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Arun Kumar is an Assistant Professor in the Department of Computer Science and Engineering and the Halicioglu Data Science Institute at the University of California, San Diego. His primary research interests are in data management and systems for machine learning/artificial intelligence-based data analytics. Systems and ideas based on his research have been released as part

Korin Reid, CEO of Ellison Labs, joins host Marty Duren to talk about the capabilities and limitations of artificial intelligence.Dr. Korin Reid is the CEO of Ellison Laboratories, “Where AI and Purpose Connect.” She holds a Ph.D. in Chemical Engineering from Georgia Tech and was named Forbes 30 Under 30 in Science for her contribution to the field of Artificial Intelligence as applied in the Healthcare space, where she has held various Data Science roles. This includes building massively scalable Machine Learning solutions scaling to billions of records and impacting more than 160 million covered lives and a third of US hospitals. Don't forget to order books through our sponsor, Hearts and Minds Books. This episode begins the Spring/Summer pledge drive. You can become an Uncommontary supporter at Patreon for as little as $2 a month or make a single support contribution through PayPal. All support is greatly appreciated.Subscribe via Apple Podcasts • Subscribe via Spotify • Subscribe via Google Play Music • Subscribe via RSS • Subscribe via Stitcher • Subscribe via Overcast • Subscribe in Castbox • Subscribe via Android • Subscribe via PodBean • Subscribe via iHeartRadio • Subscribe via Player FM

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

Information becomes biased and more negative as it's retold from person to person, according to new research from Wharton that sheds light on the dangers of distortion.

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

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.

Background: Advances in precision nutrition, coupled with the growing integration of artificial intelligence (AI) and mobile health (mHealth) technologies, are paving the way for personalized guidance as a complementary toolset to traditional population-based recommendations. These innovations have the potential to improve dietary choices, optimize hydration status, and contribute to the prevention of non-communicable chronic diseases (NCDs). Objectives: This scoping review aims to map the existing literature on AI-integrated hydration and nutrition systems. The goal is to identify key concepts, technological frameworks, and research gaps in the intersection of AI, mHealth, and personalized hydration. Eligibility Criteria: - Inclusion: Peer-reviewed articles, conference proceedings, and grey literature in English focusing on AI-driven nutrition systems, mHealth dietary guidance, hydration monitoring technologies, and personalized nutrition. - Exclusion: Non-English studies, purely clinical studies without technological components, and reviews lacking focus on AI or mHealth. Information Sources: Databases searched include PubMed/MEDLINE, Web of Science, and Scopus from inception to March 2025. Search Strategy: Search terms included combinations of: - Artificial intelligence: "machine learning", "artificial intelligence", "deep learning", "algorithm", "predictive modeling" - Mobile health: "mobile health", "mHealth", "smartphone application", "mobile app", "digital health" - Personalized nutrition: "personalized nutrition", "precision nutrition", "personalized diet", "individualized nutrition", "tailored nutrition" - Hydration: "hydration", "fluid intake", "water intake", "water consumption", "dehydration prevention" - Food recommendation systems: "food recommendation", "dietary guidance", "nutrition intervention", "eating behavior" Data Charting Process: Articles were screened by title and abstract, followed by full-text review. Data extraction focused on study characteristics, technology features, outcomes, implementation challenges, and policy considerations. Synthesis Plan: A qualitative thematic synthesis was conducted to identify trends and gaps. No formal quality appraisal tools were applied due to the conceptual nature of the review.

Artificial intelligence has become a growing concern among society. Many artists feel it is plagiarizing their work, writers fear it is devaluing theirs, and nearly everyone wonders how it will affect our future interactions with technology and each other. Madelayne Klein and I wanted to see what the fuss is all about so we asked \"Dear AI, what are the 5 most notorious Canadian crimes?\"In this episode we begin discussing it's answer.Series Links:Madelayne Klein: https://linktr.ee/madelaynekleinSend a voicememo to the show:nighttimepodcast.com/contactSubscribe to the show:premium feed:https://www.patreon.com/Nighttimepodcastapple podcasts: https://applepodcasts.com/nighttimeMusical Theme:Noir Toyko by Monty DattaSocial Links:Website: https://www.nighttimepodcast.comTwitter: https://twitter.com/NightTimePodFacebook: https://www.facebook.com/NightTimePodInstagram: https://www.instagram.com/nighttimepodLearn more about your ad choices. Visit megaphone.fm/adchoices

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.

