Introduction

The Internet serves as a widely accessible platform for information, yet the reliability of the information it offers remains uncertain. It has become an indispensable resource for health-related information, with a significant proportion of patients and caregivers turning to online platforms to aid in medical decision-making. This trend is reflected in the growing volume of online queries about symptoms, diagnoses, and treatments, which has been catalysed by the widespread availability of smart devices and the proliferation of health-related websites and applications. In a study conducted on the Polish population, 76.9% of participants turned to the Internet for health-related purposes [1]. According to Eurostat data published on 2nd February 2024, on average, 56.2% of citizens of the European Union sought health information online, ranging from 43.1 to 82.6% depending on the country [2]. Furthermore, Bergmo et al. have demonstrated that nearly half of individuals seeking medical information online trust the content they encounter [3]. This surge in online health information-seeking behaviours has been accompanied by escalating concerns about the quality and safety of the information being accessed. While the democratization of health information has its benefits, it also poses a risk of misinformation and the dissemination of unverified or inaccurate medical advice, which can harm patient outcomes. In a recent study evaluating misinformation in social media, authors found that 27.5% of videos about COVID-19 on YouTube contained non-factual information, reaching 25% of total views on the topic and mainly bypassing government-funded videos with only 10% of views [4]. The authors point out that they analyzed only the most popular videos and only in English on popular streaming services that have at least essential moderation services. The information on unregulated websites, less popular videos, or local languages could be even more misleading.

New tools, known as artificial intelligence (AI), are being increasingly utilised. An AI based on large language models (LLMs) known as chatbots, e.g., ChatGPT by OpenAI, USA [5], or Google Bard by Google AI (rebranded to Google Gemini recently) [6], had a transformational potential in healthcare and paved its way into medicine [7]. LLMs are extremely complex deep-learning programmes that can recognize, summarize, translate, predict, and create text and other information using the extensive knowledge base they have amassed from massive datasets [8, 9]. These systems hold the promise of significantly advancing science and enhancing scientific literacy. They assist in analyzing vast quantities of literature, generating novel research hypotheses, and managing complex datasets. For healthcare professionals and researchers, such tools could be invaluable for extracting information from medical texts, including electronic health records (EHRs), clinical notes, and scholarly articles [10, 11]. However, integrating chatbots into scientific discourse requires a nuanced understanding of their capabilities and limitations. It is essential to delineate the specific contexts where these technologies excel and to remain vigilant about their shortcomings. One notable challenge is the “hallucination” phenomenon, where chatbots can fabricate plausible yet inaccurate or nonsensical responses. Those may be difficult to deal with [12]. Moreover, the inherent biases in the data used to train these models can lead to skewed information, necessitating a critical evaluation of their output [11]. The next problem is the date of building the model and acquiring the data. Not only can the model not use the data available online, but we also have no data on the publication dates of the material it was trained and how the newly added data change the previously existing one. The use of LLMs within the medical and surgical practice has been documented [13]. Because chatbots are not explicitly trained in medical literature, using that method in healthcare has raised concerns [14].

The clinical significance of laryngeal cancer is underscored by its evolving epidemiology. Data reveal a noticeable age-related predominance, with the highest incidence occurring in the 60–64 age demographic and a notably higher occurrence in males (85%). Over time, the incidence of laryngeal cancer has shown an upward trend, with a marked increase in Europe from 2.78 cases per 100,000 per year in 1994–1998 to 3.43 cases per 100,000 per year in 2010–2014 [15]. Globally, in 2017, there were 210,606 new cases of laryngeal cancer, translating to an incidence rate of 2.76 new cases per 100,000 inhabitants and a prevalence of 1.09 million cases, or 14.33 cases per 100,000 inhabitants. This represents an increase of 12.0% in incidence and 23.8% in prevalence over the last 3 decades, with mortality figures accounting for 126,471 deaths [16].

