Large language models (LLMs) are rapidly advancing medical artificial intelligence, offering revolutionary changes in health care. These models excel in natural language processing (NLP), enhancing clinical support, diagnosis, treatment, and medical research. Breakthroughs, like GPT-4 and BERT (Bidirectional Encoder Representations from Transformer), demonstrate LLMs’ evolution through improved computing power and data. However, their high hardware requirements are being addressed through technological advancements. LLMs are unique in processing multimodal data, thereby improving emergency, elder care, and digital medical procedures. Challenges include ensuring their empirical reliability, addressing ethical and societal implications, especially data privacy, and mitigating biases while maintaining privacy and accountability. The paper emphasizes the need for human-centric, bias-free LLMs for personalized medicine and advocates for equitable development and access. LLMs hold promise for transformative impacts in health care.

Extrapulmonary tuberculosis (EPTB) is not a notifiable infectious disease in China but is a heavy burden on public health. However, the epidemic situation of EPTB nationwide is unclear. This study aimed to investigate the magnitude and main subtypes of EPTB in China. We conducted a national cross-sectional study with multistage, stratified cluster random sampling during 2020-2021. We calculated proportions of EPTB in all patients with TB by organs. Logistic regression models were used to estimate odds ratios by characteristics. A total of 6843 patients with TB were included. Of them, 24.6% were patients with EPTB, and the proportion of EPTB solo was 21.3%. Higher EPTB burden was observed in children, female patients, clinically diagnosed patients, provincial-level and prefectural-level health facilities, and Central and West China. EPTB occurred most frequently in respiratory (35.5%), musculoskeletal (15.8%), and peripheral lymphatic (15.8%) systems with top three subtypes, including tuberculous pleurisy (35.0%), spinal TB (9.8%) and cervical tuberculous lymphadenopathy (7.9%). With the increase of age, proportion of peripheral lymphatic TB decreased, and proportion rank of genitourinary TB rose. It is essential to strengthen the diagnosis and treatment capacity for EPTB in primary medical facilities. EPTB should be added to the National Tuberculosis Program as a notifiable disease.

The diagnosis and treatment of epilepsy necessitate the precise identification and classification of electroencephalogram (EEG) signals. However, EEG samples from different medical institutions often exhibit variability due to factors such as institutional characteristics, geographic locations, and the professional levels of physicians. This variability limits the widespread application of existing methods in small or single medical institutions, as they typically rely on large-scale and high-quality datasets. To rapidly assist small or single medical institutions in constructing models for the diagnosis and classification of epileptic EEG signals that are both highly generalizable and interpretable while ensuring patient privacy, this article proposes an innovative learning framework named Multicenter Knowledge Transfer Calibration with Rapid 0th-Order TSK Fuzzy System (MKTC-R0T). This method employs the 0th-order TSK fuzzy system as the baseline model for each center and integrates a knowledge transfer calibration strategy within a multicenter learning framework, aiming to enhance the model's generalizability and classification accuracy in the face of inconsistent sample quality and sample heterogeneity. Specifically, MKTC-R0T first establishes a base center model in a large medical institution, and then, by imitating the forgetting mechanism of the human brain, a portion of the knowledge at the base center is randomly forgotten, while the remaining knowledge is utilized to assist the auxiliary centers in rapidly deploying models. Ultimately, through a knowledge integration strategy, all centers collectively guide the target center in building an efficient linear system for the diagnosis and classification of epileptic EEG signals. Extensive experiments conducted on 12 epilepsy EEG signal datasets have validated that MKTC-R0T outperforms other typical algorithms in terms of running time, deployment speed, rule complexity, and the model's generalization and robustness, which demonstrates the substantial potential of MKTC-R0T in the field of epilepsy EEG signal diagnosis and classification.