The current narrow or weak form of Artificial Intelligence (AI) is, by itself, a data analysis tool that does nothing more or less than its programming instructs it to do. AI excels at sensing its environment and acting to maximize the likelihood of achieving its goals. However, it has no values or goals of its own, it simply follows the values and pursues the goals provided to it by its programmers. Artificial Wisdom (AW) has the potential to make AI a better tool and perhaps, eventually, more than a tool, but at least for now, AW must also be programmed and therefore similarly reflects only the wisdom of its programmers. Unlike intelligence, wisdom has a normative aspect, particularly with regard to whether and how a goal ought to be pursued, and no amount of descriptive data, by itself, can determine what ought to be done. It is not enough to recognize that AI must provide social benefit, align with our values, and, more generally, perform wisely without understanding that what constitutes social benefit, values, and wisdom is not universal but rather varies for different worldviews. Without a conscious effort to incorporate the wisdom of other cultures, AW will continue to reflect one, narrow worldview. Different cultures have different notions of wisdom, and AW would benefit greatly from incorporating elements of non-Western worldviews, particularly the metaphysically inclusive worldviews of Indigenous peoples who are also stakeholders in the development and application of AI. For example, incorporating aspects of the Navajo life way of hozho and Native Hawaiian ethic, pono into AW could greatly enhance the usefulness of overall benefit from applications of AI. Hozho introduces the values and goals of harmony, balance, interrelatedness, and connectedness. Pono similarly introduces “right conduct” or righteousness of action, and thereby has the potential to elevate AI beyond the technoscientific foundation of the Western scientific worldview which has created so many of our past and present problems.

This review aims to map the existing literature on ethical considerations associated with large language model use identified in interventions and broader discussions related to supporting neurodivergent individuals across various contexts.

Perma.cc archive of https://bmcmedethics.biomedcentral.com/articles/10.1186/s12910-021-00577-8#citeas created on 2022-01-23 14:49:16.347860+00:00.

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.

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

Perma.cc archive of https://www.semrush.com/blog/artificial-intelligence-stats/ created on 2022-04-23 15:06:27.668057+00:00.

LUCACI FLORENTIN - BLUE HEART - DISASTER ZONE ARTIFICIAL INTELLIGENCE

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.

