To map and critically appraise the current literature on Artificial Intelligence (AI) applications in emergency general surgery, with a focus on clinical decision-support tools for preoperative risk stratification and intraoperative assistance, and to identify ethical, structural, and regulatory barriers to implementation. A scoping review was conducted within the ARIES project, following established methodological frameworks. Relevant studies evaluating AI-based tools in emergency surgical settings were systematically identified and analyzed. The literature describes AI applications mainly in two domains: preoperative decision support, including risk prediction and diagnostic or triage models for acute abdominal and traumatic conditions, and intraoperative assistance, largely focused on computer vision-based systems for anatomical recognition, safety guidance, and navigation in minimally invasive emergency procedures. Additional contributions address training and telementoring platforms, as well as cross-cutting ethical, legal, and regulatory considerations relevant to AI adoption in emergency surgical care. AI has the potential to complement emergency surgeons' clinical judgment, but its routine adoption in emergency surgical practice remains limited. Addressing methodological, ethical, and regulatory challenges, together with the development of robust data infrastructures and targeted training pathways, is essential to support safe, effective, and equitable implementation in acute care settings. In addition, the lack of dedicated investment and sustainable funding models for large-scale clinical implementation and prospective evaluation represents a critical barrier to the translation of AI from research into routine emergency surgical practice.

The incorporation of generative artificial intelligence (AI) in doctoral supervision signifies a transformative evolution in higher education. This has been significant, particularly within intricate and emotionally complex research such as sexuality studies. This reflective, collaborative autoethnographic study investigates the experiences of a doctoral student and her supervisor. They explored AI generative tools to enhance research processes, quality of supervision and intellectual inquiry. Anchored in Kolb’s Experiential Learning Theory and reconceptualised through an augmented experiential learning framework, the study elucidates how AI tools like ChatGPT encourage critical thinking. These tools were also used to foster methodological innovation and facilitate ethical reflexivity. Through iterative engagements, AI supported the formulation of sophisticated research questions and bolstered academic writing. It also aided emotional resilience in traversing heteronormative and interdisciplinary landscapes. The study highlights the evolving role of supervisors, not as gatekeepers but as collaborators in AI-informed learning. Significant emphasis was placed on prompt engineering, scholarly scrutiny and academic integrity. Ethical guidelines and rigorous documentation practices ensured a responsible AI application without sacrificing originality. Contribution: The findings reveal that AI-augmented supervision promotes deeper theoretical engagement and enhances self-directed learning. It also introduces new pedagogical possibilities for complex research endeavours. Nonetheless, the study also underscores the challenges of bias, overreliance and contextual insensitivity inherent in AI outputs. By suggesting actionable strategies for ethical integration, this paper contributes to emerging global discussions on AI in higher education. It presents a framework for inclusive, transformative and contextually aware supervision practices.

Artificial Intelligence (AI) is transforming mental health service delivery within public health systems, offering innovative tools for assessment, intervention, and policy-level decision-making. Children with special needs—including those with developmental, learning, and neurodiverse conditions—often face challenges in accessing timely mental health support, leading to delayed identification of emotional and behavioral difficulties and widening disparities in care. AI-driven mental health support provides opportunities to bridge these gaps by enabling early detection, personalized interventions, and continuous monitoring of psychological well-being. Aligned with the World Health Organization (WHO) framework, which emphasizes mental health promotion, prevention, early intervention, and community-based care, AI applications such as predictive analytics, digital screening tools, and adaptive therapeutic platforms can enhance the accuracy and efficiency of mental health assessments. These technologies also allow mental health professionals and educators to monitor progress, adjust interventions in real time, and provide scalable support in school and community settings. Similarly, the National Education Policy (NEP) 2020 highlights inclusive education, early identification of learning difficulties, and the integration of technology to provide personalized learning experiences. AI can support these goals by facilitating individualized educational and psychological plans, improving access to assistive technologies, and enhancing engagement for learners with special needs. While the potential benefits of AI are significant, ethical, legal, and policy challenges must be addressed. Issues such as data privacy, algorithmic bias, informed consent, equitable access, and over-reliance on technology require careful consideration. Human oversight, interdisciplinary collaboration, and evidence-based regulation are critical to ensuring that AI tools complement, rather than replace, traditional mental health services. This research work aims to explore AI-driven mental health support from a public health perspective, emphasizing the integration of technological innovation with ethical practice, inclusive education, and policy frameworks. By examining the intersection of AI, mental health, and special education, the discussion seeks to advance strategies for responsible, equitable, and effective mental health support for children with special needs, ensuring their holistic development and psychological well-being at a population level.

