BACKGROUND: Medical informed consent stands as an ethical and legal requisite preceding any medical intervention. Hospitalized patients face functional health literacy (FHL) challenges when dealing with informed consent forms (ICFs). The legitimacy of ICFs and informed consent procedures in China remains substantially undisclosed. The study\'s aim was to investigate if Chinese patients have adequate FHL to be truly informed before providing medical consent.
METHODS: In this cross-sectional, structured interview-based study, FHL was assessed within the context of the informed consent scenarios in two teaching hospitals (a 1500-bed general tertiary hospital and a 700-bed cancer hospital) affiliated with Shantou University Medical College. Twenty-seven patients admitted across clinical departments, along with their relatives (n = 59), were enrolled in the study after obtaining informed consent. The participants underwent a three-step assessment with two selected ICFs -teach-back skills, perceived understanding (perception), and informed knowledge (cognizance), with each component carrying a maximum score of 10. Data were analyzed with SPSS (version 22.0) for descriptive and inferential statistics, with consideration of significant P values as < 0.05.
RESULTS: The median age (IQR and range) of participants was 35.5 (28 - 49 and 13 - 74) years. Most participants had only high school education (24.4%, 21/86) or below high school education (47.7%, 41/86). The median score (IQR) of FHL assessments-teach-back, perception, and cognizance-was 4.0 (2.5, 5.8), 8.0 (6.8, 8.8), and 6.5 (5.5, 8.0) out of 10, respectively. A moderate correlation was observed between the scores of cognizance and teach-back (r = 0.359, P = 0.002) or perception (r = 0.437, P < 0.001). Multivariate linear regression analysis predicted being a patient and having lower education levels as independent risk factors of inadequate FHL (Ps = 0.001). Lack of patient-centeredness in ICFs, time constraints, and poor clinical communication were identified as barriers impeding informed consent.
CONCLUSIONS: This study demonstrates inadequacy in personal FHL and impaired organizational HL, resulting in compromised informed consent in Chinese teaching hospitals. As a remedy, we propose improving the quality of ICFs and institutionally mandated outcome-focused training on informed consent for all concerned clinicians to enhance medical ethics, ensure quality health care, address patient values, and mitigate potential medical conflicts.

BACKGROUND: Medical research in complementary and alternative medicine (CAM) has increased recently, raising ethical concerns about the moral status of CAM. Medical academic journals are responsible for conducting ethical review (ER) of manuscripts to protect the interests of human subjects and to make ethical results available before deciding to publish. However, there has been no systematic analysis of the ER in CAM journals. This study is aim to evaluate the current status of ethical requirements and compliance in CAM journals.
METHODS: This is a cross-sectional study. We reviewed instructions for authors (IFAs) of CAM journals included in the Journal Citation Reports (2021) ( https://jcr.clarivate.com ) for general information and requirements for ER. We also browsed the manuscripts regarding randomized controlled trials published by CAM journals in Q1 and Q2 section from January to June, 2023, to check the actual situation of ethical requirement. Descriptive statistics and Fisher\'s exact test were used for statistical analysis.
RESULTS: 27 journals and 68 manuscripts were ultimately included. 92.6% (25/27) IFAs included keywords of ER, indicating the presence of ethical considerations. However, no specific ER was required for CAM (n = 0). We categorized journals by Geographic origin, JCR section, Year of electronic JCR, Types of studies, % of OA Gold to explore the factors that could influence CAM journals to have certain ethical review policies. The results showed there was no statistical significance in certain ethical review policy in any classification of journals (p > 0.05). All RCT manuscripts included in the study generally met the requirements of the published journals for ethical review.
CONCLUSIONS: All IFAs discussed ER, but the content was scattered, unfocused, and there were no specific ER requirements regarding CAM. Although the manuscripts basically met the requirements of the journal, it was not possible to get closer to the process of ER in the manuscript. To ensure full implementation of these policies in the future, CAM journals should require authors to provide more details, or to form a list of items necessary for CAM ethical review.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