The World Health Organization (WHO) defines palliative care as an approach that aims to enhance the quality of life for patients and their families who are facing life-threatening illnesses [1]. Its primary objective is to provide support for both patients and their families in managing the multifaceted challenges posed by serious diseases, through the alleviation of suffering and the effective management of symptoms. Central to palliative care is the provision of comprehensive support that addresses the physical, psychological, social, and spiritual needs of patients, underscoring the importance of interdisciplinary collaboration. This collaboration involves a diverse range of healthcare professionals, including physicians, nurses, social workers, psychologists, and spiritual advisors, all working together to develop a personalized care plan for each patient [2]. Palliative care was first introduced by Cicely Saunders in the United Kingdom, who founded the renowned St. Christopher’s Hospice in 1967 to offer comfort and care to patients in the terminal stages of life [3]. Since its inception, the concept of palliative care has evolved into a globally recognized model of care, marking a shift in the traditional medical paradigm. While earlier models predominantly focused on curative treatments, palliative care emphasizes a patient-centered approach, prioritizing not only the management of physical symptoms but also emotional and spiritual support. This holistic approach reflects a more humane and compassionate model of care. Clinical research has consistently demonstrated that palliative care significantly enhances patients' quality of life, reduces hospital admissions, lowers healthcare costs, and increases overall satisfaction for both patients and their families [4]. With the rising prevalence of chronic diseases, such as cardiovascular diseases, diabetes, and neurodegenerative disorders, managing long-term treatment has become increasingly complex. These conditions, often chronic and incurable, impose substantial physical, psychological, and economic burdens on patients [5]. In this context, palliative care plays a critical role in managing chronic illnesses by providing multidimensional symptom control, psychological support, and optimizing the overall quality of life. It has become an essential intervention in alleviating both the physical and psychological burdens on patients, while ensuring their dignity remains intact. Given the chronic and often multifaceted nature of these diseases, and the limitations of curative therapies, palliative care offers integrated symptom management, pain relief, and psychological assistance, thereby enabling patients to maintain a high quality of life throughout their illness trajectory. Furthermore, palliative care helps ease the psychological burden on families and contributes to reducing unnecessary healthcare resource utilization, mitigating overtreatment, and positively influencing health economics by reducing overall healthcare expenditure [6]. Additionally, palliative care plays a pivotal role in clinical decision-making, preserving patient autonomy by integrating their preferences into complex medical decisions and ensuring that treatment strategies are consistent with their values and expectations for life. Despite significant global advancements in palliative care, several challenges persist, including resource shortages, a lack of adequately trained professionals, and difficulties in providing personalized care tailored to the unique needs of individual patients. Palliative care requires close collaboration among interdisciplinary teams, yet many regions, particularly low-income countries, face a dearth of trained personnel and limited training resources. This has led to inconsistent coverage and variability in the quality of palliative care services. Furthermore, the multifaceted and often dynamic nature of the symptoms experienced by patients presents considerable challenges in monitoring and analyzing their conditions in real-time to inform the development of individualized care plans. As medical data complexity continues to grow, the integration and effective utilization of this data to optimize treatment plans and improve patient outcomes remains a significant challenge [7]. The rapid development of Artificial Intelligence (AI) in the healthcare sector presents new opportunities for transforming medical care models [8]. Leveraging technologies such as deep learning and natural language processing, AI is capable of processing and analyzing large volumes of medical data, thereby assisting healthcare providers in making more accurate diagnoses and intervention decisions. Additionally, AI has the potential to improve the equitable distribution of healthcare resources, particularly in underserved and remote areas, enhancing the accessibility and availability of healthcare services [9]. AI's application in palliative care is emerging as an important and promising research direction, particularly in areas such as enhancing patient comfort, supporting end-of-life decision-making, and optimizing care processes [10]. AI is currently employed to assess changes in patient conditions and predict end-of-life risks using advanced data analysis and machine learning algorithms, enabling the development of personalized treatment and care plans. Furthermore, AI technologies support healthcare professionals in offering emotional support, alleviating patient suffering, and optimizing resource allocation and management, thus ensuring the sustainability and efficiency of palliative care services [11]. These technologies not only improve the accuracy and timeliness of treatment but also provide intelligent decision support for both families and healthcare providers, ensuring the dignity and quality of life of the patient are preserved [12]. As a result, AI applications have significantly enhanced the effectiveness of palliative care, providing a solid technological foundation for continued advancements and innovations in this field. Although research on AI's application in palliative care has been growing in recent years, the landscape of this research remains unclear, and there is a lack of systematic and comprehensive visual analysis. This gap hinders a full understanding of the progress and potential future directions of AI in palliative care, thereby limiting future research and practical implementation. Therefore, a bibliometric analysis of AI applications in palliative care is essential to bridge this gap. Bibliometrics, a quantitative approach to analyzing academic literature, provides valuable insights into the evolution of academic research, development trends, and emerging research hotspots by statistically analyzing academic publications [13]. This method allows researchers to examine literature distribution, author collaboration networks, and interdisciplinary connections, facilitating the identification of innovative research directions and potential gaps within the field. By constructing data models, bibliometrics can assess the quantity, impact, and dissemination of academic resources, offering researchers a measurable and objective perspective to identify frontier issues and predict future research trends. Ultimately, bibliometrics provides scientific evidence to guide both research and policymaking in relevant areas [14]. This study aims to evaluate the current state and development trends of AI research in palliative care through bibliometric analysis. By identifying key technologies, research hotspots, and future directions, this study will offer clear research pathways and actionable recommendations for future investigations. This will help promote the innovative application of AI in palliative care and further advance both academic research and clinical practice in the field. References [1]World Health Organization. (2020). Palliative care. Retrieved from https://www.who.int/news-room/fact-sheets/detail/palliative-care [2]CURROW D C, AGAR M R, PHILLIPS J L. Role of Hospice Care at the End of Life for People With Cancer[J]. JOURNAL OF CLINICAL ONCOLOGY, 2020,38(9): 937. [3] CICELY S,CLARK D.Cicely Saunders:selected writings 1958-2004[M].NewYork:Oxford University Press,2006:2-6. [4]BAJWAH S, OLUYASE A O, YI D, et al. The effectiveness and cost-effectiveness of hospital-based specialist palliative care for adults with advanced illness and their caregivers[J]. COCHRANE DATABASE OF SYSTEMATIC REVIEWS, 2020(CD0127809). [5]WHO. Noncommunicable diseases (2022). World Health Organization. Available from: https://www.who.int/news-room/fact-sheets/detail/noncommunicable-diseases. [6]YADAV S, HELLER I W, SCHAEFER N, et al. The health care cost of palliative care for cancer patients: a systematic review[J]. SUPPORTIVE CARE IN CANCER, 2020,28(10): 4561-4573. [7]TATUM P E, MILLS S S. Hospice and Palliative Care An Overview[J]. MEDICAL CLINICS OF NORTH AMERICA, 2020,104(3): 359. [8]Stead, WW. Clinical implications and challenges of artificial intelligence and deep learning. J Amer Med Associat. (2018) 320:1107–8. doi: 10.1001/jama.2018.11029 [9]LI J, LI X X. Research progress of psychological and psychosocial interventions to prevent postpartum depression[J]. Chinese Journal of Health Education, 2018, 34(3): 261-264. [10]Bienfait F, Petit M, Pardenaud R, et al. Applying m-health to palliative care: a systematic review on the use of mhealth in monitoring patients with chronic diseases and its transposition in palliative care[J]. Am J Hosp Palliat Care, 2019, 37(7): 549-564. doi:10.1177/1049909119885655. [11]Bonsignore L, Bloom N, Steinhauser K, et al. Evaluating the feasibility and acceptability of a telehealth program in a rural palliative care population: tapcloud for palliative care. Journal of Pain and Symptom Management, 2018, 56(1): 7-14. [12]Calton BA, Rabow MW, Branagan L, et al. Top ten tips palliative care clinicians should know about telepalliative care. Journal of Palliative Medicine, 2019, 22(8): 981-985. [13]Ninkov, A, Frank, JR, and Maggio, LA. Bibliometrics: methods for studying academic publishing. Perspect Med Educ. (2022) 11:173–6. doi: 10.1007/S40037-021-00695-4 [14]Maggio, LA, Costello, JA, Norton, C, Driessen, EW, and Artino, AR. Knowledge syntheses in medical education: a bibliometric analysis. Perspect Med Educ. (2021) 10:79–87. doi: 10.1007/s40037-020-00626-9