Abstract Acute ischemic stroke (AIS) represents a major global health burden, with incidence projected to reach 89.32 per 100,000 people by 2030. This systematic review examines how artificial intelligence (AI), particularly machine learning and deep learning, can enhance AIS care through improved diagnostic accuracy and outcome prediction. We synthesize evidence on AI applications in lesion segmentation and functional outcome forecasting, with emphasis on clinical translation and relevance for diverse healthcare settings. Current literature demonstrates that AI-assisted approaches achieve clinically meaningful performance, with segmentation models frequently showing Dice coefficients exceeding 0.85 and outcome prediction models achieving area under the curve values above 0.80. Integration of multimodal data, combining imaging features with clinical parameters such as Barthel Index, Modified Rankin Scale, National Institutes of Health Stroke Scale, and Functional Independence Measure consistently enhances predictive accuracy. However, significant challenges persist, including demographic biases in training data, limited generalizability across populations, and reliance on small, single-center datasets. Ethical considerations around algorithmic fairness and the need for explainable AI in clinical decision support are crucial for equitable implementation. Successful clinical translation requires addressing workflow integration, validation in real-world settings, and development of approaches suitable for resource-limited environments. This review highlights the transformative potential of AI in stroke care while emphasizing the need for robust clinical validation and equitable deployment to ensure improved patient outcomes across diverse healthcare contexts.

Diabetes mellitus is a fast growing universal public health concern that greatly increases sickness, mortality, and economic burden, especially in low- and middle-income areas like South Asian nations. The rising incidence of diabetes and the shortcomings of traditional healthcare delivery methods accentuate the pressing need for creative, scalable, and easily accessible alternatives. The growing role of artificial intelligence (AI) and machine learning (ML) in the diabetes care weighbridge including early risk prediction, diagnosis, individualized treatment, real-time monitoring, and complication prevention is studied in this paper. To evaluate worldwide and SAARC specific changes in diabetes prevalence and estimated disease burden, a secondary data based comparative study was carried out using epidemiological data from the International Diabetes Federation (IDF) Diabetes Atlas. Concurrently, a comprehensive review of current AI and ML-driven diabetes management applications was conducted, with a focus on predictive modeling, AI assisted screening, and new glucose monitoring technologies like continuous glucose monitoring and flash glucose monitoring. The results show that the prevalence of diabetes is suspiciously high and rising quickly among SAARC countries. Models based on AI and machine learning show great promise for early detection, better glycemic control, better drug adherence, and prompt identification of problems associated with diabetes, particularly diabetic retinopathy.

The global poultry industry is a critical sector, tasked with meeting the increasing demand for animal protein. Despite its growth and efficiency, it faces challenges, including enhancing productivity, optimizing resource utilization, and ensuring animal welfare. Addressing these challenges requires innovative solutions to improve both the efficiency and sustainability of poultry production. This paper presents an in-depth analysis of how advancements in artificial intelligence (AI) and machine learning (ML) technologies are being integrated into poultry farming to revolutionize its practices. We explore the application of AI in monitoring systems, smart poultry houses, and automated management practices that significantly enhance production metrics and animal welfare. Our study delves into various AI-driven methods, such as predictive modelling, real-time environmental monitoring, and precision feeding systems. Furthermore, the research identifies the current limitations and future potential of these technologies in facilitating a shift towards more responsive and responsible poultry farming practices. Our findings suggest that embracing AI technologies not only contributes to the economic viability of poultry farms but also aligns with ethical standards and sustainability goals, indicating a promising direction for the future of poultry farming.

This study was conducted within the context of the KI meets vhb project funded by the Virtuelle Hochschule Bayern, which addresses the use of artificial intelligence applications in university-based teacher education. Despite the increasing use of chatbots in teacher education programs, there is a lack of comprehensive and psychometrically validated instruments to assess pre-service teachers’ perceptions of different types of education chatbots. To address this gap, the present study reports the development and validation of a scale designed to measure pre-service teachers’ perceptions of different types of chatbots used in educational contexts. The technology acceptance model (TAM, TAM 3) and the value-based adoption model (VAM) served as the theoretical foundation in the development of the scale items. Data were collected from 224 German pre-service teachers enrolled in university-based teacher education programs. Exploratory and confirmatory factor analyses supported a four-factor structure, with strong model fit indices. Criterion-related validity provided initial support for the scale, as significant associations with chatbot usage frequency were observed for all dimensions except perceived risk. The four-factor structure of the scale was further confirmed in an independent sample of 263 in-service teachers in Türkiye, demonstrating the robustness of the model across different teacher populations. Overall, the <i>G-CAVS</i> scale emerged as a valid and reliable instrument for assessing perceptions of chatbots in teacher education contexts, with implications for broader pre-service teachers’ populations beyond the present samples.

