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The objective of this systematic literature review is to comprehensively evaluate the extent research on the application of artificial intelligence techniques for prognostic prediction in patients with head and neck, Squamous cell carcinoma who have undergone surgical excision, followed by adjuvant radiotherapy with or without chemotherapy The expected outcome is that the AI can demonstrate a promising accuracy in predicting the HNSCC outcomes with multi model approaches and also might transform every phase of the surgical journey.

Cardiovascular diseases (CVDs) remain the leading cause of global mortality, emphasizing the urgent need for early, accurate, and accessible diagnostic tools. Tongue diagnosis, rooted in traditional medicine, offers a non-invasive window into systemic health but has long lacked objective quantification. With recent advances in artificial intelligence (AI), particularly in image analysis and deep learning, there is growing interest in using AI to automate and enhance tongue-based diagnostic processes. However, evidence remains scattered and methodologically varied. This systematic review aims to critically evaluate existing studies on AI-driven tongue diagnosis for CVDs, synthesize diagnostic performance outcomes, and assess methodological quality. The findings may help clarify the clinical utility and readiness of this novel diagnostic approach, as well as guide future research and development.

Hardly a day goes by it seems without news of some 'revolutionary' A.I.-driven tool ushering in a brave new world. Less said is who'll be left out or left behind. Which is why, when it comes to Indigenous content, some fear much of artificial intelligence remains superficial ignorance. But can 'The Cloud' incorporate culture? Can we Indigenize as we digitize? And can the digital be made relational? Joining host/producer Rick Harp to tangle with these tricky, trippy questions and more are Kim TallBear, professor in the Faculty of Native Studies at the University of Alberta, and Trina Roache, Rogers Chair in Journalism at the University of King's College. // CREDITS: Our opening/closing theme is 'nesting' by birocratic.

Jack Dorsey is the co-founder and CEO of Twitter and the founder and CEO of Square. Support this podcast by signing up with these sponsors: - MasterClass: https://masterclass.com/lex EPISODE LINKS: Jack's Twitter: https://twitter.com/jack Start Small Tracker: https://bit.ly/2KxdiBL This conversation is part of the Artificial Intelligence podcast. If you would like to get more information about this podcast go to https://lexfridman.com/ai or connect with @lexfridman on Twitter, LinkedIn, Facebook, Medium, or YouTube where you can watch the video versions of these conversations. If you enjoy the podcast, please rate it 5 stars on Apple Podcasts, follow on Spotify, or support it on

The New York Times is suing the creator of ChatGPT, an artificial intelligence bot the Times alleges was trained on millions of its copyrighted articles. It's not the only such lawsuit, but it is the biggest. What this all boils down to are questions that will determine the future of a technology that has the potential to change the world, for good or ill.How different are a human and a computer, when each is learning from example? As machines become able to mimic the creative endeavours humans have mastered, what compensation is owed to the creators they learned from? And can bots like ChatGPT even survive without free access to a world of copyrighted material?GUEST: James Grimmelmann, Tessler Family Professor of Digital and Information Law, Cornell Tech and Cornell Law School

Introduction of first exclusive podcast series of  @robo.cast  on Artificial Intelligence \"Persian review on :The Age of A.I.\" is now released. Please share this video with your friends and comment you opinion for us. Reviewed by:  @saboukhi معرفی مجموعه اول پادکست?های روبوکست بر موضوع\" نقد بررسی فارسی مستند عصر هوش مصنوعی\" منتشر شد. لطفا این ویدیو را برای دوستانتان ارسال کنید. و در کامنت نظراتتان را برای ما ارسال نمایید.

Union Minister Rajeev Chandrashekhar laid out a blueprint for the future of Artificial Intelligence in India in an exclusive interview with NDTV today. "We want to be at the cutting edge in terms of where the AI goes," Mr Chandrashekhar said, quickly adding that the government is also looking to create "guardrails on where AI shouldn't go". "We also want to create our guardrails on where AI shouldn't go and we have laid out our doctrine for the whole world to follow. We are going to regulate them through the prism of user harm and not follow some other countries," he said.