The aim of this review is to synthesise current research on the use of artificial intelligence (AI) in assessments within tertiary psychology programs.

This systematic review examines the effectiveness of artificial intelligence (AI)-generated dietary recommendations in improving clinical and psychological outcomes among adults. Personalized nutrition is increasingly recognized as essential for managing chronic conditions such as type 2 diabetes, irritable bowel syndrome (IBS), and functional constipation. Traditional dietary interventions often overlook individual metabolic variability and behavioral patterns, leading to inconsistent outcomes. To address this gap, we conducted a systematic review following PRISMA 2020 guidelines and registered the protocol post hoc. We searched six major databases (Cochrane, EBSCO, EMBASE, PubMed, SCOPUS, and Web of Science) for peer-reviewed articles published between November 19, 2015, and June 4, 2024. Eleven studies met our inclusion criteria, comprising five randomized controlled trials, five pre-post studies, and one cross-sectional analysis. The included interventions utilized machine learning, deep learning, and IoT-integrated systems to generate personalized dietary plans based on various individual data inputs—such as blood glucose, gut microbiome composition, and self-reported health metrics. Outcomes demonstrated that AI-generated diets consistently improved glycemic control, metabolic health, gastrointestinal symptoms, and psychological well-being, with some studies reporting up to a 72.7% diabetes remission rate and a 39% reduction in IBS symptom severity. Mild side effects were reported in a subset of studies. This review not only evaluates the clinical efficacy of AI-assisted dietary interventions but also highlights challenges related to algorithm transparency, user adherence, data privacy, and generalizability. Our findings underscore the potential of AI in advancing precision nutrition, while emphasizing the need for further rigorous and inclusive research to inform scalable and equitable implementation in real-world settings.

This systematic review examines the effectiveness of artificial intelligence (AI)-generated dietary recommendations in improving clinical and psychological outcomes among adults. Personalized nutrition is increasingly recognized as essential for managing chronic conditions such as type 2 diabetes, irritable bowel syndrome (IBS), and functional constipation. Traditional dietary interventions often overlook individual metabolic variability and behavioral patterns, leading to inconsistent outcomes. To address this gap, we conducted a systematic review following PRISMA 2020 guidelines and registered the protocol post hoc. We searched six major databases (Cochrane, EBSCO, EMBASE, PubMed, SCOPUS, and Web of Science) for peer-reviewed articles published between November 19, 2015, and June 4, 2024. Eleven studies met our inclusion criteria, comprising five randomized controlled trials, five pre-post studies, and one cross-sectional analysis. The included interventions utilized machine learning, deep learning, and IoT-integrated systems to generate personalized dietary plans based on various individual data inputs—such as blood glucose, gut microbiome composition, and self-reported health metrics. Outcomes demonstrated that AI-generated diets consistently improved glycemic control, metabolic health, gastrointestinal symptoms, and psychological well-being, with some studies reporting up to a 72.7% diabetes remission rate and a 39% reduction in IBS symptom severity. Mild side effects were reported in a subset of studies. This review not only evaluates the clinical efficacy of AI-assisted dietary interventions but also highlights challenges related to algorithm transparency, user adherence, data privacy, and generalizability. Our findings underscore the potential of AI in advancing precision nutrition, while emphasizing the need for further rigorous and inclusive research to inform scalable and equitable implementation in real-world settings.

