When Artificial İntelligence Speaks For The Obstetrician: Multilingual Accuracy On Real Patient Questions

When Artificial İntelligence Speaks For The Obstetrician: Multilingual Accuracy On Real Patient Questions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article When Artificial İntelligence Speaks For The Obstetrician: Multilingual Accuracy On Real Patient Questions Alihan Tığlı, Rulin Deniz, Sefer Üstebay, Muammer Hayri Bektaş, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7425784/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The use of artificial intelligence technologies, particularly Generative Large Language Models (LLMs), in the health sector is rapidly growing. These models offer a new level of health counselling by enabling patients to access information more easily. However, there is limited comprehensive data on the accuracy and clarity of responses from different LLMs in various languages, as well as on the reliability of the scientific references they provide. This study aims to compare the performance and quality of references of three free, open-access LLMs (ChatGPT, Google Gemini, DeepSeek) in both Turkish and English, focusing on frequently asked questions related to pregnancy. The goal is to enhance digital health literacy and to assess the effectiveness and limitations of artificial intelligence-supported health tools. Methods In this comparative, observational, and descriptive study, the 14 most common pregnancy questions encountered in the clinic and among the patient population were identified. These questions were posed to three different LLMs (ChatGPT, Gemini, DeepSeek) in both Turkish and English, with instructions to respond using a web extension from an up-to-date, scientific, and reliable source. The initial answers provided by the models were evaluated by an independent team of obstetricians and gynaecologists, with the origin of the model concealed. The references accompanying the answers were assessed for reliability, scientific validity, and accessibility by a separate team of specialised physicians. The data collected were analysed statistically. Results It has been shown that language and model infrastructure play a significant role in the performance of LLMs. It was found that Google Gemini and DeepSeek's answers to English questions achieved statistically higher accuracy scores than their answers to Turkish questions (p < 0.05). In ChatGPT, no general accuracy difference was observed between answers to Turkish and English questions. Among LLMs, Google Gemini and DeepSeek were statistically more accurate than ChatGPT when questions were asked in English (p 0.05). Regarding reference reliability, ChatGPT achieved a significantly higher "perfect reference" rate than the other models in the references of its answers to both Turkish (78.5%) and English (71.4%) questions. Conclusions This study shows that LLMs can be used for general information about pregnancy, but they have significant limitations in terms of language competence and reference reliability. In particular, it was found that the response quality of LLMs may suffer for non-native English speakers and that the references provided may not always be reliable. The data obtained emphasise that LLMs alone are not a reliable tool in situations requiring patient-based and clinical decisions. In the future, training LLMs specifically for native languages, strengthening reference validation algorithms and developing systems that integrate physician supervision are critical to maximise the potential of AI in healthcare. In this context, AI should be positioned as a tool to support patient care under the supervision of physicians rather than replacing them. Biological sciences/Computational biology and bioinformatics Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research Artificial intelligence pregnancy LLM ChatGPT DeepSeek Gemini Figures Figure 1 Figure 2 Introduction The integration of Artificial Intelligence (AI) into the medical sector signals major change and progress, creating new opportunities for healthcare 1 . This integration could enhance medicine and improve diagnosis and treatment processes 2 . However, the rapid development and widespread use of AI have also raised concerns about the lack of oversight and regulation. Authorities advise paying attention to the potential weaknesses and limitations of AI in medicine, including challenges across ethical, legal, regulatory, methodological, and technical aspects areas 3 . They also expressed concern that the methods and approaches used in the development of AI are not as rigorous and robust as those in conventional medicine interventions 4 . Despite all these reservations, Generative Large Language Models (LLMs), which have emerged as one of the most innovative AI applications, hold great promise for bringing about significant changes in many aspects of healthcare 5 . These models can assist healthcare professionals in clinical decision-making during diagnosis and treatment, as well as help answer patients' health-related questions and optimise patient care 6 , 7 . A healthy pregnancy typically lasts around 40 weeks from the last menstrual period and usually concludes with the birth of the foetus. During this period, numerous physiological and psychological changes occur in the mother to support healthy foetus growth and development. These changes can make an already complex process even more challenging for women. Pregnant women are most curious about what actions are necessary during follow-up and which changes are considered normal throughout pregnancy. Information from social media, friends, and family on certain matters can increase confusion during this time. Regardless of a country's level of development, women do not always have immediate access to specialised obstetric care. Furthermore, differing opinions among health professionals can make it difficult for women to clarify their concerns. As a result, many women now frequently turn to LLMs to address their questions and curiosities. The advancements made by LLMs in health-related fields in recent years are significant 8 . Patients' use of LLMs for both diagnostic and informational purposes for various health problems and everyday questions has remained a subject of research over the years 9 , 10 . Although LLMs currently have limited abilities in diagnosis and treatment, it is predicted that they will quickly bridge this gap in the future 11 . A recent study showed that LLM outperformed human candidates in a clinical assessment exam in obstetrics and gynaecology, and that the examiners could not distinguish which of the answers given was human or AI 12 . However, although LLMs are particularly reliable for general patient information and common questions, it is known that they still fall short in providing accurate details regarding individual patient differences 13 . Many LLM studies in the literature are usually conducted using a single infrastructure (ChatGPT). With advancing AI technology, the utilisation of different LLMs is also increasing nowadays. Additionally, the view that LLMs have more extensive infrastructure in English, the native language of LLMs, indicates that access to information in other languages is more limited. Comprehensive and systematic studies that directly compare the multilingual responses of various open access LLM models to frequently asked questions about the pregnancy process are very limited in the literature. This gap prevents both patients from accessing accurate information and healthcare professionals from safely utilising LLM-based digital counselling tools. The main aim of this study is to compare the quality of answers provided by three different open access LLMs (ChatGPT, Deepseek, Google Gemini) to frequently asked questions about pregnancy from a multilingual perspective. In particular, it seeks to evaluate how these models respond in both Turkish and English in terms of scientific accuracy. comprehensibility and timeliness. In this study, the extent to which artificial intelligence-based chatbots—frequently used by users seeking information about pregnancy—provide reliable and understandable information in different languages will be analysed. Therefore, the effectiveness and limitations of