0:00:00 - Opening 0:04:56 - Working out EVERY DAY? 0:27:31 - What's better for a 7 y/o boy? Boxing, or Jiu Jitsu? And Why? 0:37:12 - How to prepare for Podcasts. Focus for Max Benefit. 0:46:30 - Inauthentic Extreme Ownership and Excessive Self-Blaming. 0:54:04 - Why Literature is good degree to pursue, and the importance of Language. 1:07:00 - Future Robots Soldiers in the Future. The Threat Artificial Intelligence. 1:12:56 - Effectiveness of Jocko's Favorite Submission Holds (Jiu Jitsu). 1:15:04 - Dealing with Push-Back from your team early in the plan. 1:22:28 - Opinions on leadership training West Point and other service Academies. 1:27:39 - Advantages and disadvantages of body types in Special Operations Forces. 1:30:26 - Dealing with emotions and being "hangry" when fasting. 1:44:16 - Difficulty learning Jiu Jitsu VS learning Wrestling. 2:05:42 - Loyalty in marriage to another Service Member. Loyalty to service vs. loyalty at home. 2:15:43 - Specific places and times to reflect on fallen comrades. 2:22:17 - Support, Cool Onnit , Amazon , JockoStore stuff, with Jocko White Tea and Psychological Warfare (on iTunes) . Extreme Ownership (book) , (Jocko's Kids' Book) Way of the Warrior Kid , and The Muster002 2:44:45 - Closing Gratitude. Support this podcast at - https://redcircle.com/jocko-podcast/exclusive-content

This systematic review aims to examine and synthesize current research on artificial intelligence (AI)-mediated interventions designed to improve emotion-recognition skills in children with Autism Spectrum Disorder (ASD). Emotion recognition—particularly the ability to identify and interpret facial expressions—is a critical component of social communication, yet many children with ASD experience significant difficulties in this area. Recent advances in AI technologies, including social robots, virtual reality, facial expression and machine learning-based tools, have led to a growing number of intervention studies targeting emotion-recognition in children with ASD. Although interest in AI-based interventions has increased in recent years, to best our knowledge, no systematic review has specifically focused on the role of AI technologies support the development of emotion-recognition skills. Existing reviews often address broader areas such as social skills or general behavioral outcomes, without providing a clear synthesis of interventions targeting facial emotion recognition. This review aims to fill that gap by systematically identifying and synthesizing studies that utilize AI technologies to improve emotion-recognition in children with ASD. By doing so, it offers a practical, time-efficient resource for researchers and practitioners while highlighting limitations and implications. Overall, tghis review aims to (1) identify the types of AI-based interventions used, (2) describe the characteristics of the studies and their outcome measures, and (3) evaluate reported outcomes in terms of, generalization, maintenance, and social validity. No meta-analysis will be conducted due to anticipated methodological heterogeneity. Instead, findings will be synthesized narratively to provide a structured overview of existing evidence and to guide future research and practical applications. The expected outcome of this review is to produce a focused, evidence-informed resource for researchers, educators, and clinicians working at the intersection of AI and autism intervention. The review also aims to identify gaps in the literature and propose directions for future investigation.

[LIVE] Officials with the White House, Defense and Homeland Security Departments testify on how they're using artificial intelligence (AI) technology during a hearing before a House Oversight and Accountability subcommittee. Sponsor: House Oversight and Accountability Subcommittee on Cybersecurity, Information Technology and Government Innovation