This systematic review examines the effectiveness of artificial intelligence (AI)-generated dietary recommendations in improving clinical and psychological outcomes among adults. Personalized nutrition is increasingly recognized as essential for managing chronic conditions such as type 2 diabetes, irritable bowel syndrome (IBS), and functional constipation. Traditional dietary interventions often overlook individual metabolic variability and behavioral patterns, leading to inconsistent outcomes. To address this gap, we conducted a systematic review following PRISMA 2020 guidelines and registered the protocol post hoc. We searched six major databases (Cochrane, EBSCO, EMBASE, PubMed, SCOPUS, and Web of Science) for peer-reviewed articles published between November 19, 2015, and June 4, 2024. Eleven studies met our inclusion criteria, comprising five randomized controlled trials, five pre-post studies, and one cross-sectional analysis. The included interventions utilized machine learning, deep learning, and IoT-integrated systems to generate personalized dietary plans based on various individual data inputs—such as blood glucose, gut microbiome composition, and self-reported health metrics. Outcomes demonstrated that AI-generated diets consistently improved glycemic control, metabolic health, gastrointestinal symptoms, and psychological well-being, with some studies reporting up to a 72.7% diabetes remission rate and a 39% reduction in IBS symptom severity. Mild side effects were reported in a subset of studies. This review not only evaluates the clinical efficacy of AI-assisted dietary interventions but also highlights challenges related to algorithm transparency, user adherence, data privacy, and generalizability. Our findings underscore the potential of AI in advancing precision nutrition, while emphasizing the need for further rigorous and inclusive research to inform scalable and equitable implementation in real-world settings.

Urinary tract infections (UTIs) are among the most common infections occurring across all aged groups. UTI is well known cause of acute morbidity and chronic medical conditions. Current diagnostic methods of UTI remain sub-optimal. Development of better diagnostic tools for UTI is essential for improving treatment and to reduce morbidity. Artificial intelligence (AI) is defined as the science of computers where they have an ability to perform tasks commonly associated with intelligent beings. The objective of this study is to analyze current views of attempts to applying artificial intelligence techniques in everyday practice as well as find promising methods to diagnose urinary tract infections in most efficient ways.

The present scoping review will seek to understand the implications of GAI toys in the early years. Importantly, it will consider benefits, risks, and mitigations together to provide a rounded view of the current research landscape. To understand the current state of research on GAI toys, this synthesis will collate existing evidence on children’s interactions with GAI toys through systematic searches of four databases to answer the following research questions: (1) What are the key characteristics of the research on children’s interactions with GAI toys in the early years? (2) What are the benefits, risks, and mitigations regarding GAI toys for young children that are evident in the current literature, including but not limited to implications for privacy, education, development, and mental health?

This project examines people's beliefs about artificial intelligence (AI) technology using headlines that make strong or weak claims about the AI's ability to recognize and learn human emotions.

In this project, we systematically review the existing meta-analyses on Artificial Intelligence (AI) in education. The integration of AI into educational contexts is a rapidly evolving field, capturing the interest of researchers, educators, and policymakers due to the advances and challenges it poses (Kasneci et al., 2023). The applications of AI in education are diverse and include, for example, automated feedback on written text (Fleckenstein et al., 2023), chatbots for language, STEM, or general learning (Laun & Wolff, 2025; Wu & Yu, 2024), or large language models such as the Generative Pre-trained Transformer ChatGPT (Deng et al., 2025; Heung & Chiu, 2025; Kasneci et al., 2023; Muhammad Iqbal Amrullah et al., 2024). Despite this being a relatively young field of research, several meta-analyses have been conducted, exploring various aspects of AI in education (e.g., A. Chen et al., 2025; Heung & Chiu, 2025; Hu, 2024; Wu & Yu, 2024; Zhu et al., 2025). However, there exists a need for a comprehensive synthesis of these findings to understand the current landscape and identify areas in need of further investigation (L. Chen et al., 2020). Prior second-order reviews have focused on higher education or on synthesizing systematic reviews (i.e., Bond et al., 2024; Fu et al., 2024) and did not attempt to review current meta-analyses in K-12 education. The present study aims to fill this gap by conducting a systematic review of existing meta-analyses of interventions and experiments on AI in K-12 and higher education. By mapping the existing meta-analytic research, we aim to provide insights into the diverse users, outcomes, domains, moderating variables, and methodological quality of AI in education. We focus on meta-analyses aggregating the results from interventions or experiments with AI (e.g., AI-based interventions, use of AI chatbots) to make causal inferences possible. Additionally, we will review effect sizes of the impact of AI interventions and common moderators in meta-analytic research on AI in education to broadly inform researchers and educators of potentially important variables influencing the effectiveness of AI use in education. Finally, we will assess the methodological quality of the meta-analyses on AI in education. Thus, this study is aimed at providing a comprehensive overview of AI in education, answering the following research questions based on meta-analytic research aggregating interventions and experiments on AI in education: 1.1. Who is using AI in education? 1.2. What are common outcomes of experimental and intervention research on AI in education? 1.3. In which domains (e.g., language, STEM) is AI in education commonly researched? 1.4. How large or small are the effect sizes of meta-analytic research on AI in education 1.5. What are the common moderators in meta-analytic research on AI in education? 1.6. What is the methodological quality of the meta-analytic research on AI in education?