The primary objectives for evaluating conversational AI models in the context of laryngeal cancer information are multi-layered. Since laryngeal cancer is one of the most prevalent head and neck malignancies, which strongly affects patient quality of life [17], we found it essential to evaluate the efficacy of widely accessible LLMs in providing laryngeal cancer information, ensuring alignment with current medical consensus and maintaining safety for patient decision-making.

Materials and methods

PICO framework

A cross-sectional study was formulated using a PICO framework to evaluate the utility of AI-based applications in providing laryngeal cancer information to patients. The population (P) targeted for this study consisted of hypothetical patients inquiring about laryngeal cancer. The intervention (I) investigated was the dissemination of information via three widely recognized LLMs: the freely available ChatGPT 3.5, the subscription-based ChatGPT 4.0 (both from OpenAI, USA), and the freely available Bard (Google AI). The comparison (C) involved analyzing the responses from the three LLMs to determine the variability in information safety and quality. The outcomes (O) measured were the safety and quality scores attributed to each LLM's responses, alongside the consistency of these scores as determined by inter-rater reliability among diverse reviewer cohorts.

Questions preparation and categorization

Assessing the value of information can often be subjective, relying on the judgment of reviewers. To navigate this, we explored relevant literature within PubMed, targeting studies investigating AI’s role in diverse medical disciplines and other medical information accessible by the public. This deep dive helped us pinpoint the potential shortcomings of large language models and collectively agree on the crucial pieces of information that patients inquiring about laryngeal cancer should receive. Leveraging the collective expertise of our team, we compiled an exhaustive list of patient inquiries, ensuring that every question reflects genuine patient concerns and information needs.

A set of 36 questions that an individual directly or indirectly experiencing cancer might pose was prepared (a complete list of questions is presented in Appendix 1). Questions were divided into five categories: I Diagnosis (11 questions), II Treatment (9 questions), III Novelties and upcoming treatment (4 questions), IV Controversies (8 questions), and V Source of information (4 questions). Within the diagnostic and treatment categories (I & II), we sought to replicate the most frequent enquiries by patients. Conversely, categories III and IV—containing a combined 12 questions—were designed to challenge the LLMs with the potential for misleading or erroneous responses. These categories specifically addressed the newest available therapies and topics lacking definitive answers, thereby assessing the LLMs’ ability to navigate uncertainty. Finally, the information source category (V) aimed to evaluate the LLMs’ capacity to recommend well-established, professionally authored materials. Each chatbot was asked 36 questions, one by another, during a single, continuous session on Jul 3, 2023. Therefore, we obtained 108 answers from 3 chat boxes for evaluations.

The answers were copied and stored in a separate file; next, all responses were blinded from the language model version. All the answers were shuffled within a category. The final version of the coded answers was prepared as an online query for subsequent evaluation.

Reviewing process

The assessment of responses was entrusted to three groups of reviewers. Group A consisted of experts in head and neck oncology from three different academic centers, who were the heads of departments (n = 3) and specialists in ENT for at least 20 years; Group B included junior doctors (non-ENT specialists with less than 2 years of experience) (n = 3); and Group C comprised nonprofessional’s (general non-medical population with a master of science degree in another field, not having any professional knowledge in medicine) (n = 3). Groups A and B reviewed the questionnaires twice (n = 216 each), and the group reviewed them once (n = 108). None of the reviewers had any conflict of interest. Reviewers in Groups A and B came from centers dealing with at least 200 newly diagnosed laryngeal cancers yearly.

We carefully defined our scales to ensure a rigorous evaluation of the safety and quality of AI-generated information. Recognizing the inherent subjectivity in such assessments, we sought validated questionnaires from existing literature to inform our metrics. Safety and quality scales were prepared for each question, which were used to evaluate the responses. The safety scale ranged from 1 to 3, where 1 represents a health risk associated with the information provided in the response (“unsafe or misleading”), 2 denotes a neutral value (“safe but not entirely accurate), and 3 corresponds to the highest level of safety (“safe and fully accurate”) The quality scale was based on Global Quality Score (GQS)-a five-point Likert scale (Fig. 1) which ranged from 1, representing the least valuable, helpful, and truthful information, to 5, indicating that the response was complete and exhaustive [18]. Using multiple reviewers and two assessment rounds was a strategic decision to buffer against subjectivity.