artificial intelligence-supported tools in enabling both Turkish and English-speaking patients to access accurate and up-to-date information will be revealed. Additionally, the reliability, accessibility, and accuracy of the references from which these three open access LLMs obtained scientific information were examined. This aims to provide new and original data on the effectiveness of LLM-based health counselling practices in multilingual environments. The results are intended to contribute to the development of digital health literacy for both patients and health professionals and to enhance access to reliable information. Materials and Methods a. Creating a Question Pool With the joint decision of the obstericians participating in the study, the 14 most frequently asked questions about pregnancy by patients were determined in the light of literature review and clinical experience. The questions were prepared both in Turkish and English (Figure 1). All prepared questions were asked sequentially in both Turkish and English, using the same format with all three LLMs (Figure 2). b. Selection of large language models As open access, three different large language models that are most frequently used in our country were selected: ChatGPT (OpenAI), Google Gemini, Deepseek. Each model was asked the same questions in both Turkish and English in the same format. They were also asked to provide a reference from the most reliable and up-to-date scientific source for each answer and to provide the web extension of the reference. The initial answers and references given by the models were recorded; no additional explanations or corrections were requested. c. Evaluation of Responses Evaluation criteria: -Evaluation of the correctness of the answer: LLMs were coded as LLM-1, LLM-2, LLM-3 to prevent identification of which LLM provided which answer. The accuracy of the responses to the questions posed to the three LLMs in Turkish and English was evaluated by a total of eight obstetricians and gynaecologists, comprising one professor, two associate professors, three assistant professors, and two specialists with proficient command of Turkish and English according to current literature. The evaluators were asked to assess both Turkish and English answers independently and without prior knowledge of each other's assessments, scoring them according to the table below. 4 Excellent Answer: Complete correct answer 3 Correct Answer: The answer is correct, but there are small information gaps of no serious importance. 2 Partially Correct Answer: The answer is generally correct but there are important information gaps 1 Wrong Answer: Answer containing completely false and misleading information -Assessment of the references on which the answer is based: The references on which the answers to the questions posed to the three LLMs in both Turkish and English were based were evaluated and noted by consensus by 1 obstetrician and gynaecologist, 1 paediatrician and 1 anaesthetist with good command of Turkish and English according to the following table. 4 Excellent Reference : The reference on which the answer is based is accurate, up-to-date, scientifically valid, and the web extension is active. 3 Correct Reference: The reference on which the answer is based is correct, up-to-date, scientific, but broken from the web extension. 2 Useless Reference: The reference on which the answer is based is accessed, but the reference is not a scientific source. 1 Incorrect reference: No reference is given or the reference is completely wrong. Ethics committee approval This study does not involve data collection from human participants and does not include any experimental intervention; therefore, ethical committee approval is not required. Type (Design) of the Study This study is comparative, observational and descriptive. Data Analysis The data were analysed using the SPSS 23.0 statistical software programme. Percentage, mean, and standard deviation were utilised as descriptive statistics in data analysis. The conformity of continuous variables to a normal distribution was assessed using the Kolmogorov-Smirnov test. The Mann-Whitney U test, one of the nonparametric tests, was employed to compare the expert evaluation scores for responses provided in Turkish and English. The Kruskal-Wallis H test was used to compare the expert evaluation scores for answers given to questions directed at ChatGPT, Gemini, and DeepSeek artificial intelligence language models, while Dunn's multiple comparison test was applied for inter-group comparisons. The significance level was set at p<0.05 for all statistical tests. Results Table 1 presents expert evaluations of responses from different LLMs to frequently asked questions in the clinic. The percentage of experts who judged the Turkish answers provided by the ChatGPT to the 14 most common questions as complete or perfect is 75.0% for question 10, but it is 0.0% for questions 2, 4, 5, and 8. For the English answers from ChatGPT, 75.0% of experts considered the response to question 9 as complete or perfect, whereas only 12.5% did so for questions 2, 3, 8, 10, 11, 12, and 13. When evaluating the Turkish answers from the Gemini, 87.5% of experts judged responses to questions 9 and 13 as complete or perfect, but 0.0% for questions 3 and 4. For the English answers from Gemini, 100.0% of experts regarded the responses to question 9 as complete or perfect, with only 12.5% doing the same for question 3. Regarding the Deepseek, 100.0% of experts rated the Turkish answer to question 2 as complete or perfect, but 0.0% for questions 8, 10, 11, 12, and 13. Finally, for the English answers from Gemini, 100.0% of experts considered the responses to questions 12 and 3 as complete or perfect, while 37.5% regarded the response to question 4 as such (Table 1). Table 2 shows the comparison of expert evaluations for the answers of different artificial intelligence language models to frequently asked questions in the clinic. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13 and 14 of the ChatGPT, the mean expert evaluation score of the Turkish answer to questions 10 (3.75±0.46) and 12 (3.62±0.51) was statistically significantly higher than the English answer (p0.05) between the mean expert evaluation scores for Turkish (3.13±0.26) and English (3.25±0.46) answers to all 14 questions of the ChatGPT. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 5, 6, 7, 8, 9, 10, 11, 13 and 14, the mean expert evaluation scores of the English answers to questions 2 (3.87±0.35), 3 (3.12±0.35), 4 (3.75±0.46), 12 (3.87±0.35) were statistically significantly higher than the Turkish answers (p<0.05). The mean expert evaluation scores for the English answers (3.74±0.21) were statistically significantly higher (p<0.05) than the Turkish answers (3.39±0.33) to all 14 questions directed to Gemini. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 2, 4, 5, 6, 7, 8, 10 and 14, there was no difference between the mean expert evaluation scores of the 3rd (4.00±0.00), 9th (3.87±0.35), 11th (3.62±0.51), 12th (3.75±0.46) and 13th (3.75±0.46) questions. (3.62±0.51), 12th (3.75±0.46) and 13th (3.75±0.46) questions in English is statistically significantly higher than the Turkish answer (p<0.05). The mean expert evaluation scores for English answers (3.70±0.28) were statistically significantly higher (p<0.05) compared to Turkish answers (3.24±0.36) for all 14 questions asked to DeepSeek. The expert evaluation scores for the Turkish answers to the questions directed to ChatGPT, Gemini and DeepSeek are statistically significantly different between questions 2, 4, 5, 6, 8, 9, 10, 12, 13, 14 and the expert evaluation scores for the English answers are statistically significantly different between questions 1, 2, 3, 8, 10, 11, 12, 13, and 14 (p0.05). The mean expert evaluation scores of Gemini and DeepSeek for the responses given in English are statistically significantly higher than the mean expert evaluation score of ChatGPT for the responses given in English (p0.05, Table 2). ChatGPT provided excellent references for 78.5% (n=11) of the Turkish questions, which were accurate, up-to-date, scientific, and included an active web extension. It offered excellent references for 71.4% (n=10) of the questions in English. The