see 241029_AINutrition_Prereg.pdf

To what extent is artificial intelligence used in hospital administration and decision making?

This is a scoping review entitled, "Curriculum frameworks and educational programs in artificial intelligence for medical students, residents and practicing physicians: A scoping review protocol". This is an updated registration.

The purpose of this scoping review is to explore the existing literature and critically appraise the impact of Artificial Intelligence and Wearables in Strength Training and Performance Monitoring.

This Pre-Test is designed to test the stimulus material as well as the scales for the project. Using an experimental 2x2 between subjects design, the project investigates the effects of expected "self-interest" and "obligation to share private information" on respondents' support for the public healthcare system in Germany.

With the advancement of transformative technologies, there has been a change in businesses, especially in industries such as banking. One of these technologies is artificial intelligence, which is changing the structure of banks and their performance, and this is an issue that affects the future of human resources. As artificial intelligence advances, the accuracy and speed of information analysis, prediction of patterns, trends, financial risks and crises will improve, but it must be said that it will inevitably force societies to change. In order to keep pace with these developments, it is necessary to identify the human capital challenges. The aim of this research is to identify the human capital challenges of fourth generation banking, affected by the advances in artificial intelligence. Methodology:  This research, with the approach of qualitative research and meta-composite tool, which includes the seven steps of Sandelowski and Barso, has been evaluated and systematically analysed 84 cases from the results of previous research. In this regard, the opinions of 15 experts were collected through a questionnaire, and the coefficient of the identified techniques was determined using the quantitative method of Shannon's entropy. Findings:  The result of this research leads to the identification of staff challenges which are grouped into four categories and they are health (staff health, user morale, communication with tools), operation (specialised training, user experience, speed of work, accuracy analysis), job and structure (simplification of processes, job enrichment, job change, unemployment or promotion), transparency and security (data manageability, trust in data entry, user security, information security and confidentiality). Originality/Value:  With the advancement of artificial intelligence and other new technologies, human capital in Banking 4.0 faces many challenges that require special attention and careful planning. Identifying these challenges and providing appropriate solutions can lead to improved performance and increased employee satisfaction in this industry. The study of metacombination as an effective tool helps managers to respond to these challenges with a comprehensive and scientific view.

Using an experimental 2x2 between subjects design, the project investigates the effects of expected "self-interest" and "obligation to share private information" on respondents' support for the public healthcare system in Germany.

The aim of the scoping review is to map current evidence on the role of AI in supporting the mental wellbeing of Health care workers.

With this study we intend to give the starting point for the development of artificial intelligence in wound care education.

In recent years, there has been an emphasis on leveraging AI techniques to enhance image interpretation and facilitate accurate diagnosis. However, there is a clear discrepancy in the extent to which the science is translated into clinical practice. There is currently a shift to address the challenges of AI deployment to optimise implementation and establish strategies for successful deployment of AI medical imaging solutions. We aim to identify the key barriers and facilitators influencing the effective deployment of Artificial intelligence (AI) medical imaging solutions in healthcare and how they can be strategically considered to streamline integration into NHS clinical pathways?

This systematic scoping review aims to explore and evaluate the applications of artificial intelligence in orthopaedic surgical training.

This scoping review explores the role of artificial intelligence (AI) in the detection, prediction, and optimization of patient-ventilator asynchronies (PVAs). By mapping the breadth of current research, the review identifies key methodologies, trends, and gaps in the literature, offering a foundation for advancing AI's clinical integration in ventilatory management.

