Takahiro Kiuchi

Background: Pregnancy-related anxiety appears to be more strongly associated with adverse outcomes in obstetrics and mental health than commonly assessed general anxiety. Support for anxiety should start in the early stages of pregnancy; however, the associated factors for pregnancy-related anxiety in early pregnancy remain unclear. This study aimed to clarify the associated factors for pregnancy-related anxiety in the early stages of pregnancy. Methods: We conducted a cross-sectional study among 183 pregnant Japanese women who were ≥ 18 years, pregnant with one baby, and before 15 weeks of gestation from four facilities in Japan. Pregnancy-related anxiety was assessed using the Japanese version of the Pregnancy-Related Anxiety Questionnaire-Revised-2. Multiple linear regression analysis was performed to identify factors associated with total and subscale of pregnancy-related anxiety, including obstetric, individual, and environmental factors. Results: The model that included all factors accounted for 24% of the variance in total pregnancy-related anxiety score. Significant associated factors included previous deliveries (β = -0.27), general anxiety (β = 0.26), a history of smoking (β = 0.19), and pre-pregnancy body mass index (β = 0.17). Lifetime history of abuse and/or domestic violence (β = 0.20) and current daily hassles (β = 0.20) were also independently associated with higher anxiety levels. Subscale analysis revealed a distinct pattern: while traditional obstetric and individual factors were associated with fear of childbirth and worries about bearing a handicapped child, environmental stressors were specifically associated with concern about own appearance. Conclusions: This study demonstrated that pregnancy-related anxiety is uniquely shaped by the additive effects of lifetime trauma and current environmental stress. While traditional clinical backgrounds help identify fears regarding childbirth and infant health, they are insufficient to capture self-image concerns. In addition to identifying a history of abuse or high daily stress, we recommend direct measurements of pregnancy-related anxiety to provide tailored counseling that addresses these often-overlooked psychological vulnerabilities in early pregnancy.

Background: The accuracy of the latest reasoning-enhanced large language models on national medical licensing examinations remains unknown, which is crucial for determining how close they are to serving as effective knowledge sources for medical education. This study aimed to evaluate the performance of four reasoning-enhanced large language models (LLMs)—GPT-5, Grok-4, Claude Opus 4.1, and Gemini 2.5 Pro—on the Japanese National Medical Examination (JNME), providing insights into their potential as educational resources and their future applicability in medical practice. Methods: We evaluated LLM performance using the 2019 and 2025 JNME ( n = 793). Questions were entered into each model with chain-of-thought prompting enabled. Accuracy was assessed overall and by question type. Incorrect responses were qualitatively reviewed by a licensed physician and a medical student. Results: From highest to lowest, the overall accuracies of the four LLMs were 97.2% for Gemini 2.5 Pro, 96.3% for GPT-5, 96.1% for Claude Opus 4.1, and 95.6% for Grok-4, with no significant pairwise differences. For image-based and non-image-based items, Gemini 2.5 Pro achieved the highest accuracy of 96.1% and 97.6%, with no significant difference, whereas accuracy was significantly lower on image-based items for the other three LLMs. Across difficulty levels, Gemini 2.5 Pro again achieved the highest accuracy (98.4% for easy, 97.3% for moderate, and 93.2% for difficult items). Within each LLM, accuracy on difficult questions was significantly lower than on easy questions. Common error patterns included providing unnecessary additional options in single-choice questions, misdiagnosis of X-ray or computed tomography images (primarily due to confusion regarding left–right laterality), and difficulties in prioritizing appropriate actions in clinical questions with complex contextual information. Conclusions: Four LLMs released in 2025 surpassed the 95% benchmark on the JNME, and their near-perfect (approximately 99%) performance on basic medical knowledge questions highlights substantial potential for use as learning resources in foundational medical education. Gemini 2.5 Pro demonstrated the most consistent performance across question types, while Grok-4 showed greater variability. The concentration of incorrectness in clinical questions indicates that LLMs still require substantial refinement and validation before their use can be extended to clinical reasoning or patient care.