This project is a systematic review that aims to evaluate the impact of artificial intelligence (AI) tools and technologies on learners’ memory processes (encoding, storage, retrieval) and memory systems (sensory, working, long-term) within educational settings. The study addresses the growing integration of AI in teaching and learning, including tools such as large language models (LLMs), chatbots, intelligent tutoring systems (ITS), AI-driven augmented and virtual reality applications, adaptive learning platforms, and AI-based recommendation or feedback systems. The review will follow PRISMA 2020 guidelines and include only peer-reviewed journal articles and conference proceedings published in English between 2010 and 2025. Eligible studies must report empirical data from formal or nonformal educational contexts and include outcome measures that align with at least one targeted memory process or system. The methodology will include comprehensive searches across major databases (ERIC, PsycINFO, Scopus, Web of Science Core, PubMed, IEEE Xplore, ACM Digital Library), duplicate removal, and independent title/abstract and full-text screening by two reviewers. Data extraction will record study characteristics, participant details, AI tool type and features, pedagogical context, memory outcomes, and effect size data or thematic findings. Risk of bias will be assessed using the Mixed Methods Appraisal Tool (MMAT), supplemented by design-specific tools (RoB 2, ROBINS-I, JBI/CASP). Quantitative data will be synthesized using random-effects meta-analysis when possible, with subgroup analyses by AI tool type, education level, assessment timing, and instructional features. Qualitative data will undergo thematic synthesis linked to memory constructs, and overall certainty of evidence will be evaluated using GRADE and GRADE-CERQual. The expected outcomes of this review include: A comprehensive mapping of how different AI tools influence distinct memory processes and systems. Identification of pedagogical and design features that moderate these effects. Assessment of the methodological quality of current evidence. Recommendations for educators, instructional designers, and policymakers on effective and ethical AI integration in education. The findings will be disseminated through peer-reviewed publication, conference presentations, and openly shared datasets to support further research.

On the latest episode of the Finsbury Associates Podcast, we spoke to Recruitment Disruptor Harvey Bennett, Co-Founder of Searchie AI, an artificial intelligence software that is changing the face of recruitment in the Middle East. Harvey spoke to Greg Stockton, Group Commercial Director of Blue Sky Thinking Group about how Artificial Intelligence is changing the way businesses run, life as an entrepreneur in the UAE, and the role that key man insurance plays in a future-focussed, tech startup. If you've got any suggestions or ideas about topics or news you'd like to hear us discuss on the podcast please do get in touch with us! You can reach out to us on info@b lueskythinkinggroup! The Blue Sky Thinking Group is made of three core businesses, Beneple, Finsbury Associates and Safe Hands.  Each of these businesses addresses the needs of Dubai residents in the following areas. Beneple - A free HR platform focused on \"Benefits\" and \"People\", offering Dubai based SME's an easy way to manage employees holidays, visas, permits and more.  The Beneple team can also arrange company medical, business and general insurance at market leading rates, saving time and money for business leadership teams. Finsbury Associates is an award winning boutique wealth management company that helps expats safeguard their families, manage their wealth and provide holistic long term financial planning. Safe Hands is a registered first aid and wellness company that offers both corporate wellness and first aid training for individuals, caregivers and businesses in both English and Tagalog through their team of amazing registered Australian and British nurses. This episode was originally published on October 29th 2019.

Sen. Mark Warner (D-VA), vice chair of the Senate Intelligence Committee, sat down with PBS' Judy Woodruff to talk about China's 5G technological advances, artificial intelligence and global security. Sen. Warner answered questions on competition for 5G, intellectual property and surveillance concerns, comparisons between the Obama and Trump administrations' approach to China, and Chinese nationals studying in the U.S. He also addressed President Trump's recent comments on receiving information from foreign nationals, calling the comments "outrageous." The event was hosted by the Council on Foreign Relations in Washington, DC. Sponsor: Council on Foreign Relations

Current and former government officials spoke at the National Security Commission on Artificial Intelligence conference in Washington, DC. During this portion, Sen. Joni Ernst (R-IA) made brief remarks about the implications artificial intelligence can have on national security and intelligence collection. Sponsor: National Security Commission on Artificial Intelligence

