI .807-.848), respectively. GPT-4 exhibited a trend of enhanced accuracy, clinical relevance and efficiency compared to GPT-3.5 based on clinician evaluation. Additionally, Pearson's r values between GPT-4 and FLI, as well as USFLI, were .718 and .695, respectively, indicating robust and moderate correlations. Moreover, GPT-4V showed potential in understanding characteristics from hepatic ultrasound imaging but exhibited limited interpretive accuracy in diagnosing MASLD compared to skilled radiologists. GPT-4 achieved performance comparable to traditional risk scores in diagnosing MASLD and exhibited improved convenience, versatility and the capacity to offer user-friendly outputs. The integration of GPT-4V highlights the capacities of LLMs in handling both textual and visual medical data, reinforcing their expansive utility in healthcare practice.