Background: Dental caries is one of the most prevalent chronic conditions globally. In Japan, fluoride application—mainly via toothpaste, mouth rinses, and professional treatments—is a key preventive measure, as community water fluoridation is not implemented. Despite its proven effectiveness, fluoride use faces opposition from certain groups citing potential health risks. Social media platforms, especially YouTube, have become major sources of health information, but also facilitate the spread of misinformation, which may influence public perceptions and behaviors toward fluoride use. Objective: This study aimed to analyze YouTube videos addressing fluoride use for caries prevention, focusing on the types of information presented and comparing the messages shared by proponents and opponents of fluoride use. Methods: A comprehensive search was conducted on YouTube using fluoride-related keywords in Japanese. The top 50 videos for each keyword were screened, and after excluding irrelevant or duplicate content, 86 videos were analyzed. Videos were categorized as proponent (“pro”), opponent (“anti”), or others. The sources, intended audiences, and content themes were assessed. Interrater reliability was confirmed using the Cohen κ coefficient. Results: Of the 86 analyzed videos, 58% (n=50) were categorized as “pro,” 22% (n=19) as “anti,” and 20% (n=17) as others. Proponent videos, mainly produced by dental professionals, emphasized scientific evidence, such as the mechanism of fluoride in preventing caries and guideline-based recommendations. Opponent videos, largely uploaded by laypersons, highlighted potential dangers of fluoride, including health risks and additives, and frequently promoted fluoride-free products. Opponent videos had higher daily viewership and engagement than proponent videos. Conclusions: Anti-fluoride content on YouTube appears to reach broader audiences than expert-generated profluoride videos. Opponent messages tend to use emotionally charged communication, whereas proponents focus on scientific information. These differences in style may influence public perceptions of fluoride use. Public health professionals should develop engaging and accessible communication strategies to counter misinformation and promote evidence-based practices.

Background: The human capacity to process information is limited. During the COVID-19 pandemic, people were exposed to a large amount of uncertain and complex health information. This situation made some people experience perceived information overload, which made them unable to adopt appropriate preventive behaviors. Objective: This study aimed to examine the individual characteristics, abilities, and attention to informational media that predict the perception of information overload during a pandemic. Methods: We conducted a longitudinal study with 2 time points, August 2020 and August 2021, among residents of Japan under a COVID-19 emergency declaration. The sample had the same proportions for sex, age, and prefecture as the general Japanese population. We used a web-based survey to measure sociodemographic characteristics, health literacy (HL), attention to 6 different types of information channels, and participants’ perception of information overload. Hierarchical multiple regression analysis was conducted with information overload as the objective variable. Results: A total of 784 participants responded to the survey at both time points, with a follow-up rate of 78.4% (784/1000). Hierarchical multiple regression analysis showed that younger age (β=−0.084, 95% CI −0.142 to −0.013), male sex (β=−0.163, 95% CI −0.008 to −0.003), lower HL (β=−0.084, 95% CI −0.114 to −0.011), paying less attention to television news (β=−0.118, 95% CI −0.038 to −0.001), and paying greater attention to social media (β=0.089, 95% CI 0.000-0.027) significantly predicted information overload 1 year after exposure to information during the pandemic. Conclusions: Public health communicators should aim to provide concise and understandable information in consideration of a target population that is vulnerable to information overload during a pandemic. A high level of attention to social media may increase the perception of information overload. By contrast, HL may reduce the cognitive load in information processing. Providing an environment during normal periods that allows people to develop the skills to critically interpret health information will help them to prepare for future infodemics.