The history of xenotransplantation started in the 19th century. After a few decades of investigation, significant breakthroughs and preclinical milestones have been achieved worldwide. With the recent transplantation of genetically modified porcine kidneys and heart into humans, these ground-breaking achievements have attracted great attention worldwide, in the hope that xenotransplantation might alleviate or even solve the problem of organ shortage. On January 20, 2022, the China Organ Transplantation Development Foundation convened a symposium on \"The History, Current Situation and Future of Human Xenotransplantation Clinical Trials,\" where ways to promote the ethical and sustainable development of xenotransplantation in China were discussed among the participating experts. A formal consensus was reached as the product of the symposium, outlining the expert opinions on scientific, regulatory, and ethical issues of clinical trials of xenotransplantation in China.

BACKGROUND: In recent years, the influence of artificial intelligence technology on clinical trials has been steadily increasing. It has brought about significant improvements in the efficiency and cost reduction of clinical trials. The objective of this scoping review is to systematically map, describe and summarise the current utilisation of artificial intelligence in recruitment and retention process of clinical trials that has been reported in research. Additionally, the review aims to identify benefits and drawbacks, as well as barriers and facilitators associated with the application of artificial intelligence in optimising recruitment and retention in clinical trials. The findings of this review will provide insights and recommendations for future development of artificial intelligence in the context of clinical trials.
METHODS: The review of relevant literature will follow the methodological framework for scoping studies provided by the Joanna Briggs Institute. A comprehensive electronic search will be conducted using the search strategy developed by the authors. Leading medical and computer science databases such as PubMed, Embase, Scopus, IEEE Xplore and Web of Science Core Collection will be searched. The search will encompass analytical observational studies, descriptive observational studies, experimental and quasi-experimental studies published in all languages, without any time limitations, which use artificial intelligence tools in the recruitment and retention process of clinical trials. The review team will screen the identified studies and import them into a dedicated electronic library specifically created for this review. Data extraction will be performed using a data charting table.
BACKGROUND: Secondary data will be attained in this scoping review; therefore, no ethical approval is required. The results of the final review will be published in a peer-reviewed journal. It is expected that results will inform future artificial intelligence and clinical trials research.

OBJECTIVE: The present study was designed to examine the attitudes towards oocyte cryopreservation among healthcare providers working in hospitals across specialties and potential influencing factors.
METHODS: A cross-sectional study.
METHODS: The questionnaire was distributed among Chinese healthcare providers via the Credamo platform.
METHODS: There were 877 respondents recruited from 8 April to 8 May 2022, among whom 160 were identified as unqualified because of inconsistency between the IP and work addresses.
METHODS: Individual attitudes towards oocyte cryopreservation under four different settings, familiarity with oocyte cryopreservation and perceived risks about oocyte cryopreservation of healthcare providers were measured using a self-designed questionnaire.
RESULTS: There were 877 respondents recruited, and 717 were identified as qualified respondents. Two latent classes of healthcare providers characterised by different attitudes towards oocyte cryopreservation under four different settings were identified, the supportive and reluctant. Familiarity with oocyte cryopreservation had a significant direct effect on perceived risks, with better familiarity predicting lower perceived risks (β=-0.102, p<0.05). Perceived risks showed a significant direct effect on participants\' attitudes towards oocyte cryopreservation, with higher perceived risks predicting a more reluctant attitude (β=0.165, p<0.001).
CONCLUSIONS: The majority of healthcare providers held a reluctant attitude towards oocyte cryopreservation of unmarried women for non-medical reasons, which might relate to their worries about the risks to offspring\'s health and lack of knowledge about a reproductive technique.