Abstract: The use of Artificial Intelligence in recognizing pediatric emotion has gained a central interest with high potential for effective results on children and adolescents. Artificial intelligence is a science that designs systems to perform tasks that need human intellect, such as recognition of patterns, decision-making, and interpretation of data. Applications of artificial intelligence are rapidly growing in the field of health care. In pediatrics, AI technologies have been applied to early disease diagnosis, treatment optimization, and monitoring of neonates to decrease clinical errors and increase precision in care. More directly, the application of AI in recognizing emotions will provide new opportunities for overcoming the difficulties that may have developed in the ability to express emotions of children due to cognitive, physical, or emotional reasons, all of which may delay the timely detection of problems in their mental health. One such promising area is via AI analysis of child behavior, facial expressions, and other non-verbal elements that could offer an understanding of children's emotional states and thereby provide the caregivers, parents, and educators with valuable information to design more targeted interventions. Furthermore, it has been promising how AI systems are capable of recognizing emotions in children with intellectual and multiple disabilities, thus enhancing communication and care. This study aims to map the existing literature on AI applications in recognizing emotions in children and adolescents, evaluating its applicability in clinical settings. Methods: A scoping review will be conducted following the Joanna Briggs Institute guidelines. The research will be carried out in PubMed, Scopus, CINAHL Complete, MEDLINE Complete, IEEE Xplore and Wiley Online Library, without temporal restrictions. Studies in English, Portuguese and Spanish that address the use of AI for emotion recognition in pediatric health contexts will be included. Study selection will be performed by two independent reviewers, and data will be extracted using a standardized form. Discussion: We expect to identify and synthesize the AI technologies applied to emotion recognition in pediatrics, highlighting their clinical applications, gaps in the literature, and implications for clinical practice. The review will contribute to understanding the potential and limitations of AI in emotion recognition within pediatric settings, providing a basis for future research and the development of clinical practices.

To explore the categorization of humanness confronted with the challenges of the development in artificial intelligence.

To conduct a scoping review of the application of artificial intelligence (AI) in nursing education to understand the current state of AI application in this field and to provide clues for future research.The application of artificial intelligence in the field of nursing education is still in its infancy, with limited practical guidance resources, and there are differences in research methods and assessment tools , which pose challenges for the development of artificial intelligence in the nursing field.This article guided by the scoping review report framework , organizes the forms, content, evaluation indicators, and effects of the application of artificial intelligence in nursing education, aiming to provide a reference basis for the effective implementation of artificial intelligence in the field of nursing education.

Background: Artificial Intelligence (AI) is becoming increasingly prominent in domains such as healthcare. It is argued to be transformative through altering the way in which healthcare data is used. The realisation and success of AI depend heavily on people’s trust in its applications. Yet, influences on trust in healthcare AI applications so far have been underexplored. Objective: The objective of this study was to identify aspects related to users, the AI application and the wider context influencing trust in healthcare AI (HAI). Methods: We performed a systematic review to map out influences on user trust in HAI. To identify relevant studies, we searched 7 electronic databases in November 2019 (ACM digital library, IEEE Explore, NHS Evidence, ProQuest Dissertations & Thesis Global, PsycINFO, PubMed, Web of Science Core Collection). Searches were restricted to publications available in English and German. To be included studies had to be empirical; focus on an AI application (excluding robotics) in a health-related setting; and evaluate applications with regards to users. Results: Three studies, one mixed-method and two qualitative studies in English were included. Influences on trust fell into three broad categories: human-related (knowledge, expectation, mental model, self-efficacy, type of user, age, gender), AI-related (data privacy and safety, operational safety, transparency, design, customizability, trialability, explainability, understandability, power-control-balance, benevolence) and context-related (AI company, media, users’ social network). The factors resulted in an updated logic model illustrating the relationship between these aspects. Conclusion: Trust in healthcare AI depends on a variety of factors, both external and internal to the AI application. This study contributes to our understanding of what influences trust in HAI by highlighting key influences as well as pointing to gaps and issues in existing research on trust and AI. In so doing, it offers a starting point for further investigation of trust environments as well as trustworthy AI applications.