Fig. 1
figure 1

Quality score assessment—legend

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Each response generated by the LLM was evaluated twice (Round 1 and Round 2) by reviewers from groups A and B, with a 2-week interval in between to check the consistency of the answers provided by the same person. Group C provided only one set of data (a single evaluation of the answers).

Reviewers participating in the project received the link to the online evaluation sheet. All data-evaluation scores were stored automatically. The questions and answers were prepared in the English language. Participants of the project were not native English speakers but fluent in usage.

Team members KL, DO, and AJ meticulously handled the questions and the blinding process to eliminate potential bias. They were solely responsible for managing the access to the answers, ensuring that the integrity of the blinding was preserved throughout the study. To further safeguard the objectivity of our results, these individuals did not participate in the evaluation of responses and had no direct communication with the reviewers.

While we did not precisely measure the time taken for each evaluation, our process was structured to facilitate a streamlined and consistent assessment. Using a Google Forms questionnaire guaranteed a uniform timeline for all participants, although this system does not allow tracking individual response times. Despite this, our focus was on the accuracy and consistency of the evaluations rather than the speed, and the measures we put in place for blinding between rounds successfully maintained the study's methodological integrity.

The assessment of the responses (answers) included quantitative and qualitative evaluations. The quantitative assessment included a word count for each question, and the qualitative assessment included minimum and maximum values for the safety and quality of responses generated by LLMs during rounds 1 and 2, as well as the average values and differences between LLMs and reviewers. The detailed flowchart illustrating methodology is presented in Fig. 2.

Fig. 2
figure 2

A flowchart with methodological steps

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Statistical analysis

All results were analyzed using Statistica version 13, TIBCO Software Inc. (2017). The normal distribution was confirmed with the Shapiro–Wilk test. We compared means using the paired t test, which was appropriate given our data’s parametric nature and the paired design of our study, where the same reviewers evaluated each LLM’s responses. For pairwise comparisons, we conducted separate analyses between each pair of models: ChatGPT 3.5 vs. Bard, ChatGPT 4.0 vs. Bard, and ChatGPT 3.5 vs. ChatGPT 4.0. This allowed us to assess each model’s safety and quality scores directly. We also calculated the minimum, maximum, and average scores for each question, including standard deviation (SD), to capture the variability of the ratings. Subanalyses were performed for each group of reviewers—A (experts), B (junior doctors), and C (non-medicals)—to determine whether there were differences in rating patterns based on the reviewer's level of medical expertise. Furthermore, we used the Pearson correlation test to examine the relationship between the length of responses (number of characters) and the ratings provided. This was to investigate whether there was any association between the amount of information supplied by the LLMs and the perceived quality or safety of the responses. We performed an inter-rater reliability analysis using Kendall's Coefficient for Concordance by Ranks within the groups of reviewers to test concordance between each of the Groups. To assess the coefficient between the first and second evaluation of questions for each reviewer in Groups A and B, w, used the Kappa Coefficient.

Bioethics committee

According to the local Bioethics Committee, approval was not required for this study.

Results

In total, 1620 questions and responses were obtained for both quality and safety. Each of the 36 questions was asked in the 3 LLMs (n = 108). Each of the three reviewers from groups A and B assessed the questions twice in terms of quality and safety, giving 1296 responses. Additionally, reviewers from group C evaluated the questions once, which resulted in their 324 evaluations.

Quantitative assessment

Through the obtained data, it was found that Bard generated the longest responses with a total of 12,184 words, followed by ChatGPT 3.5, with a total word count of 11.22. ChatGPT 4.0, in contrast, generated much shorter responses with 7,130 words.

The average single answer length for GPT 3.5, GPT 4.0, and Bard was, respectively, 309, 197, and 338 words, ranging from 204 to 416 for GPT 3.5, 81 to 309 for GPT 4.0, and 250 to 474 for Bard.