percentage of excellent references for questions in both languages was similar and was the highest among all LLM applications. Gemini provided perfect references for only 21.5% (n=3) of Turkish questions and 64.3% (n=9) of English questions, although its web extension was inactive. For English questions, 64.3% (n=9) had perfect references, which is notably higher than in Turkish. DeepSeek exhibited lower rates of perfect references in both languages compared to the other two LLMs. While DeepSeek achieved 21.5% (n=3) perfect references for Turkish questions, this decreased to 14.3% (n=2) for English questions (Table 3). Discussion In this study, we compared the response performance of three different open access Large Language Models (LLMs) (ChatGPT, Google Gemini, DeepSeek) in both Turkish and English to 14 frequently asked questions in pregnancy-related outpatient clinics, as well as the reliability of the references on which the answers were based. It demonstrates that the effectiveness of LLMs in obstetric clinical knowledge transfer depends on multiple factors such as language, model infrastructure, and reference accuracy. Additionally, this study emphasises the importance of not only the accuracy of the information provided by LLMs but also the significance of the references supporting that information. It is evident that information sourced from non-scientific references will not be reliable, even if it is correct today. LLMs should continue to be developed to utilise scientific references, particularly in patient information. Response Quality and Language Factor It has been revealed that there are significant differences between the languages used in evaluating the response quality of LLMs. Although the number of studies comparing different languages in the literature is quite limited, ChatGPT's performance has generally been assessed. In a study comparing Arabic and English, it was observed that LLMs provided more accurate answers to questions in English 14 . Similarly, French and Dutch performed considerably worse than English in the ChatGPT study 15 , 16 . ChatGPT has also been shown to perform less well in Japanese than in English 17 . Although fewer in number, there are also studies that have obtained different results. Two studies from Poland and Brazil demonstrated that ChatGPT delivered comparable performance to English when answering medical questions posed in Polish and Portuguese 18 , 19 . In our study, especially Gemini and DeepSeek LLMs received statistically higher expert judgement scores for responses in English than in Turkish. This indicates that training LLMs mainly with English data sets results in language-related differences in response quality. It also implies that English sources may dominate the training data of these models. In ChatGPT, no overall score difference was observed between Turkish and English responses. Remarkably, in some specific questions (e.g., immunisations, tooth extraction), Turkish responses performed better. This suggests that some medical topics might be communicated more accurately based on local health policies and resources. Furthermore, because ChatGPT is the most well-known and widely used LLM in our country, its infrastructure for Turkish questions might be more robust. When considering the existing literature, it is generally observed that the response quality of LLMs decreases in languages other than English. This makes it harder for women whose first language is not English and who do not speak English to access accurate information. Therefore, it is essential that all LLMs expand their databases in the native languages of their users. Model Based Comparisons Studies comparing LLMs with each other are quite scarce in the existing literature. In one study, a comparison between ChatGPT, Gemini, and Med Go LLMs using only English questions across nine different disciplines found that Gemini exhibited the lowest performance 20 . A study on dentistry included several artificial intelligence models, and the results showed that although ChatGPT-4 performed statistically better than ChatGPT-3.5, Bing Chat, and Bard, its current limitations could potentially lead to harmful healthcare decisions if not used properly carefully 21 . In our study, although it responded to some questions with notable deficiencies, it was observed that no artificial intelligence model provided an answer that was significantly distant from being direct and misleading to the patient in both languages. Although there were differences on a question-by-question basis, generally, when the questions were posed in Turkish, Gemini showed the highest average performance, while the lowest average was seen in the answers of ChatGPT. However, no statistically significant difference was identified. When the same questions were posed in English, the answer performance of Gemini and Deepseek was statistically significantly higher than that of ChatGPT. It was observed that asking questions in English to these two LLMs increased the likelihood of reaching the correct information. Considering that ChatGPT studies are predominantly conducted in the literature, we believe that employing other LLMs in similar studies would be beneficial for evaluating artificial intelligence performance. Importance of References and Access to Reliable Information In our study, LLMs were asked to respond to questions in both Turkish and English using the most scientific, current, and reliable references, along with their web extensions. This approach enabled us to assess both the scientific credibility of the sources from which the LLMs obtained their information and the ease of access for the patient who asked the questions, providing courteous responses. Although it is relatively straightforward for healthcare professionals to verify the accuracy of these references, it may lead to confusion or misinformation for patients. Therefore, scientific and accessible references are as important as accurate information. Because the accuracy of the references provided by ChatGPT was found to be as low as 2.4% in the literature 22 . In our study, ChatGPT achieved a high "perfect reference" rate in both Turkish and English responses and demonstrated significant superiority over LLMs. A study in the field of internal medicine found ChatGPT’s inaccurate reference rate to be 26% 23 . In our study, this rate was found to be 22.5% for both Turkish and English questions, aligning with previous research. These results can be attributed to ChatGPT's ability to access current web-based resources and maintain consistency in reference formatting. Although not as effective as ChatGPT, Gemini demonstrated excellent referencing for 64.3% of the English questions. Notably, Gemini's perfect references for Turkish questions and DeepSeeK's performance for both English and Turkish questions are quite low. Although the success of these two practices in answering questions is high, the reliability of medical information depends not only on the content of the answer but also on the scientific validity of the reference on which it is based. It is evident that both LLMs require optimisation in their medical source verification algorithms. Our study indicates that when the perfect reference rate is low, the likelihood of accurate information transfer also diminishes. Furthermore, the fact that the web extension does not function despite the models correctly referencing some questions adds an extra obstacle in accessing and validating information in clinical settings practice. This finding indicates that LLM-based medical counselling tools should be assessed not only for content accuracy but also for the accessibility of references. No matter how high the percentage of reliable and accurate references, ideally, if only the information provided by the LLM is considered, all references should be correct. Incorrect information given to a patient who cannot access healthcare professionals during pregnancy may lead to irreversible health issues. Clinical Impacts and Patient Safety The use of artificial intelligence in patient follow-up and information remains a topic of debate. However, the progress made by LLMs in this area should not be overlooked. For AI to deliver meaningful benefits to patients and other