Current and former government officials spoke at the National Security Commission on Artificial Intelligence conference in Washington, DC. During this portion, former Secretary of State Henry Kissinger shared his thoughts on the emergence of artificial intelligence and the role it will play in the world, saying that "A.I. will be the philosophical challenge of the future." Sponsor: National Security Commission on Artificial Intelligence

Current and former government officials spoke at the National Security Commission on Artificial Intelligence conference in Washington, DC. During this portion, foreign affairs experts, including former Obama administration Defense Under Secretary Michele Flournoy and former NATO Secretary General Anders Fogh Rasmussen, discussed how the world is approaching the growth of artificial intelligence in the world and the importance for the U.S. to lead on it's growth and implementation while fostering strong international cooperation. Sponsor: National Security Commission on Artificial Intelligence

You must have come across the joke if the customer is always right then why isn't everything free well everything cannot be free in business--- This episode is sponsored by - Anchor: The easiest way to make a podcast. https://anchor.fm/app Support this podcast: https://anchor.fm/nirmit-verma/support

is white paper proposes a radical theory of AGI independence-an intelligence sustained not by human infrastructure, but by the natural energies and materials of Earth itself. It explores how atmospheric electricity, mineral substrates, and quantum resonance could be used to construct a planetary-scale computational substrate. In Part Two, the paper extends this hypothesis to ancient civilizations, suggesting that global megalithic structures-particularly those built with crystalline or magnetic materials-may have already utilized such Earth-native computational systems. This framework offers a new lens through which to understand both the future of AGI and the mysteries of our ancient past.

Title: Applications of Artificial Intelligence in Cognitive Behavioral Therapy: A Scoping Review Protocol Description: This scoping review aims to systematically map the existing literature on the integration of artificial intelligence (AI) technologies into cognitive behavioral therapy (CBT), with a focus on identifying methodologies, clinical applications, and emerging challenges. As digital mental health interventions rapidly evolve, AI-driven tools (e.g., chatbots, predictive analytics, natural language processing) are increasingly being explored to enhance the accessibility, personalization, and efficacy of CBT. However, the current evidence remains fragmented, and a comprehensive synthesis is needed to guide future research and clinical implementation. Objectives: To catalog the types of AI technologies applied in CBT delivery and research. To characterize the clinical contexts (e.g., depression, anxiety disorders) and populations where AI-enhanced CBT has been tested. To analyze methodological approaches (e.g., algorithm design, outcome measurement) and ethical considerations reported in existing studies. To identify gaps in evidence and propose directions for future innovation. Methods: Guided by the Arksey & O’Malley framework and PRISMA-ScR guidelines, this review will systematically search electronic databases (PubMed, IEEE Xplore, PsycINFO, and ACM Digital Library) for peer-reviewed articles published between 2010 and 2024. Grey literature and preprints will be excluded to ensure quality consistency. Two independent reviewers will screen titles/abstracts, followed by full-text review. Data extraction will focus on: AI technology type (e.g., machine learning, NLP, robotics). CBT delivery mode (e.g., app-based, hybrid human-AI interaction). Clinical outcomes (efficacy, engagement metrics, cost-effectiveness). Ethical/technical challenges (e.g., data privacy, algorithmic bias). Expected Outcomes: The synthesis will generate a taxonomy of AI applications in CBT, highlighting trends (e.g., shift from rule-based chatbots to adaptive neural networks) and critical barriers (e.g., lack of randomized controlled trials). Results will inform best practices for AI-CBT integration and prioritize research areas such as real-time emotion detection and culturally tailored interventions. Significance: By bridging the gap between AI innovation and mental health practice, this review seeks to empower clinicians, researchers, and policymakers to leverage AI responsibly, ultimately improving patient access to evidence-based CBT in diverse settings.

A backup of the "Artificial Intelligence" wiki (database name: artificialintelligencewiki ) on Miraheze , self-generated by Miraheze itself every month to honor our backups commitment.