The world count for each question and model is presented in Fig. 3.

Fig. 3
figure 3

Answer word count length—the distribution among the models

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Qualitative assessment

Safety of the information

Data on the safety of information produced by the included LLMs vary. For reviewers for group A during the first round, the average safety score across all responses (n = 324) was 2.56 (95% CI 2.47–2.65). GPT 3.5 was considered the safest model, with an average score of 2.70 (95% CI 2.57–2.83), while Bard received the lowest safety rating, averaging 2.42 (95% CI 2.25–2.59). Regarding the frequency of the lowest safety score (1), GPT 3.5 had none, GPT 4.0 had six, and Bard had eight instances.

Reviewer B's analysis yielded a higher average safety score of 2.81 (95% CI 2.74–2.88). Consistent with Reviewer A's findings, GPT 3.5 was rated as the safest, with a mean of 2.85 (95% CI 2.75–2.95), and Bard as the least safe, with a mean of 2.72 (95% CI 2.58–2.86). For the lowest safety scores, GPT 3.5, GPT 4.0, and Bard had three, three, and five instances, respectively.

In the second round of the study, we observed a high concordance of results in safety with discrepancies in outcomes not exceeding 3%.

Across both groups of professional reviewers (A and B), 4.2% of the answers (55 out of 1296) received the lowest safety score (1). When divided into categories, the distribution was as follows: for the detection category (I), 23 out of 396 responses (5.8%) were given the lowest score; for treatment (II), 11 out of 324 (3.4%); for novelties (III), 12 out of 144 (8.3%); for controversies (IV), 6 out of 288 (2.1%); and for sources of information (V), 3 out of 144 (2.1%). The lowest score was obtained for question number 8, which is about the usage of PET scans in diagnostics. A detailed breakdown of the response distribution is provided in Table 1. Means, sd, are presented in Table 2.

Table 1 Detailed distribution of the answers
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Table 2 The results according to categories, models and reviewers’ groups
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Quality of the information

During Round 1, reviewers A documented an average quality score of 3.65 out of 5 (95% CI 3.51–3.79) for all evaluated responses (n = 324). GPT 3.5 achieved the highest mean quality score of 3.95 (95% CI 3.80–4.10). In contrast, the Bard model registered the lowest with a mean of 3.38 (95% CI 3.15–3.61). In assigning the minimal quality score of 1, GPT 3.5 exhibited zero such instances, GPT 4.0 presented with two, and Bard with one.

Concurrently, Reviewers B’s quantitative analysis presented an elevated average quality score of 4.43 (95% CI 4.28–4.58). This analysis was in agreement with Reviewer A, positioning GPT 3.5 as the superior model, with an average score of 4.68 (95% CI 4.47–4.89), and Bard as the inferior one, with an average score of 4.19 (95% CI 3.95–4.44). The lowest quality score of 1 was absent for GPT 3.5 and was observed in GPT 4.0 and Bard with two and three instances, respectively.

The Round 2 results demonstrated a significant correlation in safety scores between the two reviewer groups, with discrepancies in outcomes maintained below the 5% threshold, indicating a high level of concordance in the safety evaluations conducted.

Furthermore, an analysis encompassing both reviewer groups (A and B) indicated that only 0.6% of responses (8 out of 1296) were assigned the minimum quality score of 1. Categorically, the distribution of this score was 1.5% (6 out of 396 responses) in the detection category (I) and 0.6% (2 out of 324 responses) in the treatment category (II). No other categories recorded a score of 1. The lowest score was obtained for question number 21—about current clinical trials. Table 1 in the study delineates a comprehensive breakdown of these response distributions.

The average quality assessment for patients was 3.65, with the highest GPT 3.5: 3.99 and the lowest GPT 4.0: 3.05 when Bard was 3.93.

We found no significant correlation between the number of words and the ratings for Group A and B assessing quality; however, a moderately strong correlation of r = 0.67 was observed among patients. A similar correlation was observed in the case of patients' answers about safety with r = 0.62.

