LAUSR

Background: Several chatbots that utilize large language models now exist. As a particularly well-known example, ChatGPT employs an autoregressive modeling process to generate responses, predicting the next word based on previously derived words. Consequently, instead of deducing a correct answer, it arranges the most frequently appearing words in the learned data in order. Optimized for interactivity and content generation, it presents a smooth and plausible context, regardless of whether the content it presents is true. This report aimed to examine the reliability of ChatGPT, an artificial intelligence (AI) chatbot, in diagnosing diseases and treating patients, how to interpret its responses, and directions for future development.Current Concepts: Ten published case reports from Korea were analyzed to evaluate the efficacy of ChatGPT, which was asked to describe the correct diagnosis and treatment. ChatGPT answered 3 cases correctly after being provided with the patient’s symptoms, findings, and medical history. The accuracy rate increased to 7 out of 10 after adding laboratory, pathological, and radiological results. In one case, ChatGPT did not provide appropriate information about suitable treatment, and its response contained inappropriate content in 4 cases. In contrast, ChatGPT recommended appropriate measures in 4 cases.Discussion and Conclusion: ChatGPT’s responses to the 10 case reports could have been better. To utilize ChatGPT efficiently and appropriately, users should possess sufficient knowledge and skills to determine the validity of its responses. AI chatbots based on large language models will progress significantly, but physicians must be vigilant in using these tools in practice.