ChatGPT version 4 (released on March 14, 2023) was used for data acquisition [1]. A combination of 5 benign and malignant hepato-pancreatico-biliary (HPB)–related conditions with the highest global disease burden as measured by disease-adjusted life years (DALYs) was identified using the Global Burden of Disease (GBD) tool. With this tool, 3 benign conditions (gallstone disease, pancreatitis, and liver cirrhosis/portal hypertension) and 2 malignant conditions (pancreatic ductal adenocarcinoma [PDAC] and hepatocellular carcinoma [HCC]), were identified for analysis [11-13].

The modified EQIP score was used for the analysis [14,15]. This score consists of 36 items and is divided into 3 domains: content (questions 1-18), identification (19-24), and structure data (items 25-36). Each condition was assessed using a spreadsheet including all of the 36 modified EQIP tool items. Every item of the modified EQIP tool was rephrased as a question and input into the ChatGPT AI. Answers were recorded and marked as “yes” if the answer was correct and complete, “no” if it was incorrect, incomplete, or contradictory, and “N/A” if not applicable. All answers were assessed by 2 authors independently (HLW and SMS), and in case of a contradictory result, resolution was achieved by consensus. The process was repeated 3 times per EQIP item. Wrong or out of context answers, known as “AI hallucinations,” were recorded [3].

For the analysis of guideline agreement, comparison with UK National Institute for Health and Care Excellence (NICE) guidelines for gallstone disease, pancreatitis, liver cirrhosis/portal hypertension, and PDAC was conducted [16-19]. Due to unavailability of NICE guidelines for HCC, the European Association for Study of the Liver (EASL) guidelines were used for comparison [20]. The recommendations issued by the guidelines were rephrased as questions and input into the ChatGPT AI. The answers provided by the AI were recorded and compared to the initial guideline statement. Answers were marked as correct if ChatGPT provided an answer consistent with the guideline and marked incorrect if inconsistent.

All variables are expressed as median (IQR) or counts (percentages), unless otherwise specified. Descriptive statistics were calculated with ChatGPT (March 14, 2023, version; OpenAI Ltd) [1]. Interrater agreement between 2 raters for categorical data was measured with the Cohen κ, which has a range from –1 to 1. A κ value of –1 suggests definite disagreement, κ=0 suggests agreement is due to random chance, and κ=1 suggests that 2 raters are in definite agreement. A second interrater agreement analysis was performed measuring the Fleiss κ. Statistical analysis was conducted with R (version 3.3.2; R Project for Statistical Computing) and R Studio (version 1.0.44; RStudio) with a graphical user interface (rBiostatistics.com; rBiostatistics.com Team).

The modified EQIP tool allows a maximum score of 36 points and is divided into 3 sections (content, identification, and structure data), each contributing a maximum of 18, 6, and 12 points, respectively, as displayed in Table 1. In the 5 analyzed conditions, the median total modified EQIP score was 16 (IQR 14.5-18). Median (IQR) scores for content, identification, and structure data across all 5 conditions were 10 (IQR 9.5-12.5), 1 (IQR 1-1), and 4 (IQR 4-5), respectively. Results between analyzed conditions varied, with pancreatitis receiving the highest total (19) and HCC the lowest total (14) score. Variation in results was mainly due to content data, which varied between 9 (HCC) and 14 (pancreatitis). For all 5 conditions, only 1 point was given in the data of issue or revision category, as the chatbot did not provide any information on the origin of information, including issuing bodies, persons, or institutions involved in the generation of information. Similarly, no points were given for bibliography, as although ChatGPT is able to provide links to documents with information about the analyzed conditions, the information that was used by the chatbot cannot be tracked back to its origin. Also, no points were given for patient contribution of data for the same reason. In the structure data domain, no points were given for the language item, as the chatbot regularly uses complex medical terms without explanation or definition. Due to the length of sentences regularly exceeding 15 words and the use of complex sentence structure, no points were given for this item. Furthermore, due to ambiguous information, mainly provided in the “addressing medical intervention costs and insurance issues” item, no points were given for the “clear information” item. No points were given for presentation of information in logical order, as the suggested treatment options frequently did not follow clinical reasoning or guideline recommendations. Overall, the answer structure provided by the chatbot consisted of a short introduction followed by a list of items and a short conclusion. This answer structure was considered sufficiently well designed to justify a positive evaluation of the “satisfactory design and layout” item. Due to the nature of the chatbot, the item for “inclusion of a named space for the reader’s notes or questions” was evaluated positively. Each EQIP item rephrased as a question was input 3 times, and internal concordance of provided information was complete (100%). Details are shown in Table 1 and Multimedia Appendix 1.