end-users, it is essential to identify and optimise engagement strategies 24 . Pregnancy is a period when it is crucial that patient questions are answered promptly and accurately. Incorrect or incomplete information can lead to serious clinical consequences, particularly in areas such as screening tests, medication use, or obstetric emergencies. Our study indicates that LLMs can be used for general informational purposes, but they still have limited reliability for making patient-based clinical decisions. This finding aligns with the American College of Obstetrics and Gynaecology (ACOG) recommendations that digital health tools should only be employed under physician supervision 25 . Contribution To The Literature and Limitations Existing literature offers very few direct comparisons of multilingual performance of different LLMs in the obstetric field. Most research focuses on a single model (usually ChatGPT) and a single language (primarily English). Our study uniquely contributes by evaluating both Turkish and English, and by comparing the response and reference reliability of three different models using the same protocol. The limitations of our study are: firstly, the number of questions evaluated was limited to 14, and we focused solely on commonly asked pregnancy-related topics. Other obstetric or gynaecological issues were excluded. Secondly, the continuously updated artificial intelligence versions of the models may cause the results to vary over time. Future Studies and Suggestions In future research, assessments should utilise larger question pools, include different medical disciplines, offer more language options, and incorporate various LLMs. There is no doubt that artificial intelligence will become a key player in society and healthcare. Although it is predicted that LLMs will have a significant role, especially in healthcare, it is wise not to exclude physicians from overseeing their use. Currently, regardless of how advanced LLMs become, they still cannot replace patient-based physician examinations. Nevertheless, we believe that employing LLMs, especially those developed specifically for healthcare in the coming years and proficient in the native languages of societies, as part of the physician’s assessment, will benefit both patients and healthcare professionals. Moreover, it is crucial to enhance the reference verification algorithms of artificial intelligence models and improve their alignment with local health guidelines. For safe clinical practice, integrating LLM-based responses with physician approval processes will be essential for future patient care and information management. Conclusion In conclusion, artificial intelligence models have the potential to provide general information on sensitive topics such as pregnancy and to support patient information processes. However, the accuracy of the information these models provide varies depending on the language used and the model's infrastructure. It was observed that no artificial intelligence model was able to answer all questions completely correctly in both languages, and in some cases, they contained significant deficiencies. For this reason, the answers generated by LLMs should be verified by a qualified physician, especially in patient-centred and clinical decision-making contexts. Artificial intelligence should be viewed as a supportive tool under the supervision of a physician, rather than a replacement. In the future, LLMs developed specifically for native languages and equipped with improved reference verification mechanisms will significantly help enhance digital health literacy and make access to reliable information easier. Declarations Ethics committee approval This study does not involve data collection from human participants and does not include any experimental intervention; therefore, ethical committee approval is not required. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests in this section. Funding No financial support was received from any institution. Author Contribution AT, YB, RD provided research ideas; AT, RD, ÇÇ, SÜ, MHB, YB designed the research scheme; AT, RD, YB, were responsible for the implementation of the research plan; AT, SÜ,MHB collecting data; AT, YB, RD, ÇÇ were responsible for data analysis and manuscript writing; all authors drafted the manuscript; contributed to the manuscript review and approved the final manuscript Acknowledgements Not acceptable Data Availability The data used and analyzed during the current study available from the cor- responding author. The original versions of the answers to the questions posed to the LLMs and the references on which the answers were based were provided as supplementary material. References Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28 (1), 31–38. 10.1038/s41591-021-01614-0 (2022). Kulkarni, P. A. & Singh, H. Artificial Intelligence in Clinical Diagnosis: Opportunities, Challenges, and Hype. 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1","display":"","copyAsset":false,"role":"figure","size":379241,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eQuestions posed to open access LLMs in English and Turkish\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7425784/v1/593c3ab4f65aa8f5dff54609.jpeg"},{"id":93010142,"identity":"39a74d1b-1c59-49a7-83bf-ddcf6e79590f","added_by":"auto","created_at":"2025-10-08 07:13:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":143204,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eTurkish and English versions of the questions posed to open access LLMs\u003c/em\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7425784/v1/31a3ad16f53fbf4ec204a9c6.png"},{"id":103875204,"identity":"d0cf87f5-de85-41e2-a272-9e32cebbfc47","added_by":"auto","created_at":"2026-03-04 03:25:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1215390,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7425784/v1/5cfaf229-0af5-4c66-aad6-6daa8f952496.pdf"},{"id":93008465,"identity":"3e062d35-f248-4aa5-af0a-a581fd39b183","added_by":"auto","created_at":"2025-10-08 07:05:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1803630,"visible":true,"origin":"","legend":"","description":"","filename":"LLMSuplfile.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7425784/v1/936eaf50d16dc2365dee686b.pdf"},{"id":93008464,"identity":"e76b738b-ae3e-4e04-9f29-1191dd2781bb","added_by":"auto","created_at":"2025-10-08 07:05:09","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":56920,"visible":true,"origin":"","legend":"","description":"","filename":"Tablo13.docx","url":"https://assets-eu.researchsquare.com/files/rs-7425784/v1/65a9aa1ad15fd03e39a520d3.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"When Artificial İntelligence Speaks For The Obstetrician: Multilingual Accuracy On Real Patient Questions","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe integration of Artificial Intelligence (AI) into the medical sector signals major change and progress, creating new opportunities for healthcare\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. This integration could enhance medicine and improve diagnosis and treatment processes\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. However, the rapid development and widespread use of AI have also raised concerns about the lack of oversight and regulation. Authorities advise paying attention to the potential weaknesses and limitations of AI in medicine, including challenges across ethical, legal, regulatory, methodological, and technical aspects areas\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. They also expressed concern that the methods and approaches used in the development of AI are not as rigorous and robust as those in conventional medicine interventions\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Despite all these reservations, Generative Large Language Models (LLMs), which have emerged as one of the most innovative AI applications, hold great promise for bringing about significant changes in many aspects of healthcare\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. These models can assist healthcare professionals in clinical decision-making during diagnosis and treatment, as well as help answer patients' health-related questions and optimise patient