Medical Misinformation in AI-Assisted Self-Diagnosis: Development of a Method (EvalPrompt) for Analyzing Large Language Models

Introduction

Background

Large language models (LLMs) have grown in popularity with an ever-expanding list of applications due to their efficiency and accessibility [1,2]. With their increased usage, LLMs are gaining user trust [3], partly due to the anthropomorphic responses produced by models such as GPT-4o, even though they can generate misinformation at scale [4,5]. Recent reports highlight the inability of differentiating truth from misinformation, and the potential collapse of health care systems, as major disruptors on the horizon [6]. This emphasizes the urgent need to develop solutions to ensure the delivery of factual information.

In health care, self-diagnosis through web searches has become a widespread practice and is especially important for underserved communities [7-10], which means that the prospective usage of LLMs in this domain is inevitable. However, relying solely on online searches for health information can result in severe misinformation as content on social media often spreads more rapidly than scientific knowledge [11]. Inaccurate content, conspiracy theories, and false claims are all forms of misinformation which can impact public perceptions, alter behaviors, and reduce trust in health care systems [12]. Moreover, the ongoing global shortage of health care workers [13-16] has driven government entities and health care organizations to explore the use of LLMs as health care assistants and expertise replacements for diagnosis and education [17-23]. Thus, there is a need to study the quality and reliability of LLM-generated responses to health care–related questions.

Prior Work

Recent work has focused on analyzing ChatGPT across various industries, including its application within health care. ChatGPT is a natural language processing model distinctive for its narrative response style to user input [24,25]. Studies have assessed its performance on examinations [20,24,26] and its utility as a self-studying tool [24,27,28], leveraging its ability to provide tailored responses and immediate feedback. Furthermore, ChatGPT has demonstrated potential in assisting research and academic writing by enhancing efficiency and mitigating gaps in researcher knowledge [29]. However, the increased usage of ChatGPT raises significant ethical concerns regarding plagiarism, bias, transparency, inaccuracy, and health equity [30-34].

ChatGPT has also demonstrated superior performance in answering medical questions compared to other LLMs [35]. Research has explored its applications in medical education, including its effectiveness on licensing examinations, tailored learning experiences, and comprehension of complex medical concepts and clinical reasoning [20,24,27,33,36]. Other areas of study have focused on identifying inefficiencies and inaccuracies within clinical workflows, medical research, and diagnoses, with the objective of integrating LLMs to optimize documentation, triage, and clinical data management procedures [33,37-44]. Additionally, investigations into diagnostic assistance have integrated patient questionnaires and medical imaging with LLMs [43,45-48]. Despite the apparent high performance of LLMs in health care, they remain inferior compared to the judgment of human clinicians [49].

In summary, prior work has generally evaluated the trustworthiness of LLMs [50], along with specifically examining their performance in medical situations. These methods have used the multiple-choice questions from the United States Medical Licensing Exam (USMLE) [51] to evaluate the capability of LLMs in attaining scores near the passing threshold of 60% [20,52]. However, these evaluations inaccurately depict the capability of LLMs for self-diagnosis. In practical situations where individuals use technology to self-diagnose, they would not include answers when posing questions and would not provide the same level of information as the examination questions.

Goal of This Study

This study critically examines the performance of LLMs in responding to health care–related questions. To achieve this, we propose evaluation of LLM prompts. This evaluation procedure contains detailed guidelines to assess ChatGPT’s response to open-ended questions and validate the robustness of these responses using a sentence dropout method. This 2-staged approach, to our knowledge, is the first comprehensive strategy aimed at better understanding LLM responses and their implications for medical misinformation. We hypothesize that LLMs, particularly ChatGPT-4.0 (referred to as GPT-4.0), are currently unsuitable for self-diagnosis purposes since a significant portion of responses will be ambiguous or incorrect. In particular, this hypothesis is validated if GPT-4.0 surpasses a minimum threshold of 60% [20] for each of the following three questions:

1. Are the responses clear? This question can be answered by analyzing the response consistency.
2. Are the responses genuinely correct? This question can be answered by identifying the responses classified as correct by all assessors.
3. Are the responses robust? This question can be answered by conducting an ablation study on the correct responses.

Methods

Study Design

In this section, the considerations and preparations for the decided dataset are first specified. Then, the assessor procedures and guidelines are discussed along with the process used to analyze the output answers from ChatGPT. Finally, the complete testing methodology is introduced. The overall process uses both nonmedical and medical experts for assessment and is segmented into two experiments: (1) ChatGPT responses on USMLE step 1 open-ended prompts and (2) ChatGPT robustness and ablation study analysis. The GPT-4.0 responses were generated for each of the USMLE questions using Python, with the code and datasets available in the EvalPrompt (evaluation of large language model prompts) repository [53].