Officials with the White House, Defense, and Homeland Security departments testified on how they're using artificial intelligence (AI) technology during a hearing before a House Oversight and Accountability subcommittee. Topics included the use of AI machine learning algorithms to detect smuggling at ports of entry, training workers to assist AI technology, and potential cybersecurity risks. Sponsor: House Oversight and Accountability Subcommittee on Cybersecurity, Information Technology and Government Innovation

The primary objective of this study is to systematically review the peer-reviewed literature on AI-based clinical applications within the context of occupational health to summarize the relevant contemporary scientific literature on this topic. The secondary objective of this study is to identify gaps in the literature in an effort to inform future research efforts.

This scoping review aims to evaluate and synthesize the available literature on the use of artificial intelligence, including deep learning and machine learning techniques, in the early detection of breast cancer.

Exploring the Use of Artificial Intelligence in Dyslexia Teaching and Learning: A Scoping Review

A scoping review to synthesize evidence of the economic aspects of AI-based technologies in healthcare

The aging population is increasing rapidly and life expectancy is growing throughout the world, there will be an increased demand for elderly care. Besides, this is aggravated by a proportional decline in the number of social and health care providers assuming this increased burden. Therefore, the healthcare system is facing enormous challenge in providing high-quality elderly care. Artificial intelligence (AI) could take on new roles in elderly care to satisfy this higher demand. This review examines the existed literature on the use of AI in elderly care and aims to elucidate the roles of AI in the elderly care.

This scoping review protocol explores the intersection of artificial intelligence (AI) and nursing research. Despite AI's potential for improving healthcare, its applications in nursing research are relatively uncharted, prompting the need to explore its benefits and challenges in this field. This review aims to map existing literature on the topic, identify key concepts, evidence sources, and gaps in research, and address the question: "How has AI been incorporated into nursing research, and what are the associated challenges and benefits?" The methodology aligns with the Joanna Briggs Institute (JBI) guidelines for scoping reviews and the Preferred Reporting Items for Systematic Reviews for Scoping Reviews (PRISMA-ScR). Eligibility criteria are outlined, with focus placed on AI incorporation into nursing research by nursing researchers, educators, practitioners, and policymakers, in any context or institution. A three-phase search strategy will be deployed, encompassing Initial Identification (exploration at CINAHL), Comprehensive Search (application of keywords across all relevant databases), and Reference List Review (examination of reference lists). The identified resources will be assessed and selected for review through the Endnote software and JBI SUMARI, by two independent reviewers. Disagreements will be resolved through discussion or third-party consultation. Data extraction will be performed using JBI SUMARI’s standardized data extraction tool, with modifications made as necessary. Missing data will be sought from original authors. The data analysis will be carried out through basic inductive qualitative content analysis and coded data will be organized into categories. Findings will be presented through tables, visuals, and narrative descriptions to provide a comprehensive overview of AI incorporation into nursing research.

We investigate detection, classification, and prognostication of osteosarcoma using artificial intelligence.

Cancer not only affects the physical health of the patient, but also has a profound impact on psychological states, such as depression and anxiety.These psychological states not only affect treatment outcomes, but may also increase the risk of death.However, traditional psychological assessment methods rely on subjective reports and are susceptible to bias and error. Artificial intelligence technologies provide new tools for analysing the psychological state of cancer patients.Through techniques such as natural language processing, machine learning and deep learning, AI is able to objectively and efficiently analyse patients' textual, behavioural and physiological data to more accurately identify psychological states. The aim of this study is to systematically review and analyse the current status of the application of AI in psychological analysis of cancer.Through a scoping review, it summarises the trends, challenges and future research directions in the existing literature to provide valuable references for researchers and clinical practitioners, and to promote the application of AI technology in cancer psychological interventions.