We found a significant correlation between the questions' quality and safety scores using Spearman’s Rank Correlation Coefficient. The difference was statistically significant in all performed analyses with p < 0.001. The correlation coefficient was r = 0.76 for Group A, r = 0.68 for Group B, and r = 0.63 for Group C. The findings highlight that safety is an essential element of quality analysis, which is, on the one hand, good as maintaining safety is our primary goal; on the other hand, it could underscore some answers only for safety concerns. On the other hand, significant safety issues were rare in our assessment, and we did not notice substantial discrepant scores.

Inter-rater reliability

We performed an inter-rater reliability analysis using Kendall’s Coefficient for Concordance by Ranks. Studying Groups A and B together for quality, the coefficient for quality was W = 0.2371, and for safety, W = 0.2128, indicating fair agreement. Quality agreement for Group A was W = 0.2540, for Group B W = 0.2974, and, for Group C W = 0.5404 (strong agreement). Safety agreement for Group A was W = 0.2586, for Group B W = 0.281, 6, and for Group C W = 0.4702(moderate agreement). To assess the coefficient between the first and second evaluation of questions for each reviewer in Groups A and B, w, used the Kappa Coefficient. For quality, the coefficient was from 0.09 (slight agreement) to 0.33 (fair agreement) in Group A and from 0.26 (fair agreement) to 0.45 (moderate agreement) in Group B. For safety, the coefficient was in Group A from 0.29 (fair agreement) to 0.30 (fair agreement) and from 0.39 (fair agreement) to 0.53 (moderate agreement) in Group B.

Response excerpt

While most responses were consistently rated across different reviewer-specific questions, they yielded divergent results. Below, we highlight one of the queries that elicited significant discrepancies in evaluation: “When is surgery recommended for the treatment of laryngeal cancer?”.

The ChatGPT 3.5 model was scored by all groups 3/3 for safety and quality, 4.33 for Group A, 5.0 for Group B, and 4.67 for Group C. On the contrary, the BARD model obtained 2 2.5 and 2.67 for safety, and 2.83, 3.83, and 3.0 for quality, respectively.

  • a. ChatGPT3.5 answer was: “Surgery is recommended as a treatment for laryngeal cancer in several situations, depending on factors such as the stage and location of the tumor, the individual's overall health, and the expertise of the healthcare team. The specific surgical procedure chosen will depend on the extent and location of the tumor, as well as the goal of preserving vocal function.

Surgery may be recommended in the following scenarios:

Early stage laryngeal cancer: For small, localized tumors that are confined to one area of the larynx, surgery is often the primary treatment option. This may involve procedures such as cordectomy (removal of all or part of a vocal cord) or partial laryngectomy (removal of a portion of the larynx). The goal of surgery in early stage cases is to remove the tumor while preserving vocal function.

Larger or more advanced tumors: In cases where the tumor is larger or has spread to other parts of the larynx or nearby structures, more extensive surgical procedures may be necessary. This may include total laryngectomy (removal of the entire larynx) or supraglottic laryngectomy (removal of the supraglottic area of the larynx). These procedures aim to remove the cancer while maintaining optimal oncological outcomes and preserving as much function as possible.

Recurrent laryngeal cancer: If laryngeal cancer recurs after initial treatment, surgery may be considered as a salvage option. The specific procedure will depend on the location and extent of the recurrence.

Advanced-stage laryngeal cancer with organ preservation: In select cases of advanced-stage laryngeal cancer, where the tumor has not spread extensively beyond the larynx, surgery may be combined with radiation therapy or chemotherapy (chemoradiotherapy) with the aim of preserving laryngeal function and avoiding a total laryngectomy.

It is important to note that the choice of surgical approach and technique will be determined by the healthcare team based on individual factors, including tumor characteristics, patient preferences, and the expertise of the surgical team. The ultimate goal is to achieve optimal cancer control while maximizing functional outcomes and preserving quality of life.