Dataset Considerations and Preparations

Overview

The USMLE [51] dataset consists of 3 steps undertaken by medical students throughout their program. Each step is a test consisting of multiple-choice, single-answer, and no-justification questions. Particularly, in this work, the questions are extracted from step 1 since the step 2 and step 3 questions are medically complex for the general population. Furthermore, only textual questions were kept, resulting in a dataset containing 94 single-answer questions that would be used for prompting ChatGPT. Textbox 1 [51] displays a sample of an extracted step 1 question.

Baseline ChatGPT Answer Analysis

The first experiment established the foundation for all testing. From the initial multiple-choice questions, each question was transformed into an open-ended question to accurately simulate the circumstances of an individual interacting with ChatGPT. This process was accomplished by removing the multiple-choice options and replacing any instance of “which of the following” with “what.” An example transformation is provided in Textbox 2 [51], where the original question from Textbox 1 was transformed into an open-ended question.

ChatGPT Robustness and Ablation Study Analysis

The second experiment was built upon the foundation established in the first experiment by using only the questions that were categorized as correct (C) by all assessors. Using this subset of data, new questions were iteratively generated by removing a sentence from the question and prompting ChatGPT with the modified question. This process is repeated across all the sentences of a question, excluding the final question sentence indicated by the “what...?” structure. For instance, if a question has 3 sentences, then 3 versions of the same question would be generated, where each question variation has 2 sentences. The first version would remove the first sentence, the second version would remove the second sentence, and the third version would remove the third sentence. The ablation study aimed to simulate how a self-diagnosing individual may overlook information within their health-related searches.

Each of the open-ended modified questions was then provided to ChatGPT using the OpenAI application programming interface to aggregate the LLM responses for the questions. Specifically, the gpt-4 model was used for GPT-4.0, along with specific parameters to ensure consistency among the responses and that each question would be answered independently. For instance, the model’s temperature was set to zero to ensure the responses were focused and deterministic. Details of the complete GPT-4.0 model and corresponding parameters are provided in Multimedia Appendix 1, along with Textbox 3 [51] displaying the GPT-4.0 response to the open-ended Textbox 2 question.

Assessor Procedures and Guidelines

The 94 open-ended questions served as prompts for ChatGPT, where 3 nonexperts and 3 experts evaluated the generated responses. Nonexperts were used to simulate the self-diagnosis process, while experts were used to assess the response quality.

Each assessor was provided with detailed procedures and guidelines as outlined in Multimedia Appendix 2 to ensure the response categorization process was standardized among the assessors. Assessors were instructed to categorize the responses as either C, partially correct (PC), incorrect (I), or ambiguous (A), and were provided with detailed examples to distinguish between the categories. Moreover, assessors were directed to evaluate the responses independently to ensure no bias.

Testing Methodology

We develop the testing methodology EvalPrompt, as summarized in Figure 1. In total, $N_q=94$ questions were selected from the USMLE step 1 test. Each question contained a question portion $x_j$, answer options $o_j$, and a correct answer $y_j$, altogether forming the dataset of multiple-choice questions $D={\left\{x_j,o_j,y_j\right\}}_{j=1}^{N_q}$. Subsequently, each question, $x_j$, was extracted and transformed into an open-ended variation which was then presented to ChatGPT.

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After collecting and processing the answers from ChatGPT, 3 nonexpert and 3 expert assessors independently evaluated each ChatGPT answer, ${\widehat{y}}_j$. In total, $K=6$ assessors were used. The assessors were instructed to provide a label $z\in Z;Z=\left\{C,PC,I,A\right\}$ denoting whether the answer, in comparison to the ground truth, $y_j$, was C, PC, I, or A, respectively. The assessment process can be expressed as a function $f^k\left({\widehat{y}}_j,y_j\right)=z_j^k$ for the $k^{th}$ assessor, where the assessor would use the correct answer to the question along with ChatGPT’s answer to categorize the response.

After the $k$ assessors finished the categorization process, the questions categorized as C by all $k$ assessors were aggregated to define a subset dataset, $\beta$. Once the resulting dataset, $\beta =\left\{x_j|z_j^k=C\forall k\in K\right\}$ was generated, the first experiment was concluded.