Case conceptualization is considered a critical foundation for effective psychological treatment (Kuyken et al., 2008). It has been described as “the heart of evidence-based practice” (Bieling & Kuyken, 2003; p. 53) and “the lynchpin that holds theory and practice of therapy together” (Butler, 1998, p. 1). A case conceptualization is essentially a road map for treatment; a rationale for a client’s psychological distress, informed by the clinician’s expertise in clinical psychology and the client’s description of their history and presenting concerns, is developed collaboratively (Eells, 2022; Persons, 2008). Importantly, therapeutic outcomes may be improved if treatment is informed by a case conceptualization that is evidence-based, culturally informed, and tailored to the client (Gloster et al., 2020; Haynes & Williams, 2003; Hopwood et al., 2019; Luoma et al., 2007; Padesky, 2020; Sanchez et al., 2022; Wolpe & Turkat, 1985). For example, Berman (2018) notes that case conceptualizations are needed to prevent “treatment chaos” (p. 3) such as jumping from crisis to crisis without addressing the underlying reasons for frequent crises. Beyond more focused treatment, additional benefits of case conceptualization may include normalization of clients’ concerns (which can be a validating experience), a better understanding of non-response or deterioration, more effective decision-making about deviating from manualized treatment, more directed generalization of clients’ strengths and resilience, and better facilitation of consultation and supervision (Kuyken et al., 2008). Yet the development of high-quality case conceptualizations can be challenging for novice and experienced clinicians alike (Eells et al., 2005; Sørbye et al., 2019; Waltman et al., 2017). Though a number of guidelines have been developed to facilitate teaching case conceptualization skills (Ellis et al., 2013; Padesky, 2020; Sperry & Sperry, 2022), researchers have not yet begun to explore how these approaches might be enhanced by using generative artificial intelligence. Given that generative artificial intelligence has been shown to benefit learning through development of personalized examples and more immediate feedback (Baidoo-anu & Ansah, 2023), I wanted to know how I might be able to use generative artificial intelligence to help students learn case conceptualization skills in a way that is challenging and interesting, but not too overwhelming or intimidating. The focus of the present study is to explore the effect of a course I developed that uses generative artificial intelligence to augment my teaching of case conceptualization skills on my students’ learning

Ravi Ramos (gaming SW engineer in Ubisoft, expert in ML/AI and Bioinformatics) speaks about trends, possibilities and limits of AI/ML and data science with Erik Ferencz (Google Cloud Developer, full-stack engineer, Revolgy). Ravi shares his impressive professional experience in the gaming industry, cloud technology, science and the way from Brazil to Brno, Czechia. Ravi is a real expert in ML/AI, so enjoy technical deep dive!

Background Inflammatory bowel disease (IBD) mainly includes Crohn's Disease (CD) and Ulcerative Colitis (Ulcerative Colitis). UC is a group of chronic non-specific inflammatory diseases with abdominal pain, diarrhea, mucous blood stool as the main symptoms. In the past 30 years, the number of patients with IBD has been growing, and the current incidence has not yet reached a peak, and the disease burden will further increase in the future. It is generally believed that diet is closely related to the occurrence, development and prognosis of IBD. Reasonable diet is not only beneficial to correct malnutrition, but also to promote mucosal repair and achieve the purpose of maintaining disease remission. With the continuous expansion of the application of artificial intelligence technology in the field of medical treatment and nursing and the continuous development of precise diet management, the research of artificial intelligence in the field of diet management of IBD patients has gradually increased. However, the specific applications of AI in diet management among patients with IBD and how to address barriers in their current usage is widely unexplored. Objective To review the research status of artificial intelligence (AI) in diet management of patients with IBD. Methods Following the methodology of the scoping review, the following databases were searched to include relevant literature: PubMed, Web of Science, Embase, Cochrane Library, CINAHL, IEEE Xplore, Association for Computing Machinery Digital Library, China Biomedical Literature Database, CNKI, Wanfang and VIP. The search period was from the establishment until March 2024 and the included articles were summarized and analyzed. Title and abstract screening and full-text review are performed using Endnote, where all articles are independently revised by two reviewers. We include articles written in English or Chinese on experimental and observational studies with qualitative, quantitative and mixed methods approaches, which evaluate AI technologies applicable to nursing. Data are extracted to Excel by two researchers and summarized. Results The scoping review will be completed in June 2024. The results will describe where research has been down, what AI technologies have been developed and studied to assist diet management in patients with IBD, how these technologies have been evaluated, how nurses have participated and how ethical issues have been addressed in the research. Conclusion Findings of this review will provide an overall state of artificial intelligence-based applications in diet management among patients with IBD. This information is important in understanding critical issues that require attention for the use of AI in diet management.

This project is a systematic literature review of research on the challenges and opportunities of generative artificial intelligence integration in foreign language teaching in higher education.

In the study, we investigate the interplay between privacy and transparency on citizens' willingness to support new AI solutions in public domains.