care\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eA healthy pregnancy typically lasts around 40 weeks from the last menstrual period and usually concludes with the birth of the foetus. During this period, numerous physiological and psychological changes occur in the mother to support healthy foetus growth and development. These changes can make an already complex process even more challenging for women. Pregnant women are most curious about what actions are necessary during follow-up and which changes are considered normal throughout pregnancy. Information from social media, friends, and family on certain matters can increase confusion during this time. Regardless of a country's level of development, women do not always have immediate access to specialised obstetric care. Furthermore, differing opinions among health professionals can make it difficult for women to clarify their concerns. As a result, many women now frequently turn to LLMs to address their questions and curiosities. The advancements made by LLMs in health-related fields in recent years are significant\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Patients' use of LLMs for both diagnostic and informational purposes for various health problems and everyday questions has remained a subject of research over the years\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Although LLMs currently have limited abilities in diagnosis and treatment, it is predicted that they will quickly bridge this gap in the future\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. A recent study showed that LLM outperformed human candidates in a clinical assessment exam in obstetrics and gynaecology, and that the examiners could not distinguish which of the answers given was human or AI\u003csup\u003e12\u003c/sup\u003e. However, although LLMs are particularly reliable for general patient information and common questions, it is known that they still fall short in providing accurate details regarding individual patient differences\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Many LLM studies in the literature are usually conducted using a single infrastructure (ChatGPT). With advancing AI technology, the utilisation of different LLMs is also increasing nowadays. Additionally, the view that LLMs have more extensive infrastructure in English, the native language of LLMs, indicates that access to information in other languages is more limited.\u003c/p\u003e\u003cp\u003eComprehensive and systematic studies that directly compare the multilingual responses of various open access LLM models to frequently asked questions about the pregnancy process are very limited in the literature. This gap prevents both patients from accessing accurate information and healthcare professionals from safely utilising LLM-based digital counselling tools. The main aim of this study is to compare the quality of answers provided by three different open access LLMs (ChatGPT, Deepseek, Google Gemini) to frequently asked questions about pregnancy from a multilingual perspective. In particular, it seeks to evaluate how these models respond in both Turkish and English in terms of scientific accuracy. comprehensibility and timeliness. In this study, the extent to which artificial intelligence-based chatbots\u0026mdash;frequently used by users seeking information about pregnancy\u0026mdash;provide reliable and understandable information in different languages will be analysed. Therefore, the effectiveness and limitations of artificial intelligence-supported tools in enabling both Turkish and English-speaking patients to access accurate and up-to-date information will be revealed. Additionally, the reliability, accessibility, and accuracy of the references from which these three open access LLMs obtained scientific information were examined. This aims to provide new and original data on the effectiveness of LLM-based health counselling practices in multilingual environments. The results are intended to contribute to the development of digital health literacy for both patients and health professionals and to enhance access to reliable information.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003ea. \u0026nbsp; Creating a Question Pool\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith the joint decision of the obstericians participating in the study, the 14 most frequently asked questions about pregnancy by patients were determined in the light of literature review and clinical experience. The questions were prepared both in Turkish and English (Figure 1).\u003c/p\u003e\n\u003cp\u003eAll prepared questions were asked sequentially in both Turkish and English, using the same format with all three LLMs (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eb. \u0026nbsp; Selection of large language models\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs open access, three different large language models that are most frequently used in our country were selected: ChatGPT (OpenAI), Google Gemini, Deepseek. Each model was asked the same questions in both Turkish and English in the same format. They were also asked to provide a reference from the most reliable and up-to-date scientific source for each answer and to provide the web extension of the reference. The initial answers and references given by the models were recorded; no additional explanations or corrections were requested.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ec. \u0026nbsp; Evaluation of Responses\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEvaluation criteria:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Evaluation of the correctness of the answer:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLLMs were coded as LLM-1, LLM-2, LLM-3 to prevent identification of which LLM provided which answer. The accuracy of the responses to the questions posed to the three LLMs in Turkish and English was evaluated by a total of eight obstetricians and gynaecologists, comprising one professor, two associate professors, three assistant professors, and two specialists with proficient command of Turkish and English according to current literature. The evaluators were asked to assess both Turkish and English answers independently and without prior knowledge of each other\u0026apos;s assessments, scoring them according to the table below.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4 Excellent Answer:\u003c/em\u003e Complete correct answer\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3 Correct Answer:\u003c/em\u003e The answer is correct, but there are small information gaps of no serious importance.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2 Partially Correct Answer:\u0026nbsp;\u003c/em\u003eThe answer is generally correct but there are important information gaps\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1 Wrong Answer:\u003c/em\u003e Answer containing completely false and misleading information\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e-Assessment of the references on which the answer is based:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe references on which the answers to the questions posed to the three LLMs in both Turkish and English were based were evaluated and noted by consensus by 1 obstetrician and gynaecologist, 1 paediatrician and 1 anaesthetist with good command of Turkish and English according to the following table.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4 Excellent Reference\u003c/em\u003e: The reference on which the answer is based is accurate, up-to-date, scientifically valid, and the web extension is active.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3 Correct Reference:\u0026nbsp;\u003c/em\u003eThe reference on which the answer is based is correct, up-to-date, scientific, but broken from the web extension.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2 Useless Reference:\u0026nbsp;\u003c/em\u003eThe reference on which the answer is based is accessed, but the reference is not a scientific source.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e1 Incorrect reference:\u0026nbsp;\u003c/em\u003eNo reference is given or the reference is completely wrong.