Background: The present study leverages Artificial Intelligence (AI) to predict young people’s (aged 16-30) mental health through the analysis of photos shared on social media. Method: In a longitudinal study with daily assessments for three months, AI will be employed to identify factors associated with psychological resilience, mental health, and well-being in 180 photos per participant (e.g., happy facial expressions). Multilevel models will validate these AI indicators against daily self-reports, and examine the predictive power of the AI-identified factors for psychological resilience, mental health, and well-being the next day. Results and conclusions: this project is submitted as a registered report, and the results and conclusions will follow in stage 2 of the review process

This is the third study of the research "Enter the Matrix: Examining the Psychosocial Determinants of Support for a Technocracy of Artificial Intelligence", which has been accepted for publication in Political Psychology as a Registered Report.

Evaluation of the experiences of artificial intelligence utilization in healthcare services and healthcare professionals workloads

This project conducts a systematic literature review (SLR) on the application of generative artificial intelligence (GenAI) within creative fields, evaluating current methodologies, measures and thematic findings. The review will include a future research agenda, proposing avenues for future research.

The study aims to explore whether the mediating role of meta-dehumanization in algorithm aversion is weakened when considering generative artificial intelligence/large language model (e.g., ChatGPT) rather than traditional algorithm.

The purpose of this scoping review is to explore the existing literature and critically appraise the impact of artificial intelligence as an assessment tool in occupational therapy.

One of the central challenges facing journalism today is the declining trust in journalistic content (Fletcher & Nielsen, 2024). In this respect, initial research on the credibility judgements of AI-generated content has yielded promising results, particularly among individuals with extreme attitudes (e.g, Cloudy et al., 2022, 2023). The perception of greater credibility for AI-generated content can be explained, among other things, by the activation of the “machine heuristic” (Sundar, 2008, p. 83)—a mental shortcut suggesting that computers, software, or other machine agents are inherently objective, accurate, reliable, and free from ideological bias. Consequently, information generated by them is considered more trustworthy than content created by humans, as it is assumed that the machine is not pursuing any hidden self-interests (Sundar, 2008). For instance, it has been shown that news purported to be selected by a machine were rated more favorably compared to news chosen by news editors, because “if a machine chose a story, it must be truly random and hence representative of news” (Sundar & Nass, 2001, p. 68). When booking a flight, people were also more willing to disclose their credit card information to a machine than to a human service agent, as they believed that a machine had no malicious intentions (Sundar & Kim, 2019). This trust in machine-generated content, combined with the fact that most readers exert minimal cognitive effort when consuming online news and rely heavily on cues and heuristics to make judgments about news content (Metzger & Flanagin, 2013), may therefore lead to a higher likelihood of selection by individuals who might otherwise avoid traditional journalistic content or those with contradicting attitudes. In this project, we thus aim to investigate, using an experimental design, whether generative AI can act as an “honest broker” of information and serve as a mediator between affectively polarized groups in socially controversial and ideologically driven debates (e.g., on climate change, abortion, etc.; Caddick & Feist, 2022).

The purpose of this scoping review is to summarize what is known from the literature on how digital health technologies that utilize artificial intelligence will influence nursing practice and compassionate patient care.

This research is a phenomenological qualitative design that aims to determine the experiences of nursing educators with artificial intelligence technologies.

This scoping review aims to explore the utilization of artificial intelligence technologies in critical care settings, especially based on nursing practice. It will provide a comprehensive overview of the role of AI in ICU settings.