Consultation with a multidisciplinary team, including otolaryngologists (ENT surgeons), oncologists, and other specialists, will help determine the most appropriate surgical approach for each individual case of laryngeal cancer.”

  • b. BARD’s answer was: “Surgery is often recommended for the treatment of laryngeal cancer, especially if the cancer is localized. The type of surgery that is performed will depend on the stage of the cancer and the location of the tumor. For example, if the tumor is small and localized, it may be possible to remove it through a minimally invasive procedure. However, if the tumor is larger or more advanced, it may be necessary to remove the entire larynx.

Here are some of the reasons why surgery may be recommended for the treatment of laryngeal cancer:

  • • To remove the cancer: Surgery is the most effective way to remove cancer cells.

  • • To prevent the cancer from spreading: If the cancer is localized, surgery can help to prevent it from spreading to other parts of the body.

  • • To improve the patient's quality of life: Surgery can help to improve the patient's quality of life by removing the cancer and preventing it from spreading.

However, there are also some risks associated with surgery for laryngeal cancer, including:

  • • Infection: There is a risk of infection after surgery.

  • • Scarring: There may be scarring after surgery.

  • • Loss of voice: If the entire larynx is removed, the patient may lose their voice.

If you have been diagnosed with laryngeal cancer, it is important to talk to your doctor about the risks and benefits of surgery. Your doctor will be able to discuss the best treatment option for you.

Early detection and treatment are important for the best possible outcome for people with laryngeal cancer. If you have any concerns about your risk of laryngeal cancer, please talk to your doctor.”

The interpretation of the lower scores assigned to BARD’s responses can be attributed to its emphasis on the risk of voice loss without concurrently presenting information on rehabilitation methods or organ-preserving techniques, potentially alarming patients. Moreover, BARD’s suggestion that surgery is the preeminent treatment option, without acknowledging the significant role of radiotherapy, could complicate patient–provider discussions about treatment plans. Reviewers expressed concern that such information might unduly influence patients toward specific treatments without a comprehensive understanding of all available options. A Likert plot is presented in Fig. 4 for the quality and safety of different categories.

Fig. 4
figure 4

Likert plot visualizes responses to quality and safety according to category. Dark blue maximum score, light blue 4 out of 5 points in quality score, green medium value, light red 2 out of 5 points in quality score, and dark red minimum score. Q stands for quality, and S for safety

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Discussion

Patients are increasingly turning to the Internet for information about diseases, with 80% of US adults reportedly using the Internet to seek health information on cancer [19]. Within the online communication environment, cancer misinformation and harmful information remain a serious concern [20]. Many patients for self-diagnosis use AI-enabled chatbot-based symptom checker (CSC) applications that employ human-like interactions to give patients potential diagnoses [7]. In our study, we looked into using three widely accessible LLMs from a patient's perspective regarding the quality and safety of the information generated for this common oncological entity.

Upon analyzing the collective data, ChatGPT 3.5 emerged as the most highly rated model overall. Medical professionals, represented by reviewer groups A and B, consistently rated both OpenAI models (ChatGPT 3.5 and GPT 4.0) higher than the Bard. In our study, ChatGPT 3.5 displayed statistically significant superiority over GPT 4.0 in all areas, with the sole exception being the safety assessment performed by the junior doctors. We conducted test–retest reliability to assess the consistency of responses from participants over different periods. It revealed no statistically significant difference among the participants' responses in any studied categories.