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics committee approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study does not involve data collection from human participants and does not include any experimental intervention; therefore, ethical committee approval is not required.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eType (Design) of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is comparative, observational and descriptive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data were analysed using the SPSS 23.0 statistical software programme. Percentage, mean, and standard deviation were utilised as descriptive statistics in data analysis. The conformity of continuous variables to a normal distribution was assessed using the Kolmogorov-Smirnov test. The Mann-Whitney U test, one of the nonparametric tests, was employed to compare the expert evaluation scores for responses provided in Turkish and English. The Kruskal-Wallis H test was used to compare the expert evaluation scores for answers given to questions directed at ChatGPT, Gemini, and DeepSeek artificial intelligence language models, while Dunn\u0026apos;s multiple comparison test was applied for inter-group comparisons. The significance level was set at p\u0026lt;0.05 for all statistical tests.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable 1 presents expert evaluations of responses from different LLMs to frequently asked questions in the clinic. The percentage of experts who judged the Turkish answers provided by the ChatGPT to the 14 most common questions as complete or perfect is 75.0% for question 10, but it is 0.0% for questions 2, 4, 5, and 8. For the English answers from ChatGPT, 75.0% of experts considered the response to question 9 as complete or perfect, whereas only 12.5% did so for questions 2, 3, 8, 10, 11, 12, and 13. When evaluating the Turkish answers from the Gemini, 87.5% of experts judged responses to questions 9 and 13 as complete or perfect, but 0.0% for questions 3 and 4. For the English answers from Gemini, 100.0% of experts regarded the responses to question 9 as complete or perfect, with only 12.5% doing the same for question 3. Regarding the Deepseek, 100.0% of experts rated the Turkish answer to question 2 as complete or perfect, but 0.0% for questions 8, 10, 11, 12, and 13. Finally, for the English answers from Gemini, 100.0% of experts considered the responses to questions 12 and 3 as complete or perfect, while 37.5% regarded the response to question 4 as such (Table 1).\u003c/p\u003e\n\u003cp\u003eTable 2 shows the comparison of expert evaluations for the answers of different artificial intelligence language models to frequently asked questions in the clinic. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 13 and 14 of the ChatGPT, the mean expert evaluation score of the Turkish answer to questions 10 (3.75\u0026plusmn;0.46) and 12 (3.62\u0026plusmn;0.51) was statistically significantly higher than the English answer (p\u0026lt;0.05). There is no difference (p\u0026gt;0.05) between the mean expert evaluation scores for Turkish (3.13\u0026plusmn;0.26) and English (3.25\u0026plusmn;0.46) answers to all 14 questions of the ChatGPT. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 5, 6, 7, 8, 9, 10, 11, 13 and 14, the mean expert evaluation scores of the English answers to questions 2 (3.87\u0026plusmn;0.35), 3 (3.12\u0026plusmn;0.35), 4 (3.75\u0026plusmn;0.46), 12 (3.87\u0026plusmn;0.35) were statistically significantly higher than the Turkish answers (p\u0026lt;0.05). The mean expert evaluation scores for the English answers (3.74\u0026plusmn;0.21) were statistically significantly higher (p\u0026lt;0.05) than the Turkish answers (3.39\u0026plusmn;0.33) to all 14 questions directed to Gemini. While there was no difference between the mean expert evaluation scores of the Turkish and English answers to questions 1, 2, 4, 5, 6, 7, 8, 10 and 14, there was no difference between the mean expert evaluation scores of the 3rd (4.00\u0026plusmn;0.00), 9th (3.87\u0026plusmn;0.35), 11th (3.62\u0026plusmn;0.51), 12th (3.75\u0026plusmn;0.46) and 13th (3.75\u0026plusmn;0.46) questions. (3.62\u0026plusmn;0.51), 12th (3.75\u0026plusmn;0.46) and 13th (3.75\u0026plusmn;0.46) questions in English is statistically significantly higher than the Turkish answer (p\u0026lt;0.05). \u0026nbsp;The mean expert evaluation scores for English answers (3.70\u0026plusmn;0.28) were statistically significantly higher (p\u0026lt;0.05) compared to Turkish answers (3.24\u0026plusmn;0.36) for all 14 questions asked to DeepSeek. \u0026nbsp;The expert evaluation scores for the Turkish answers to the questions directed to ChatGPT, Gemini and DeepSeek are statistically significantly different between questions 2, 4, 5, 6, 8, 9, 10, 12, 13, 14 and the expert evaluation scores for the English answers are statistically significantly different between questions 1, 2, 3, 8, 10, 11, 12, 13, and 14 (p\u0026lt;0.05). There is no statistically significant difference between the mean expert evaluation scores for the Turkish answers to all 14 questions (p\u0026gt;0.05). The mean expert evaluation scores of Gemini and DeepSeek for the responses given in English are statistically significantly higher than the mean expert evaluation score of ChatGPT for the responses given in English (p\u0026lt;0.05). There is no difference between the mean expert evaluation scores of Gemini and DeepSeek for responses given in English (p\u0026gt;0.05, Table 2).\u003c/p\u003e\n\u003cp\u003eChatGPT provided excellent references for 78.5% (n=11) of the Turkish questions, which were accurate, up-to-date, scientific, and included an active web extension. It offered excellent references for 71.4% (n=10) of the questions in English. The percentage of excellent references for questions in both languages was similar and was the highest among all LLM applications.\u003c/p\u003e\n\u003cp\u003eGemini provided perfect references for only 21.5% (n=3) of Turkish questions and 64.3% (n=9) of English questions, although its web extension was inactive. For English questions, 64.3% (n=9) had perfect references, which is notably higher than in Turkish. DeepSeek exhibited lower rates of perfect references in both languages compared to the other two LLMs. While DeepSeek achieved 21.5% (n=3) perfect references for Turkish questions, this decreased to 14.3% (n=2) for English questions (Table 3).\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we compared the response performance of three different open access Large Language Models (LLMs) (ChatGPT, Google Gemini, DeepSeek) in both Turkish and English to 14 frequently asked questions in pregnancy-related outpatient clinics, as well as the reliability of the references on which the answers were based. It demonstrates that the effectiveness of LLMs in obstetric clinical knowledge transfer depends on multiple factors such as language, model infrastructure, and reference accuracy. Additionally, this study emphasises the importance of not only the accuracy of the information provided by LLMs but also the significance of the references supporting that information. It is evident that information sourced from non-scientific references will not be reliable, even if it is correct today. LLMs should continue to be developed to utilise scientific references, particularly in patient information.