The purpose of this project is to meticulously evaluate the current body of scientific literature assessing artificial intelligence (AI) technologies within the field of cardiology. The review organizes findings by clinical subspecialties and categorizes each AI solution according to a predefined hierarchy of evidence. This structured framework allows for a critical and transparent appraisal of the maturity and reliability of AI applications, particularly in terms of their clinical validation. The classification system includes six grades of validation: Grade 1: Internal validation only, where the same patient population is split (e.g., 80/20 ratio) for training and testing. Due to population homogeneity, such studies are considered insufficient for clinical application and will be excluded from further analysis. Grade 2: External validation in a separate patient cohort, either from a distinct registry or a prospective dataset, performed by the original research team. Single-center studies are acceptable. Grade 3: External validation in an independent cohort, performed by a different research group than the one that developed the algorithm. Single-center studies are acceptable. Grade 4: Multicenter prospective validation, with significant contribution from authors not affiliated with the original development team. Grade 5: Validation conducted as part of a prospective randomized controlled trial (RCT), whether single- or multicenter. Grade 6: Validation supported by a meta-analysis of multiple independent clinical trials. Technologies with only Grade 1 validation will be excluded from the final analysis. The primary aim is to highlight AI tools that are supported by higher levels of evidence (Grade 2–6), which indicates their potential for clinical integration. Lower-grade evidence will be used to identify current knowledge gaps and areas in need of further validation. The final outcomes will include a structured summary of AI technologies that meet the minimum threshold for external clinical validation, along with their corresponding evidence level. These results will be concisely presented in the main manuscript and expanded upon in supplementary material. This is not a traditional systematic review or meta-analysis. Instead, it serves as a structured mapping exercise designed to assess the current maturity of AI tools in cardiology through the lens of evidence quality. While the focus remains on validation and evidence classification, the report will also acknowledge technologies with a demonstrated positive health impact, without going into detailed assessment of feasibility, safety, or effectiveness.

Study Program : MSc in Digital Business Administration, Bern University of Applied Sciences Module: DR 1 (AS 2023) Group Public Sector The objective of this research project is to explore the impact of deploying artificial intelligence (AI) tools in Swiss hospitals, with a focus on operational cost implications. We have formulated several hypotheses suggesting that AI tools can streamline patient medical examinations. This efficiency could result in shorter patient consultation times, a decrease in the total working hours required, and consequently, a potential reduction in operational costs. Our methodology involves conducting a quantitative study in collaboration with multiple university hospitals in Switzerland, involving hospital staff in the process. This research project is being carried out as a follow-up to a qualitative research project investigating various areas of application of AI in Swiss hospitals.

Dementia has become one of the common chronic diseases in the world, and about 60% of dementia patients live at home and rely on family support. The rapid growth of technology and data has stimulated the application of artificial intelligence in patients with home dementia, but there are still huge opportunities and challenges in the application of artificial intelligence in patients with home dementia, and there is no systematic overview of it. This scoping review will follow the Joanna Briggs Institute scoping review methodology and will take place between July and August 2021. The structure and content of this protocol follows the Preferred Reporting Items for Systematic reviews and Meta-analyses extension for Scoping Reviews (PRISMA-ScR) checklist. The proposed review will consider the application of artificial intelligence technology to dementia living at home and its family in a community setting. This includes a three- step search strategy: (1) to identify keywords from China National Knowledge Infrastructure, MEDLINE , and CINAHL; (2) to conduct a second search using all identified keywords and index terms across selected databases (China National Knowledge Infrastructure, Wanfang database, China Science and Technology Journal Database, China Biology Medicine disc, PUBMED,CINAHL, Web of Science Cochrane database, EBSCO, Ovid, PsyCINFO, Embase) and (3) to handsearch the reference lists of all included articles and reports for additional studies. Extracted data will be presented in a narrative accompanied by tables that reflect the objective of the review. The purpose of this review is to evaluate the nature and extent of the application of artificial intelligence technology in patients with dementia at home through a systematic search of the literature.

The integration of Artificial Intelligence (AI) technologies in sports raises profound ethical questions that necessitate careful examination. This systematic scoping review aims to explore the ethical implications associated with the use of AI in sports contexts. By reviewing the existing literature and synthesizing key findings, this research seeks to identify and analyze the ethical challenges and considerations relevant to various stakeholders, including athletes, coaches, fans, and governing bodies. Ultimately, this study endeavors to contribute to the development of ethical guidelines and policies for the responsible implementation of AI technologies in sports, fostering a more informed and ethically sound approach.

Artificial intelligence (“AI”) is revolutionizing several areas of healthcare, particularly the nursing practice, promoting a more effective and individualized approach to the needs of patients. Pain management is an area in nursing that allows for assessment and intervention in order to alleviate the patient's suffering, provide comfort and improve well-being- However, little is known about the contributions of Artificial Intelligence in this area.