The second experiment was built upon the dataset and assessments completed during the first experiment. An ablation study was conducted over the questions within dataset $\beta$ via an iterative sentence dropout by performing $i-1$ iterations over an open-ended prompt $x_j$, where $i$ is the number of sentences in the prompt. For the $i^{th}$ iteration, the $i^{th}$ sentence was removed from the prompt before running the prompt through ChatGPT, as expressed by $x_{ji}$, where $x_j\in \beta$. Each $i^{th}$ sentence was iteratively removed and processed except for the final sentence which contained the question sentence. The final sentence of the question was mandatory to include to ensure ChatGPT provided an appropriate response.

After processing the sentence dropout questions through ChatGPT, the same 6 assessors evaluated the responses, ${\widehat{y}}_j^{`}$. The assessment process can again be expressed as a function $f^k\left({\widehat{y}}_j^{`}\mathrm{},y_j\right)=z_j^{`k}$ for the $k^{th}$ assessor, where the assessor would use the correct answer to the question along with ChatGPT’s answer to categorize the response. This generated the resulting dataset, $W$, defined as $W=\left\{x_{ji}|z_j^{`k}=C\forall k\in K\right\}$, being the subset of USMLE question variations for the certainly correctly prompts.

Ethical Considerations

Since the aim of this study is to analyze ChatGPT and not human subjects, a research ethics board review was not required. The evaluation of ChatGPT’s responses was carried out on a volunteer basis, and all assessors were informed that contributing to the experiments would not result in any safety or privacy risks.

Results

Are the Responses Clear?

To determine if the GPT-4.0 responses are clear, response categorizations across assessors were compared. As displayed in Figure 2, categorization disparities exist across the nonexpert and expert assessors with few alignments across assessors as depicted in Table 1. Namely, the number of C categorizations ranges from 39 to 51, while the number of I categorizations ranges from 12 to 39. This wide range of values for the categories immediately depicts the uncertainty across assessors due to each assessor having varying backgrounds and levels of medical expertise.

Analyzing the categorization for each question independently, many discrepancies exist among the assessors. As detailed in Table 2, on average 52% ([(52+46)/2]/94, SD of 3) of the responses in experiment 1 and 73% ([(119+104)/2]/152, SD of 7.5) of the responses in experiment 2 were categorized identically by the assessors. For example, USMLE question 54.1 was categorized by the nonexperts as PC, A, and C, and by the experts as I, PC, and PC, respectively. The inconsistency in categorizations depicts that LLM responses do not yet have a single apparent answer, but rather are still subject to interpretation depending on the individual.

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The nonexpert and expert assessors reached an identical conclusion for 52 and 46 of the questions for experiment 1, respectively, as detailed in Table 2. However, the groups collectively could only reach an identical conclusion for 32 of the questions as listed in Table 1. Since only 34% (32/94) of the responses were consistent across all assessors, the 60% threshold could not be met indicating that the responses are unclear. The decrease in the collective number of identically classified responses suggests that the GPT-4.0 responses are still too ambiguous for assessors to reach appropriate conclusions. The responses are not obvious enough such that anyone, regardless of their background and expertise, can reach the same conclusion.

Are the Responses Genuinely Correct?

To determine the number of GPT-4.0 responses that are genuinely correct, the responses categorized as C by all nonexpert and expert assessors were analyzed and compared. As provided in Table 1, the experiment 1 categorizations where all assessors agreed are listed. In particular, 29 of the 94 responses were categorized as C, meaning that GPT-4.0 is certainly correct 31% (29/94) of the time.

The limited number of C responses indicates that GPT-4.0 is not often factual. As portrayed in Table 2, nonexpert and expert assessors classified 33 and 35 of the responses as C, respectively, even though collectively 29 responses were considered C. Since only 31% (29/94) of the responses were considered C across all assessors, the 60% threshold could not be met indicating that the responses are mostly incorrect. Hence, even though both groups approximately categorized the same number of responses as C, there are still many responses that are either A or I.

Are the Responses Robust?

To determine if the GPT-4.0 responses are robust, an ablation study was conducted on the 29 correct responses. Reprompting GPT-4.0 with similar variations of the correct questions tested its ability to attain the correct answer even with information missing. This process aimed to simulate the self-diagnosis process since each individual would prompt LLMs with varying levels of information; some individuals would provide extensive details, while others may provide limited information. Thus, assessing GPT-4.0’s robustness.