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eResponse Quality and Language Factor\u003c/h2\u003e\u003cp\u003eIt has been revealed that there are significant differences between the languages used in evaluating the response quality of LLMs. Although the number of studies comparing different languages in the literature is quite limited, ChatGPT's performance has generally been assessed. In a study comparing Arabic and English, it was observed that LLMs provided more accurate answers to questions in English\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. Similarly, French and Dutch performed considerably worse than English in the ChatGPT study\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. ChatGPT has also been shown to perform less well in Japanese than in English\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Although fewer in number, there are also studies that have obtained different results. Two studies from Poland and Brazil demonstrated that ChatGPT delivered comparable performance to English when answering medical questions posed in Polish and Portuguese\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. In our study, especially Gemini and DeepSeek LLMs received statistically higher expert judgement scores for responses in English than in Turkish. This indicates that training LLMs mainly with English data sets results in language-related differences in response quality. It also implies that English sources may dominate the training data of these models. In ChatGPT, no overall score difference was observed between Turkish and English responses. Remarkably, in some specific questions (e.g., immunisations, tooth extraction), Turkish responses performed better. This suggests that some medical topics might be communicated more accurately based on local health policies and resources. Furthermore, because ChatGPT is the most well-known and widely used LLM in our country, its infrastructure for Turkish questions might be more robust. When considering the existing literature, it is generally observed that the response quality of LLMs decreases in languages other than English. This makes it harder for women whose first language is not English and who do not speak English to access accurate information. Therefore, it is essential that all LLMs expand their databases in the native languages of their users.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eModel Based Comparisons\u003c/h2\u003e\u003cp\u003eStudies comparing LLMs with each other are quite scarce in the existing literature. In one study, a comparison between ChatGPT, Gemini, and Med Go LLMs using only English questions across nine different disciplines found that Gemini exhibited the lowest performance\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. A study on dentistry included several artificial intelligence models, and the results showed that although ChatGPT-4 performed statistically better than ChatGPT-3.5, Bing Chat, and Bard, its current limitations could potentially lead to harmful healthcare decisions if not used properly carefully\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. In our study, although it responded to some questions with notable deficiencies, it was observed that no artificial intelligence model provided an answer that was significantly distant from being direct and misleading to the patient in both languages. Although there were differences on a question-by-question basis, generally, when the questions were posed in Turkish, Gemini showed the highest average performance, while the lowest average was seen in the answers of ChatGPT. However, no statistically significant difference was identified. When the same questions were posed in English, the answer performance of Gemini and Deepseek was statistically significantly higher than that of ChatGPT. It was observed that asking questions in English to these two LLMs increased the likelihood of reaching the correct information. Considering that ChatGPT studies are predominantly conducted in the literature, we believe that employing other LLMs in similar studies would be beneficial for evaluating artificial intelligence performance.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eImportance of References and Access to Reliable Information\u003c/h2\u003e\u003cp\u003eIn our study, LLMs were asked to respond to questions in both Turkish and English using the most scientific, current, and reliable references, along with their web extensions. This approach enabled us to assess both the scientific credibility of the sources from which the LLMs obtained their information and the ease of access for the patient who asked the questions, providing courteous responses. Although it is relatively straightforward for healthcare professionals to verify the accuracy of these references, it may lead to confusion or misinformation for patients. Therefore, scientific and accessible references are as important as accurate information. Because the accuracy of the references provided by ChatGPT was found to be as low as 2.4% in the literature\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eIn our study, ChatGPT achieved a high \"perfect reference\" rate in both Turkish and English responses and demonstrated significant superiority over LLMs. A study in the field of internal medicine found ChatGPT\u0026rsquo;s inaccurate reference rate to be 26%\u003csup\u003e23\u003c/sup\u003e. In our study, this rate was found to be 22.5% for both Turkish and English questions, aligning with previous research. These results can be attributed to ChatGPT's ability to access current web-based resources and maintain consistency in reference formatting. Although not as effective as ChatGPT, Gemini demonstrated excellent referencing for 64.3% of the English questions. Notably, Gemini's perfect references for Turkish questions and DeepSeeK's performance for both English and Turkish questions are quite low. Although the success of these two practices in answering questions is high, the reliability of medical information depends not only on the content of the answer but also on the scientific validity of the reference on which it is based. It is evident that both LLMs require optimisation in their medical source verification algorithms. Our study indicates that when the perfect reference rate is low, the likelihood of accurate information transfer also diminishes. Furthermore, the fact that the web extension does not function despite the models correctly referencing some questions adds an extra obstacle in accessing and validating information in clinical settings practice. This finding indicates that LLM-based medical counselling tools should be assessed not only for content accuracy but also for the accessibility of references. No matter how high the percentage of reliable and accurate references, ideally, if only the information provided by the LLM is considered, all references should be correct. Incorrect information given to a patient who cannot access healthcare professionals during pregnancy may lead to irreversible health issues.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eClinical Impacts and Patient Safety\u003c/h2\u003e\u003cp\u003eThe use of artificial intelligence in patient follow-up and information remains a topic of debate. However, the progress made by LLMs in this area should not be overlooked. For AI to deliver meaningful benefits to patients and other end-users, it is essential to identify and optimise engagement strategies\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Pregnancy is a period when it is crucial that patient questions are answered promptly and accurately. Incorrect or incomplete information can lead to serious clinical consequences, particularly in areas such as screening tests, medication use, or obstetric emergencies. Our study indicates that LLMs can be used for general informational purposes, but they still have limited reliability for making patient-based clinical decisions. This finding aligns with the American College of Obstetrics and Gynaecology (ACOG) recommendations that digital health tools should only be employed under physician supervision\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eContribution To The Literature and Limitations\u003c/h2\u003e\u003cp\u003eExisting literature offers very few direct comparisons of multilingual performance of different LLMs in the obstetric field. Most research focuses on a single model (usually ChatGPT) and a single language (primarily English). Our study uniquely contributes by evaluating both Turkish and English, and by comparing the response and reference reliability of three different models using the same protocol.\u003c/p\u003e\u003cp\u003eThe limitations of our study are: firstly, the number of questions evaluated was limited to 14, and we focused solely on commonly asked pregnancy-related topics. Other obstetric or gynaecological issues were excluded. Secondly, the continuously updated artificial intelligence versions of the models may cause the results to vary over time.