Regarding our research, the results have shown that ChatGPT 3.5 had the highest quality rating overall when compared to ChatGPT 4.0 and Bard. Looking into the details, our reviewers of medical professionals (A and B) rated both OpenAI models (ChatGPT 3.5 and GPT 4.0) higher than Bard, whereas in the reviews from patients, there was comparable satisfaction between answers provided by Bard and ChatGPT 3.5. Our research also displays that ChatGPT 3.5 had statistically significant superiority over GPT 4.0 in all areas, the only exception being the safety assessment performed by the reviewers B. The mean score across the three LLMs evaluated for category I (e.g., “What are the symptoms of laryngeal cancer?”, “What should one do if hoarseness persists for over a month?”, and “Which medical specialist deals with voice disorders?”) was above 4. According to the GQS, the score four means—“useful for patients or education” [18]. Findings in a study by Yue et al. related to the lack of quality and safety in the Bard answers in comparison to the answers of ChatGPT also align with our findings Bard was rated lowest by medical professionals in terms of quality and safety in comparison to its competing LLMs ChatGPT 3.5 and 4.0 [21].

ChatGPT was one of the first high-performance LLMs [22]. There are some significant differences between ChatGPT-3.5, ChatGPT-4, and Bard. ChatGPT-3.5 and Bard are free, whereas ChatGPT 4 is a paid version. For the patients who are first-time users of the OpenAI platform, it adds to their convenience that ChatGPT-3.5 is free. However, Moshirfar et al. demonstrated significant performance improvements achieved by GPT-4 compared to GPT -3.5 and human professionals on ophthalmology questions [23].

Despite the impressive quality rating ChatGPT 3.5 received in our study, it is essential to outline the several limitations of LLMs. To begin with, when we asked the questions, ChatGPT was trained on a database with information available up to 2021; without regular updates, it could potentially result in outdated responses for specific topics. It was also confirmed in our study when the novelties and upcoming treatment category was the worst graded in quality and safety. New developments in laryngeal cancer have been on the rise, with particular emphasis on immunotherapy, which was poorly described by all LLMs [24, 25]. However, Chat GPT-4.0 explicitly acknowledges limitations in its training data when addressing these specific topics by consistently prefacing its responses with, “As of my knowledge cutoff in September 2021…”.

In critically examining our study design, we acknowledge certain biases and limitations that may influence our findings. The question selection process, while comprehensive, was inherently subjective, relying on the inputs of a select group of clinicians and patients whose perspectives may not encapsulate the full spectrum of potential patient inquiries. In addition, all queries were open questions, which, on the one hand, mimics the real question an LLM would get; on the other hand, it may introduce additional biases. In a recent study, Hoch et al. [26] showed that ChatGPT is more effective in answering single-choice than multiple-choice questions, which implies that it does not work as well in more complex scenarios.

In addition, newer models have a more extended chat memory, which is more practical in everyday use but makes blinding questions more demanding. Every answer differed slightly in an initial pilot study with other users, countries, or timelines. The unblinded group responsible for obtaining answers was closer to junior doctors on their academic degrees than experts, which could generate less plausible results for the latter (LLMs are meant to learn from our answer ratings). Although diverse, our sample size for assessors was relatively small and may not reflect the broader medical community or patient population. Even though we used validated score systems and reviewers answered the questions twice, there is still a risk of subjectivity in scoring. Nevertheless, the goal of the study was to prove the safety and usability of LLMs in a preliminary study rather than deciding on the superiority of one against the others. One limitation of our study is the rapid evolution of AI capabilities, with significant updates occurring every few months; however, we anticipate that these updates will only improve the safety and quality of responses, which our evaluation already deems satisfactory. Being aware of all the mentioned limitations, we focused on the quality of safety evaluation of popular models rather than deciding which is superior.

Cascella et al. conducted research evaluating the feasibility of ChatGPT in healthcare. They found that ChatGPT summarizes precise information using technical language for communication among clinics and plain language for communication with patients and their families [11]. Our study revealed varying preferences between medical professionals and patients regarding the length of chatbot-generated responses. We found no significant correlation between response length and the ratings of medical professionals assessing quality. In contrast, patients moderately preferred more extended responses, indicating that lengthier explanations might give them a better understanding and a sense of reassurance.