The underrepresentation of scholarly works from low- and middle-income countries (LMICs) in academic literature is a documented concern, attributed partly to editorial biases. This trend, prevalent across various disciplines, has been less explored in the context of medical ethics journals. This study aimed to examine the composition of editorial board members (EBM) in high-impact medical ethics journals and to evaluate the extent of international diversity within these editorial teams.
This study incorporated an analysis of 16 high-impact medical ethics journals. Information regarding the EBM of these journals was systematically gathered and categorized based on the World Bank\'s country income classifications. An in-depth examination of the editorial board compositions was then conducted.
The study identified 669 EBM across the selected journals. A predominant 89.84% (601) of these members were from high-income countries (HICs), with upper-middle-income countries contributing 7.47% (50) and lower-middle-income countries 2.69% (18). No EBM were associated with low-income countries. A regional breakdown indicated that North America was the most represented area, accounting for 48.88% (327), followed by Europe & Central Asia (27.50%, 184), East Asia & Pacific (13.45%, 90), Latin America & Caribbean (4.63%, 31), Sub-Saharan Africa (4.19%, 28), Middle East & North Africa (0.75%, 5), and South Asia (0.60%, 4). In total, these EBMs hailed from 46 different countries, with the United States representing the largest proportion (43.80%, 293), followed by the United Kingdom (13.15%, 88), Australia (7.92%, 53), Germany (6.73%, 45), and Canada (5.08%, 34).
There is a significant lack of international representation within the EBM of high-impact medical ethics journals. The majority of editors in this field are affiliated with HICs, leading to a severe underrepresentation of LMICs within the editorial boards.

During the COVID-19 pandemic, there was an apparent conflict between medical and political ethics regarding the ethical evaluation of vaccine nationalism - the \"My Country First\" vaccine allocation policy. Medical ethics sees this policy as selfish, leading to an unequal global vaccine allocation. Political ethics, however, argues that this policy is in the national interest and should not be labeled unethical. This conflict is one of the fundamental reasons why various medical ethics-based global vaccine allocation schemes, including the COVID-19 Vaccines Global Access Facility, have been difficult to implement. As long as the international community remains composed of different countries, vaccine nationalism will be difficult to eradicate. Therefore, international organizations, including World Health Organization, should focus on universal vaccine access rather than allocation based solely on medical ethics. Countries, especially low-income countries, must strengthen vaccine-related capacity-building to immunize their citizens as early as possible. Otherwise, they may still be at the bottom of the global vaccine allocation queue when the next globally challenging outbreak occurs. High-income countries should work to expand the distribution of vaccines, including donating vaccines to countries that lack them, helping other countries set up vaccine factories, and sharing vaccine production technology and intellectual property, which is the right choice from medical and political ethics perspectives.

BACKGROUND: Large language models, such as ChatGPT, are capable of generating grammatically perfect and human-like text content, and a large number of ChatGPT-generated texts have appeared on the internet. However, medical texts, such as clinical notes and diagnoses, require rigorous validation, and erroneous medical content generated by ChatGPT could potentially lead to disinformation that poses significant harm to health care and the general public.
OBJECTIVE: This study is among the first on responsible artificial intelligence-generated content in medicine. We focus on analyzing the differences between medical texts written by human experts and those generated by ChatGPT and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
METHODS: We first constructed a suite of data sets containing medical texts written by human experts and generated by ChatGPT. We analyzed the linguistic features of these 2 types of content and uncovered differences in vocabulary, parts-of-speech, dependency, sentiment, perplexity, and other aspects. Finally, we designed and implemented machine learning methods to detect medical text generated by ChatGPT. The data and code used in this paper are published on GitHub.
RESULTS: Medical texts written by humans were more concrete, more diverse, and typically contained more useful information, while medical texts generated by ChatGPT paid more attention to fluency and logic and usually expressed general terminologies rather than effective information specific to the context of the problem. A bidirectional encoder representations from transformers-based model effectively detected medical texts generated by ChatGPT, and the F1 score exceeded 95%.
CONCLUSIONS: Although text generated by ChatGPT is grammatically perfect and human-like, the linguistic characteristics of generated medical texts were different from those written by human experts. Medical text generated by ChatGPT could be effectively detected by the proposed machine learning algorithms. This study provides a pathway toward trustworthy and accountable use of large language models in medicine.

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