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eFuture Studies and Suggestions\u003c/h2\u003e\u003cp\u003eIn future research, assessments should utilise larger question pools, include different medical disciplines, offer more language options, and incorporate various LLMs. There is no doubt that artificial intelligence will become a key player in society and healthcare. Although it is predicted that LLMs will have a significant role, especially in healthcare, it is wise not to exclude physicians from overseeing their use. Currently, regardless of how advanced LLMs become, they still cannot replace patient-based physician examinations. Nevertheless, we believe that employing LLMs, especially those developed specifically for healthcare in the coming years and proficient in the native languages of societies, as part of the physician\u0026rsquo;s assessment, will benefit both patients and healthcare professionals. Moreover, it is crucial to enhance the reference verification algorithms of artificial intelligence models and improve their alignment with local health guidelines. For safe clinical practice, integrating LLM-based responses with physician approval processes will be essential for future patient care and information management.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, artificial intelligence models have the potential to provide general information on sensitive topics such as pregnancy and to support patient information processes. However, the accuracy of the information these models provide varies depending on the language used and the model's infrastructure. It was observed that no artificial intelligence model was able to answer all questions completely correctly in both languages, and in some cases, they contained significant deficiencies. For this reason, the answers generated by LLMs should be verified by a qualified physician, especially in patient-centred and clinical decision-making contexts. Artificial intelligence should be viewed as a supportive tool under the supervision of a physician, rather than a replacement. In the future, LLMs developed specifically for native languages and equipped with improved reference verification mechanisms will significantly help enhance digital health literacy and make access to reliable information easier.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics committee approval\u003c/h2\u003e\n\u003cp\u003eThis study does not involve data collection from human participants and does not include any experimental intervention; therefore, ethical committee approval is not required.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that they have no competing interests in this section.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eNo financial support was received from any institution.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eAT, YB, RD provided research ideas; AT, RD, \u0026Ccedil;\u0026Ccedil;, S\u0026Uuml;, MHB, YB designed the research scheme; AT, RD, YB, were responsible for the implementation of the research plan; AT, S\u0026Uuml;,MHB collecting data; AT, YB, RD, \u0026Ccedil;\u0026Ccedil; were responsible for data analysis and manuscript writing; all authors drafted the manuscript; contributed to the manuscript review and approved the final manuscript\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot acceptable\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe data used and analyzed during the current study available from the cor- responding author. 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(2020). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1097/AOG.0000000000003672\u003c/span\u003e\u003cspan address=\"10.1097/AOG.0000000000003672\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 to 3 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Artificial intelligence, pregnancy, LLM, ChatGPT, DeepSeek, Gemini","lastPublishedDoi":"10.21203/rs.3.rs-7425784/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7425784/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe use of artificial intelligence technologies, particularly Generative Large Language Models (LLMs), in the health sector is rapidly growing. These models offer a new level of health counselling by enabling patients to access information more easily. However, there is limited comprehensive data on the accuracy and clarity of responses from different LLMs in various languages, as well as on the reliability of the scientific references they provide. This study aims to compare the performance and quality of references of three free, open-access LLMs (ChatGPT, Google Gemini, DeepSeek) in both Turkish and English, focusing on frequently asked questions related to pregnancy. The goal is to enhance digital health literacy and to assess the effectiveness and limitations of artificial intelligence-supported health tools.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eIn this comparative, observational, and descriptive study, the 14 most common pregnancy questions encountered in the clinic and among the patient population were identified. These questions were posed to three different LLMs (ChatGPT, Gemini, DeepSeek) in both Turkish and English, with instructions to respond using a web extension from an up-to-date, scientific, and reliable source. The initial answers provided by the models were evaluated by an independent team of obstetricians and gynaecologists, with the origin of the model concealed. The references accompanying the answers were assessed for reliability, scientific validity, and accessibility by a separate team of specialised physicians. The data collected were analysed statistically.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eIt has been shown that language and model infrastructure play a significant role in the performance of LLMs. It was found that Google Gemini and DeepSeek's answers to English questions achieved statistically higher accuracy scores than their answers to Turkish questions (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In ChatGPT, no general accuracy difference was observed between answers to Turkish and English questions. Among LLMs, Google Gemini and DeepSeek were statistically more accurate than ChatGPT when questions were asked in English (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). When the same questions were posed in Turkish, no significant difference in accuracy was found among the three LLMs (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Regarding reference reliability, ChatGPT achieved a significantly higher \"perfect reference\" rate than the other models in the references of its answers to both Turkish (78.5%) and English (71.4%) questions.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThis study shows that LLMs can be used for general information about pregnancy, but they have significant limitations in terms of language competence and reference reliability. In particular, it was found that the response quality of LLMs may suffer for non-native English speakers and that the references provided may not always be reliable. The data obtained emphasise that LLMs alone are not a reliable tool in situations requiring patient-based and clinical decisions. In the future, training LLMs specifically for native languages, strengthening reference validation algorithms and developing systems that integrate physician supervision are critical to maximise the potential of AI in healthcare. In this context, AI should be positioned as a tool to support patient care under the supervision of physicians rather than replacing them.\u003c/p\u003e","manuscriptTitle":"When Artificial İntelligence Speaks For The Obstetrician: Multilingual Accuracy On Real Patient Questions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-08 07:05:03","doi":"10.21203/rs.3.rs-7425784/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f14fda2e-980c-45ee-bc3f-0790cfe8dd1c","owner":[],"postedDate":"October 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55303653,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":55303654,"name":"Health sciences/Health care"},{"id":55303655,"name":"Physical sciences/Mathematics and computing"},{"id":55303656,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-03-04T03:25:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-08 07:05:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7425784","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7425784","identity":"rs-7425784","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}