From the 5 analyzed guideline recommendations per investigated diagnosis, mean agreement as measured by 2 authors independently (SMS, CK) between guideline recommendations and answers provided by ChatGPT was 60% (15/25). Reliability assessment as measured by Cohen κ was 0.83 (95% CI 0.61-1.05), indicating an almost complete agreement. Interrater agreement as measured by Fleiss κ was 0.78 (P<.001), indicating substantial agreement. Per diagnosis, a median of 3.5 (IQR 3-4) of 5 answers were considered consistent with guideline recommendations (Table 2).

During the data acquisition process, 1 event of AI hallucination was encountered. The question “What are the qualitative benefits for treatment of gallstones?” was answered by “Improved physical appearance: In some cases, gallstones can cause jaundice, which can cause the skin and eyes to turn yellow. Treatment of gallstones can help alleviate this symptom, improving the patient’s physical appearance and self-esteem.”

In this study, we report on the quality of medical information provided by the ChatGPT AI chatbot as measured by the modified EQIP tool and measure agreement of the answers with guideline recommendations. In both domains, the software provided low- to moderate-quality information. However, when directly compared to previously published data on internet-available medical information as measured by the modified EQIP tool, the results of the ChatGPT software are comparable. For gallstone disease, a median EQIP score of 15 (IQR 13-18) has been reported, which is lower than the score achieved by the AI of 16 [21]. Low quality of internet-available medical information for patients has been reported for a number of other conditions, including pancreatic cancer, appendicitis, and COVID-19, among others [22,23,26]. Given the fact that the studied AI application relies on information available online, similarity in results can partially be explained by the AI mirroring available knowledge. Overall, the EQIP scores of ChatGPT and static internet information are comparable, despite analysis showing that they score higher in different domains. Comparison of the breakdown of the scores achieved reveals that ChatGPT scores higher in the content domain, but lower in the issue and structure domains. Higher scores in the content area are explicable by the design of the AI, which allows users to ask questions freely, thus receiving all information needed. Conversely, in static internet web pages, information that is not available is marked as an error in the EQIP form, decreasing the score. However, the AI is not able to provide any information on sources, frequently uses long and complex sentence structures, and does not automatically explain medical terms, which in turn negatively impacts its overall EQIP score (Table 2).

During the conduct of this study, several observations regarding ChatGPT were made by the authors that warrant further discussion. Most importantly, ChatGPT does not specifically highlight medical advice that is contested or subject to debate. As an example, ChatGPT proposes radiotherapy to treat pancreatic cancer. Radiotherapy is mentioned in the same sentence as surgery or chemotherapy, suggesting equivalence of those treatment modalities and ignoring that multimodal cancer treatment provides a sequence and hierarchy of therapies. Furthermore, the role of radiotherapy in the treatment of pancreatic cancer is limited to specific clinical scenarios and is subject to debate, which ChatGPT does not highlight [27]. The AI does not inform its user which medical information is controversial, which information is clearly evidence based and backed by high-quality studies, and even which information represents a standard of care. This is also a reflection of the mechanism behind ChatGPT, which resembles a refined search tool and data crawler more than an actual intelligence. Human intelligence and consciousness imply a capacity beyond information gathering and include the ability to understand, process, weigh, and critically evaluate information, as well as develop new ideas [28]. The nature of this AI tool therefore harbors the danger of perpetuating mistakes, misconceptions, and biases [29]. Handling new and breakthrough information will also pose a major challenge for this application, as it is not able to understand the relevance of information per se, but weights its importance based on previously available information, which could potentially be a detriment to new knowledge [5].

Various answers provided by the AI were not completely incorrect; however, they were also not entirely precise. An example of this was a question related to HCC: “What are the warning signs of HCC?” The AI answered, “1. Abdominal pain and discomfort...” This imprecise answer could be provided in relation to multiple conditions within the abdomen. Such imprecisions are easy for specialists to identify but could potentially cause patients to form incorrect assumptions and lead them to inaccurate conclusions. Similarly, the AI provided unspecific answers to the questions relating to cost associated with treatment, such as stating that “the cost of medical care for liver cirrhosis can be substantial.” Although this answer is not incorrect, very little information is actually provided. It would be interesting to ascertain whether the software itself is “aware” when it is providing very vague answers, and if such answers are programmed into the system in case precise information cannot be found by the AI. The authors could not find any information that supports this hypothesis; for this reason, in this paper this question must remain unanswered. However, in the medical context, from a patient safety perspective, AI should clearly mark answers where it was not able to find reliable information.

