Although there is growing evidence of the use of artificial intelligence (AI) techniques in sports, ethical issues surrounding AI use are being discussed at a minimal level. Thus, this systematic scoping review aimed to summarize the current ethical implications associated with using AI in sports. In this study, a total of 9 databases-MEDLINE/PubMed, Embase, Cochrane Library, ProQuest, EBSCOhost, IEEE Xplore, Web of Science, Scopus, and Google Scholar-were searched. The review protocol was registered (https://osf.io/42a8q) before extracting data. The search yielded 397 studies, and 25 studies met the inclusion and exclusion criteria. The 25 studies were categorized into 4 primary ethical concerns: fairness and bias, transparency and explainability, privacy and data ethics, and accountability in AI's application in sports. These categorizations were derived based on the systematic review of ethical issues highlighted across the selected studies. Fifteen studies delved into fairness and bias, focusing on how AI can perpetuate existing inequalities in sports. Thirteen studies addressed the lack of transparency, emphasizing the challenges in interpretability and trust in AI-driven decisions. Privacy and data ethics emerged as significant in 22 studies, highlighting risks related to the misuse of athletes' sensitive data. Finally, accountability was examined in 8 studies, stressing the ethical obligations of AI developers and users in sports contexts. The thematic analysis revealed overlapping concerns, as some studies addressed multiple issues simultaneously. Future research should focus on developing ethical frameworks tailored to underrepresented sports contexts and creating global standards for AI regulation in sports. This includes investigating the implications of AI applications in amateur sports, enhancing diversity in AI training datasets, and exploring the integration of ethical AI practices across various sports governance structures.

Background: The development of artificial intelligence (AI) in the medical field has been growing rapidly. As AI models have been introduced in complementary and alternative medicine (CAM), a systematized review must be performed to understand its current status.Objective: To categorize and seek the current usage of AI in CAM.Method: A systematic scoping review was conducted based on the method proposed by the Joanna Briggs Institute. The three databases, PubMed, Embase, and Cochrane Library, were used to find studies regarding AI and CAM. Only English studies from 2000 were included. Studies without mentioning either AI techniques or CAM modalities were excluded along with the non-peer-reviewed studies. A broad-range search strategy was applied to locate all relevant studies.Results: A total of 32 studies were identified, and three main categories were revealed: 1) acupuncture treatment, 2) tongue and lip diagnoses, and 3) herbal medicine. Other CAM modalities were music therapy, meditation, pulse diagnosis, and TCM syndromes. The majority of the studies utilized AI models to predict certain patterns and find reliable computerized models to assist physicians.Conclusion: Although the results from this review have shown the potential use of AI models in CAM, future research ought to focus on verifying and validating the models by performing a large-scale clinical trial to better promote AI in CAM in the era of digital health.

Biomarkers play a vital role in clinical care. They enable early diagnosis and treatment by identifying a patient’s condition and disease course and act as an outcome measure that accurately evaluates the efficacy of a new treatment or drug. Due to the rapid development of digital technologies, digital biomarkers are expected to grow tremendously. In the era of change, this scoping review was conducted to see which digital biomarkers are progressing in neuromuscular disorders, a diverse and broad-range disease group among the neurological diseases, to discover available evidence for their feasibility and reliability. Thus, a total of 10 studies were examined: 9 observational studies and 1 animal study. Of the observational studies, studies were conducted with amyotrophic lateral sclerosis (ALS), Duchenne muscular dystrophy (DMD), and spinal muscular atrophy (SMA) patients. Non-peer reviewed poster presentations were not considered, as the articles may lead to erroneous results. The only animal study included in the present review investigated the mice model of ALS for detecting rest disturbances using a non-invasive digital biomarker.

Traditional, complementary, and alternative medicine (TC&AM) play an exceptional role in health care around the world as many patients has sought a holistic approach. In this study, a multinational survey was developed and administered to obtain experience, attitude, and promotion information with regard to the international use of TC&AM among nine countries: Germany, United States, Japan, China, Malaysia, Vietnam, Russia, Kazakhstan, and United Arab Emirates (UAE). The survey was administered via online to members of SurveyMonkey Audience, a proprietary panel of respondents who were recruited from a diverse population worldwide. A total of 1071 participants has completed the survey. The participants were in favor of the treatments and therapies as well as expressed positive attitudes and also have used herbal medicine treatment more than acupuncture therapy and also used the modalities to promote metabolism rather than treating musculoskeletal diseases. Moreover, participants mentioned that TC&AM should be applied for treating and managing infectious diseases, such as COVID-19. Additionally, participants recommended using Facebook channel to promote its treatments and therapies. Based on the results, this study provides initial insights on TC&AM that may influence the non-users globally and perhaps inspire a need for further research including more countries in different continents.

Korea is showing the fastest trend in the world in population aging; there is a high interest in the elderly population nationwide. Among the common chronic diseases, the elderly tends to have a high incidence of depression. That said, it has been vital to focus on preventing depression in the elderly in advance. Hence, this study aims to select the factors related to depression in low-income seniors identified in previous studies and to develop a prediction model. In this study, 2975 elderly people from low-income families were extracted using the 13th-year data of the Korea Welfare Panel Study (2018). Decision trees, logistic regression, neural networks, and random forest were applied to develop a predictive model among the numerous data mining techniques. In addition, the wrapper’s stepwise backward elimination, which finds the optimal model by removing the least relevant factors, was applied. The evaluation of the model was confirmed via accuracy. It was verified that the final prediction model, in the case of a decision tree, showed the highest predictive power with an accuracy of 97.3%. Second, psychological factors, leisure life satisfaction, social support, subjective health awareness, and family support ranked higher than demographic factors influencing depression. Based on the results, an approach focused on psychological support is much needed to manage depression in low-income seniors. As predicting depression in the elderly varies on numerous influencing factors, using a decision tree may be beneficial to establish a firm prediction model to identify vital factors causing depression in the elderly population.

The increasing application of nutrition in cancer management has attracted a great deal of research interest in recent decades. Nutritional therapies, interventions, and assessments were known to have positive effects on reducing side effects from cancer therapy. In order to identify the global research output for nutrition in cancer research, a bibliometric analysis during the past 10 years was conducted to evaluate the current status of trends, gaps, and research directions as no bibliometric studies have been conducted regarding nutrition and cancer. After the data collection, a total of 1521 articles were chosen for this bibliometric study. The visualization analysis was performed with VOSviewer. The number of publications has grown continuously since a substantial spark was identified in 2019. The majority of the authors’ affiliations were in European countries. Four cancer types were recognized among the top 10 author keywords; they were breast cancer, head and neck cancer, colorectal cancer, and gastric cancer. The Nutrients journal was the most popular among the authors as the journal published 195 articles related to the topic. In conclusion, providing evidence-based nutritional solutions for various types of cancer is essential to nutrition and cancer research. Since it is presumed to have a growing number of cancer patients worldwide with the aging population, it is vital to continuously generate research finding effective nutrition therapies for cancer patients.