Background: As one of the major constituents of the cannabis sativa plant, cannabidiol (CBD) is approved for use in medical treatment and cosmetics because of its potential health benefits. With the rapid growth of the CBD market, customers purchase these products, and relevant discussions are becoming more active on social media. Objective: In this study, we aimed to understand the users’ attitudes toward CBD products in various countries by conducting text mining on social media in countries with different substance management policies. Methods: We collected posts from Reddit and Xiaohongshu, conducted topic mining using the latent Dirichlet allocation model, and analyzed the characteristics of topics on different social media. Subsequently, a co-occurrence network of high-frequency keywords was constructed to explore potential relationships among topics. Moreover, we conducted sentiment analysis on the posts’ comments and compared users’ attitudes toward CBD products on Reddit and Xiaohongshu using chi-square test. Results: CBD-related posts on social media have been rapidly increasing, especially on Xiaohongshu since 2019. A total of 1790 posts from Reddit and 1951 posts from Xiaohongshu were included in the final analysis. The posts on the 2 social media platforms, Reddit and Xiaohongshu, were categorized into 7 and 8 topics, respectively, by the latent Dirichlet allocation model, and these topics on the 2 social media were grouped into 5 themes. Our study showed that the themes on Reddit were mainly related to the therapeutic effects of CBD, whereas the themes on Xiaohongshu concentrated on cosmetics, such as facial masks. Theme 2 (CBD market information) and theme 3 (attitudes toward CBD) on Reddit had more connections with other themes in the co-occurrence network, and theme 3 and theme 1 (CBD therapeutic effects) had a high co-occurrence frequency (22,803/73,865, 30.87%). Meanwhile, theme 1 (CBD cosmetics) on Xiaohongshu had various connections with others (169,961/384,575, 44.19%), and the co-occurrence frequency of theme 4 (CBD ingredients) and theme 1 was relatively prominent (27,128/49,312, 55.01%). Overall, users’ comments tended to be positive for CBD-related information on both Reddit and Xiaohongshu, but the percentage was higher on Xiaohongshu (82.25% vs 86.18%; P<.001), especially in cosmetics and medical health care products. Conclusions: The CBD market has grown rapidly, and the topics related to CBD on social media have become active. There are apparent differences in users’ attitudes toward CBD in countries with different substance management policies. Targeted CBD management measures should be formulated to suit the prevalence of CBD use of each country.

Background: Recently, more and more men who have sex with men (MSM) look for casual partners through online dating platforms in China. However, most are unable to know their partners’ HIV and other sexually transmitted diseases (STD) statuses, leading to the rapid increase in HIV infection among Chinese MSM. Effective partner notification is urgently needed to increase the risk awareness of MSM and prevent HIV and other STDs transmission. However, the traditional intervention mainly targets to the HIV-positive MSM and the effect is not promising. Our study aims to provide Internet-based partner notification, along with a series of health services for HIV-negative MSM to protect them from HIV and other STDs. Methods: A pragmatic stepped wedge cluster randomized controlled trial design is used to evaluate the effectiveness of a new intervention paradigm, which aims to reduce HIV and other STDs incidences among MSM in China. Through integrating a mobile health (mHealth) service application (app) to the current HIV and other STDs prevention and control methods, the new paradigm provides partner notification of HIV, syphilis, hepatitis B, and hepatitis C statuses. A total of 6172 MSM in 16 districts of Beijing, China will be recruited and randomized to sequentially receive partner notification intervention through the app at 6-month intervals. The primary outcomes are HIV incidence and the additional cost of the intervention. The secondary outcomes include incidences of syphilis, hepatitis B, and hepatitis C, disease transmission social networks, testing adherence, knowledge of HIV and STDs control, health self-responsibility awareness, changes of high risk behaviors and other related outcomes. The generalized linear mixed models (GLMM) will be used to analyze the differences of outcomes in the control period and in the intervention period. Discussion: We expect that the HIV incidence will be significantly lower and the secondary outcomes will also be improved with providing health service of partner notification through mhealth intervention. The feasible and affordable public health intervention paradigm will have implications for HIV and STDs prevention and control among MSM and other key populations. Trial registration: ClinicalTrials.gov, NCT04349748. Registered on 16 April 2020.

Background: Major sports events are the focus of the world. However, the gathering of crowds during these events creates huge risks of infectious diseases transmission, posing a significant public health threat. Objective: The aim of this study was to systematically review the epidemiological characteristics and prevention measures of infectious diseases at major sports events. Methods: The procedure of this scoping review followed Arksey and O’Malley’s five-step methodological framework. Electronic databases, including PubMed, Web of Science, Scopus, and Embase, were searched systematically. The general information (ie, publication year, study type) of each study, sports events’ features (ie, date and host location), infectious diseases’ epidemiological characteristics (ie, epidemics, risk factors), prevention measures, and surveillance paradigm were extracted, categorized, and summarized. Results: A total of 24,460 articles were retrieved from the databases and 358 studies were included in the final data synthesis based on selection criteria. A rapid growth of studies was found over recent years. The number of studies investigating epidemics and risk factors for sports events increased from 16/254 (6.3%) before 2000 to 201/254 (79.1%) after 2010. Studies focusing on prevention measures of infectious diseases accounted for 85.0% (238/280) of the articles published after 2010. A variety of infectious diseases have been reported, including respiratory tract infection, gastrointestinal infection, vector-borne infection, blood-borne infection, and water-contact infection. Among them, respiratory tract infections were the most concerning diseases (250/358, 69.8%). Besides some routine prevention measures targeted at risk factors of different diseases, strengthening surveillance was highlighted in the literature. The surveillance system appeared to have gone through three stages of development, including manual archiving, network-based systems, and automated intelligent platforms. Conclusions: This critical summary and collation of previous empirical evidence is meaningful to provide references for holding major sports events. It is essential to improve the surveillance techniques for timely detection of the emergence of epidemics and to improve risk perception in future practice.