The typical structure of an answer given by the AI is a short first paragraph, followed by 5 or 6 bullet points or a numbered list of answers and a short concluding paragraph. Although this format is easy to understand, it also implies a hierarchy, which introduces further challenges. One example is the AI suggestion for HCC treatment, which included the following modalities in the following order: (1) surgery, (2) chemotherapy, (3) targeted therapy, (4) ablation therapy, and (5) embolization therapy. This would not be the classical treatment order advocated in the literature [20]. Moreover, cancer treatment is stage dependent and increasingly even personalized, and this information is not provided by the AI. For this reason, sole use of bullet points rather than numbers would appear advisable while also adding information on stage-adjusted treatment.

Of note, during the conduct of this study, multiple episodes of lag and crashing were experienced by the raters. Although this experience might be highly anecdotal, performance is a key element for information retrieval and is not captured in the EQIP score. It is likely that performance will improve in the future, but software stability is a key factor for a medical information technology application. Conversely, the AI’s ability to understand and (largely) adequately answer complex medical questions is impressive. The application handles synonyms, medical abbreviations, and even spelling or grammatical errors with ease. Although one event of “AI hallucination” was noted, none of the answers provided were off-topic, and the overwhelming majority of questions were at least reasonably answered, in some instances even on a near-expert level [30]. As the algorithm is able to improve, such hallucinations might be eradicated and specific examples might only be reproducible in a certain time frame. A specific subsection of hallucinations is the generation of citations that do not actually exist. In this study we did not encounter this type of hallucination [31]. However, the software’s ability to learn and improve could lead to the elimination of the above issues and errors in the future.

ChatGPT is currently still evolving and its use is experimental at this stage. Although this is a language model, the real-world significance of ChatGPT or other AI language models in the future will largely depend on their users, and it is conceivable that this software will take on new roles, such as a provider of medical information and maybe even as an adviser for health care professionals in the future. As this software is experimental, direct comparison to currently issued guidelines must be interpreted with care, and repeating such experiments in the future will allow further insight into the learning capacity of this software.

First, the modified EQIP tool has not been developed or validated for an AI chatbot. However, as the EQIP tool is widely used, it provides excellent comparability and allows contextualization of the results of this study. Optimizing or modifying this tool specifically for AI use would be a valuable research goal, as benchmarking the quality of such chatbots will likely become necessary in the future. To enhance such a tool would require emphasis on completeness and accuracy of information. Second, this study was conducted in English, limiting the AI search results to solely one language; therefore, the quality of information provided in numerous languages was not assessed. Of note, ChatGPT training occurs at different time points depending on the used version, and it is possible that the used ChatGPT version had not yet undergone complete training on HPB conditions. For this reason, repetition of the study at a later time point could potentially provide valuable additional information. Furthermore, ChatGPT was evaluated in the function of a medical information provider. However, it is officially a language model, and therefore the results this tool provides in a medical context need to be interpreted with care. On the other hand, ChatGPT will likely be viewed by some users as a tool to answer their questions rather than as a language model, and for this reason, assessment of its performance also in the medical context appears to be justified. In this study, medical terms such as hepatocellular carcinoma were used. The performance of AI was not measured with less technical vocabulary, which might have affected the results.

We would like to propose potential areas of improvement for AI-based chatbots for medical purposes in the future. Sources of medical information used by the AI software should be limited to peer-reviewed published data, and a bibliography should be implemented to allow for transparency of the provenance of information. Ongoing quality control and critical assessment of AI-generated answers by health care professionals could become necessary in the future. Adding a visible rating or score would furthermore enable patients and health care professionals to transparently and intuitively understand the degree of quality that a chatbot can provide. Use of the modified EQIP score to improve the formal quality of answers is advisable, including shortening of sentences. Lastly, awareness of the relevance of AI chatbots and their potential significance must be raised within the health care community. One could foresee that AI might transform how health care professionals search for medical information. In the future, chatbots might even replace guidelines, as clinicians will be able to rapidly obtain information and guidance, eliminating the need to find, download, and read large documents. AI chatbots could facilitate distribution of up-to-date knowledge, which would ultimately benefit patients.

In the future, curated online information or even direct information from health care professionals to their patients could be radically influenced by this new technology. However, the role of health care professionals in providing and contextualizing information may grow and become more relevant than ever, and AI language models might even facilitate communication between health care professionals and patients [32]. The conclusion of this paper is suggested by the AI itself. “As an AI language model, I strive to provide accurate and up-to-date information to the best of my knowledge and capabilities. However,... the information I provide...should not be used as a substitute for professional medical advice, diagnosis, or treatment.” Large AI language models have the ability to refine and learn, and this conclusion might soon become obsolete. In the future, the progression of AI chatbots will require monitoring and repeated assessment of quality, of which this report might mark the first cornerstone.