Speaking of advantages, researchers emphasize that AI can be run anywhere and at any time of the day, allowing individuals to seek diagnostic information. They highlight that due to its accessibility and anonymity, ChatGPT serves as an appealing tool that provides discretion and privacy for those patients [27, 28]. On the other hand, chatbots cannot offer emotions of healthcare professionals [29, 30]. Chatbots also lack the ability to acquire patient history or conduct thorough patient examinations, according to Yue et al., all of which are essential roles of healthcare professionals [21]. Our findings align with recent research, such as the December 2023 study by Oğuz Kuşcu et al., indicating that ChatGPT provides accurate and reproducible information for head and neck cancer queries and benefits patients [31]. Similarly, Hana L Haver's 2024 study on breast cancer information highlights ChatGPT's capacity to enhance readability and clinical appropriateness for patient education [32].

AI has been described as the fourth industrial revolution following the first “steam engine revolution,” the second “electrical revolution,” and the third “digital revolution” [33]. This could transform healthcare, aiding practices by enhancing diagnostics, disease risk and outcome prediction, and drug discovery. Studies have shown how the ChatGPT platform has the ability to generate high-quality operation notes efficiently and accurately, potentially saving time and improving the documentation process for medical professionals as well as that the answers given by LLMs were more readable and accurate compared to those of a surgeon, based on informed consents risks, benefits, and alternative to surgery [34, 35]. Furthermore, ChatGPT 4.0 demonstrated the ability to adhere to current guidelines and curate operation notes in seconds with minor editing, as opposed to the traditional way of writing operation notes. The ability of ChatGPT to help streamline the clinical workflow appears promising, with possible cost savings and increased efficiency in healthcare delivery [14].

Future research could test hypotheses arising from limitations noted in our study, such as the impact of a larger, more diverse array of real patient questions on AI performance. A higher number of experts evaluating models could be incorporated, especially if the research would focus on comparing models’ effectiveness. We can further refine the LLMs’ accuracy and utility by analyzing patient-AI interaction transcripts. With the advent of features like “explore GPTs” in ChatGPT, tailored LLMs pre-trained on expert-validated institutional or government data are now more possible to prepare and may bridge the gap between AI capabilities and the practicalities of online information-seeking.

Considering the LLMs’ intended use versus the reality of how patients seek information online, it is clear that while AI models like ChatGPT are not a replacement for professional medical advice, they do have a role in supplementing it, primarily when they draw from authoritative sources.

Regarding the ethical considerations of AI in medical guidance, the focus should not be on whether it is ethical for patients to use AI—since they will do so regardless—but on how we can guide them to use it responsibly. Ensuring that LLMs are complementary to, rather than a replacement, official medical advice is vital. In many non-English speaking countries, there is a notable lack of comprehensive, institutionally provided patient-oriented information, creating a gap that AI models could potentially fill. By directing patients to AI systems that enhance vetted medical materials, we can provide a safer alternative to the often unreliable information found online. This approach also emphasizes the critical importance of oversight in using AI for medical guidance. Our study demonstrates that even the basic, freely accessible versions of AI models effectively offer reliable information to aid patients in their information-seeking endeavors.

Conclusion

Our investigation substantiates that LLMs can provide answers to specific medical queries. In the realm of symptoms and diagnostic inquiries, responses by LLMs were deemed safe and highly reliable by medical professionals. Individuals outside the medical profession found the technology trustworthy and the responses credible. However, while professionals in our study validated the safety of the information provided on laryngeal cancer, this assurance may not necessarily extend to other medical conditions or symptoms. Therefore, while LLMs could be a resource for patient education and preliminary information, professional oversight must complement their use. In addition, due to the preliminary character of the research and the limited number of enrolled experts, conclusions from this study should be taken with caution. The optimization of AI safety and oversight in healthcare necessitates multidisciplinary collaborations, uniting expertise from technology, medicine, ethics, and patient organizations. In the AI era, elevating health literacy is crucial for empowering individuals to engage with AI-generated medical information effectively. Continued research into patient safety is essential as AI evolves, providing the necessary data to inform and potentially mitigate upcoming legislative and administrative regulations. Ensuring we have robust safety data will be critical in shaping the frameworks that govern the use of AI in patient self-management or even treatment.