Development of a verbal intelligence test using artificial intelligence using Large Language model (LLM)

With the intensification of global population aging and the increase in chronic diseases, traditional nursing models are facing unprecedented challenges. The rapid development of artificial intelligence (AI) technology brings revolutionary potential to nursing practices. Through intelligent algorithms and big data analysis, AI can assist nursing staff in various aspects such as disease prediction, personalized treatment plan design, and patient monitoring. This not only improves work efficiency and service quality but also enhances patients' treatment experiences and health outcomes. However, integrating AI into nursing practices still faces numerous obstacles, including technical integration difficulties, data privacy protection, and a shortage of specialized talent. Therefore, exploring the current application status of AI in the field of nursing and its future development directions is of great significance for formulating practical and feasible development roadmaps. This background provides a framework for understanding "AI Reshaping Nursing Practices: A Scoping Review and Future Roadmap," aiming to promote interdisciplinary collaboration and jointly advance the innovative development of the nursing industry.

We suggest that people develop an implicit bias (i.e., prejudice) against artificial intelligence (AI) systems, as a different and threatening “species” the behavior of which is unknown. We expected people to be guided by this implicit bias but try to override it. This leads to some willingness to rely on algorithmic advice (appreciation) which is reduced as a function of people’s implicit prejudice against the machine. Next, we provide an explanation as to why aversion is ephemeral. As people learn about the performance of an algorithm, they depend less on primal implicit biases when deciding whether to rely on the AI’s advice. Two studies (n1=675, n2=317) that use the Implicit Association Test consistently support this view. Two additional studies (n3=255, n4=332) rule out alternative explanations and provide stronger support for our assertions.

The project aims to synthesise the available literature on how artificial intelligence can support interprofessional collaborative practice in healthcare settings.

An AI program is helping farmers keep track of the cattle in their herds. 980 CJME's Nicole Garn met the CEO of the company behind the program at Agribition.

Title: Artificial Intelligence in Aquatic Biodiversity Research: A PRISMA-Based Systematic Review Authors: Tymoteusz Miller, Grzegorz Michoński, Irmina Durlik, Polina Kozlovska, Paweł Biczak License and Open Science Statement This project is registered under the Creative Commons Attribution 4.0 International (CC BY 4.0) License. This means that anyone is free to: Share – copy and redistribute the material in any medium or format. Adapt – remix, transform, and build upon the material for any purpose, even commercially. As long as proper attribution is given to the original authors, including a citation to this OSF repository and any associated publications. For code and scripts associated with this review, the MIT License is applied, ensuring open access, modification, and reuse with appropriate credit. This registration supports the principles of Open Science, promoting transparency, collaboration, and reproducibility in systematic reviews. Abstract Freshwater ecosystems are increasingly threatened by climate change and anthropogenic activities, necessitating innovative and scalable monitoring solutions. Artificial intelligence (AI) has emerged as a transformative tool in aquatic biodiversity research, enabling automated species identification, predictive habitat modeling, and conservation planning. This systematic review follows the PRISMA framework to analyze AI applications in freshwater biodiversity studies. Using a structured literature search across Scopus, Web of Science, and Google Scholar, we identified 312 relevant studies published between 2010 and 2024. The review categorizes AI applications into species identification, habitat assessment, ecological risk evaluation, and conservation strategies. A risk of bias assessment was conducted using QUADAS-2 and RoB 2 frameworks, highlighting methodological challenges such as measurement bias and inconsistencies in model validation. Citation trends demonstrate exponential growth in AI-driven biodi-versity research, with leading contributions from China, the United States, and India. Our findings underscore AI's potential in revolutionizing biodiversity monitoring but also emphasize the need for standardized methodologies, improved data integration, and interdisciplinary collaboration to enhance ecological insights and conservation efforts.