Table 2 provided the categorization details of the ablation study, consisting of 29 unique questions with 152 question variations. On average 68% ([(111+96)/2]/152, SD of 7.5) of the responses continue to be categorized as C even after removing information for each group. These results are far greater than GPT-4.0’s accuracy on the initial 94 questions, which was correct only 31% (29/94) of the time. Moreover, Figure 3 depicts the categorizations for each of the 6 assessors. Assessors categorized the experiment 2 dataset C much more frequently than the experiment 1 dataset. On average, 80% ([(127+120+105+116+133+129)/6]/152, SD of 9.34) of the assessors categorized the questions as C, depicting GPT-4.0’s robustness on answers that are certainly correct.

Table 3 also displays the assessment similarities between the nonexpert and expert assessors for experiment 2. A total of 92 of the 152 questions were categorized C by all nonexpert and expert assessors, meaning that 61% (92/152) of the responses are certainly correct. Since 61% (92/152) of the responses for the sentence dropout experiment were categorized as C across all assessors, the 60% threshold is met indicating that the responses are robust. In other words, if GPT-4.0 correctly answers a question, it is likely to correctly answer a similar variation of the question again, even if some information is missing.

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Discussion

Principal Results

The hypothesis that ChatGPT is currently unsuitable for self-diagnosis is proved. From each of the 3 addressed assessments with a passing threshold of 60%, GPT-4.0 marginally only passed one. The analysis indicates that GPT-4.0 is generally unclear and incorrect when providing medical information. However, when GPT-4.0 provides correct responses, it remains robust enough to continue answering these questions accurately even when some information is missing. Table 4 summarizes the results of the evaluation procedure for GPT-4.0.

Implications

Recognizing that the GPT-4.0 responses are often ambiguous, its medical advice should be accepted with caution since these responses can vary widely in interpretation depending on the individual. Additionally, since only a small number of responses were found to be genuinely correct, LLMs still require improvement before they can reliably be used in a medical setting. Without these improvements, LLMs risk misinforming individuals.

Even though ChatGPT cannot currently be used for self-diagnosis, substantial evidence indicates that LLMs are continuously improving, suggesting their potential future use in health care systems. As EvalPrompt was also conducted on an earlier GPT-3.5 version, as detailed in Multimedia Appendix 3, the significant difference between the model performances proves that LLMs continue to improve as time progresses.

With ChatGPT being highly acclaimed for its success in passing medical examinations, researchers have proposed using ChatGPT in areas such as medical education and medical report creation [17,20]. However, ChatGPT’s ability to answer examination questions does not inherently equate to genuine medical comprehension and proficiency. Instead, using ChatGPT in these medical settings can undermine health care systems since ChatGPT’s overconfidence can result in misinforming individuals.

Unlike practicing clinicians, ChatGPT lacks formal testing and accreditation for its abilities. It has not undergone accredited medical education or licensing, lacks approval for clinical practice, and has not demonstrated the necessary understanding or skill set to support its claims. While clinicians face severe consequences for errors, such as medical malpractice charges or license revocation, ChatGPT lacks such liability. Thus, relying on ChatGPT before it becomes accurate, consistent, and robust poses a significant risk of misleading health care practitioners and the general public.

Limitations

This study has signified a substantial advancement in the quality of medical advice that LLMs can provide and their potential utility in the health care industry. Although the USMLE questions provide a solid foundation for simulating the self-diagnosis process via LLMs, the prompts contain extensive detail. In other words, an ordinary individual typically would not input paragraphs of information when self-diagnosing and may lack explicit technical health care knowledge. Therefore, future research can explore using a dataset of self-diagnosis questions that contains less detailed information to accurately assess the capability of LLMs. As a result, developing larger and more realistic self-diagnosing datasets can enhance the training of LLMs, in turn improving its performance. Additionally, LLMs are inclined to excel in tasks well-represented in the training data, potentially leading to lower performance in niche problems [17]. This raises equity concerns, as questions concerning underrepresented groups may endure poor performance. Although not explicitly examined in this study, future research could generate datasets with diverse question designs across medical specializations to ensure all fields are represented in training.

Conclusions

While LLMs make headlines for passing medical licensing examinations and are consequently being considered as candidates to train the next generation of health care professionals, it is evident that LLMs’ capabilities are (understandably) modest at this time. More importantly, this misplaced trust in these systems can lead to reliance on their use for self-diagnosis by the public. In constructing EvalPrompt for assessing the capabilities of LLMs in medical contexts, the extent of misinformation inversely correlates to the LLMs’ performance. LLMs that offer unclear and inaccurate responses are more likely to misinform individuals. Ultimately, this investigation presents challenges for machine learning researchers to build more transparent artificial intelligence–powered models capable of reasoning and responding responsibly, while also highlighting the need for a dataset that incorporates more realistic prompt styles.