Background: The widespread adoption of artificial intelligence (AI)–powered search engines has transformed how people access health information. Microsoft Copilot, formerly Bing Chat, offers real-time web-sourced responses to user queries, raising concerns about the reliability of its health content. This is particularly critical in the domain of dietary supplements, where scientific consensus is limited and online misinformation is prevalent. Despite the popularity of supplements in Japan, little is known about the accuracy of AI-generated advice on their effectiveness for common diseases. Objective: We aimed to evaluate the reliability and accuracy of Microsoft Copilot, an AI search engine, in responding to health-related queries about dietary supplements. Our findings can help consumers use large language models more safely and effectively when seeking information on dietary supplements and support developers in improving large language models’ performance in this field. Methods: We simulated typical consumer behavior by posing 180 questions (6 per supplement × 30 supplements) to Copilot’s 3 response modes (creative, balanced, and precise) in Japanese. These questions addressed the effectiveness of supplements in treating 6 common conditions (cancer, diabetes, obesity, constipation, joint pain, and hypertension). We classified the AI search engine’s answers as “effective,” “uncertain,” or “ineffective” and evaluated for accuracy against evidence-based assessments conducted by licensed physicians. We conducted a qualitative content analysis of the response texts and systematically examined the types of sources cited in all responses. Results: The proportion of Copilot responses claiming supplement effectiveness was 29.4% (53/180), 47.8% (86/180), and 45% (81/180) for the creative, balanced, and precise modes, respectively, whereas overall accuracy of the responses was low across all modes: 36.1% (65/180), 31.7% (57/180), and 31.7% (57/180) for creative, balanced, and precise, respectively. No significant difference was observed among the 3 modes ( P= .59). Notably, 72.7% (2240/3081) of the citations came from unverified sources such as blogs, sales websites, and social media. Of the 540 responses analyzed, 54 (10%) contained at least 1 citation in which the cited source did not include or support the claim made by Copilot, indicating hallucinated content. Only 48.5% (262/540) of the responses included a recommendation to consult health care professionals. Among disease categories, the highest accuracy was found for cancer-related questions, likely due to lower misinformation prevalence. Conclusions: This is the first study to assess Copilot’s performance on dietary supplement information. Despite its authoritative appearance, Copilot frequently cited noncredible sources and provided ambiguous or inaccurate information. Its tendency to avoid definitive stances and align with perceived user expectations poses potential risks for health misinformation. These findings highlight the need for integrating health communication principles—such as transparency, audience empowerment, and informed choice—into the development and regulation of AI search engines to ensure safe public use.

Background: Mpox (monkeypox) outbreaks since 2022 have emphasized the importance of accessible health education materials. However, many Japanese online resources on mpox are difficult to understand, creating barriers for public health communication. Recent advances in artificial intelligence (AI) such as ChatGPT-4o show promise in generating more comprehensible and actionable health education content. Objective: The aim of this study was to evaluate the comprehensibility, actionability, and readability of Japanese health education materials on mpox compared with texts generated by ChatGPT-4o. Methods: A cross-sectional study was conducted using systematic quantitative content analysis. A total of 119 publicly available Japanese health education materials on mpox were compared with 30 texts generated by ChatGPT-4o. Websites containing videos, social media posts, academic papers, and non-Japanese language content were excluded. For generating ChatGPT-4o texts, we used 3 separate prompts with 3 different keywords. For each keyword, text generation was repeated 10 times, with prompt history deleted each time to prevent previous outputs from influencing subsequent generations and to account for output variability. The Patient Education Materials Assessment Tool for Printable Materials (PEMAT-P) was used to assess the understandability and actionability of the generated text, while the Japanese Readability Measurement System (jReadability) was used to evaluate readability. The Journal of the American Medical Association benchmark criteria were applied to evaluate the quality of the materials. Results: A total of 119 Japanese mpox-related health education web pages and 30 ChatGPT-4o–generated texts were analyzed. AI-generated texts significantly outperformed web pages in understandability, with 80% (24/30) scoring ≥70% in PEMAT-P (P<.001). Readability scores for AI texts (mean 2.9, SD 0.4) were also higher than those for web pages (mean 2.4, SD 1.0; P=.009). However, web pages included more visual aids and actionable guidance such as practical instructions, which were largely absent in AI-generated content. Government agencies authored 90 (75.6%) out of 119 web pages, but only 31 (26.1%) included proper attribution. Most web pages (117/119, 98.3%) disclosed sponsorship and ownership. Conclusions: AI-generated texts were easier to understand and read than traditional web-based materials. However, web-based texts provided more visual aids and practical guidance. Combining AI-generated texts with traditional web-based materials may enhance the effectiveness of health education materials and improve accessibility to a broader audience. Further research is needed to explore the integration of AI-generated content into public health communication strategies and policies to optimize information delivery during health crises such as the mpox outbreak.