Artificial intelligence (AI) is transforming modern dentistry, particularly in prosthodontics. However, evidence regarding AI integration among dental professionals in Jordan remains limited, while insights into educational and clinical gaps affecting AI adoption remain unknown. Therefore, the objective of this study was to evaluate the knowledge, awareness, and perception of AI-driven prosthodontics among general dental practitioners and specialists in Jordan. A cross-sectional design was chosen for this study using a self-administered questionnaire to evaluate knowledge, awareness, and perceptions of AI technologies in fixed and removable prosthodontics among dental practitioners in Jordan. Study participants were recruited using a convenience sampling technique. The study consisted of an anonymized questionnaire containing multiple choice, Likert scale and open- ended questions. The survey assessed demographic data, knowledge of AI applications, awareness of specific tools, and perceptions toward benefits, limitations, and ethical considerations of AI in prosthodontics. The association between the categorical variables was assessed using the chi-squared test of independence. The Spearman rank correlation test was used to assess the correlation between variables. A p-value of 0.05 was set as the level of significance. A total of 385 dental professionals participated in the survey (192 specialists in prosthetic dentistry and 193 general dental practitioners). Specialists reported higher awareness and use of AI in prosthodontics, including patient education, treatment planning, 3D implant positioning and quality control (P<0.001). Overall knowledge scores were moderate (78.72 ± 9.93), while general practitioners showed higher agreement for general diagnostic use (P<0.001). In Jordan dental professionals have moderate knowledge of AI in prosthodontics, with younger clinicians and specialists showing higher awareness and comfort. The perception of AI is mostly positive. However, actual adoption in practice remains limited.

Purpose This study aims to comprehensively examine the integration of artificial intelligence (AI) and building information modeling (BIM) within the building construction field and identify four key enablers to develop PERGE as an AI–BIM adoption framework. It aims to evaluate the applicability of AI methods, including generative methods, and identify emerging trends and underexplored combinations of AI methods and use cases. Design/methodology/approach A scientometric methodology was adopted to establish the AIBI dataset, including 971 peer-reviewed publications, and analyze them based on a computational review and evidence gap maps (CEGMs) approach. A structured query was designed to identify relevant investigations, which were then analyzed to map publication trends, identify dominant AI applications in buildings, perform temporal analysis of recent developments and develop a construct–outcome heatmap. Findings The analysis reveals a significant evolution in AI–BIM integration for buildings, with a shift from early automation tasks to more advanced objectives such as generation, prediction and semantic understanding. There is a notable rise in the use of large language models, reinforcement learning and fine-tuned transformers. The study also identifies a transition in methodological focus from general prediction tasks to the development of algorithmic frameworks tailored to facility needs. Generative AI has notably influenced expectations and applications in the field while also exposing gaps in underutilized areas. Originality/value This paper provides a novel, data-driven synthesis of AI–BIM integration investigations in building construction, energy and facility management, with a particular emphasis on the transformative role of generative AI. The novel adoption framework of PERGE is established, offering valuable insights for researchers and practitioners along with the identification of key trends, suitable AI methods and underexplored opportunities.

Introduction This scoping review examines recent peer-reviewed literature (2019–2025) on the role of artificial intelligence (AI) in managing nutrition care for post-periodontal surgical patients, and identifies key risk factors influencing nutritional outcomes after periodontal surgery. AI modalities considered include machine learning, expert systems, clinical decision support, and predictive analytics. Methodology A systematic search of databases (e.g., PubMed, Scopus) identified studies on AI applications in periodontology, nutrition, or wound healing. The inclusion criteria were English-language, peer-reviewed publications from 2019 onwards that focused on AI in periodontal care or nutritional management, and studies addressing risk factors (such as age, comorbidities, dietary compliance, oral function, socioeconomic status, etc.) that affect post-surgical nutrition or healing. Data were charted on study characteristics, AI type, nutritional outcomes, and reported risk factors. 28 publications were included (10 original studies, eight reviews, five clinical reports, five conceptual papers). AI has been used in periodontal care for diagnostics, prognostics, and decision support. Results Machine learning models can predict healing and nutritional risks by analyzing patient data, with key risk factors including age, comorbidities such as diabetes, poor nutrition, low dietary compliance, oral function, and socioeconomic status. Older, chewing-impaired patients have lower nutrient intake and a higher risk of malnutrition. Poor pre-surgery nutrition delays healing. AI models forecast outcomes, identifying baseline pocket depth and antibiotic use as strong predictors. Emerging AI tools in periodontology can enhance nutrition management through early risk detection and personalized diets. Conclusion Factors like age, health, oral function, and socioeconomic status affect recovery. Using AI risk assessments with nutritional plans may improve healing. More research is needed to realize AI's full potential. While direct studies are limited, emerging evidence indicates strong potential for personalized, AI-supported nutritional care.

Artificial intelligence (AI) applications in endoscopy, particularly computer-aided detection (CADe), have shown consistent benefit in randomized controlled trials (RCTs), with improvements in adenoma detection rate (ADR) and reductions in adenoma miss rate (AMR). Despite these findings, adoption of CADe in routine colonoscopy remains controversial, with international guidelines issuing divergent recommendations. Evidence from RCTs demonstrates that CADe increases ADR, predominantly through detection of diminutive adenomas, while its effect on advanced adenomas is limited. Real-world implementation studies show comparatively diminished benefits, likely explained by factors which are difficult to measure, such as the absence of Hawthorne effect in real-world practice, the quality of mucosal exposure and decision-making regarding diminutive polyps. Cost-effectiveness analyses generally favour CADe even with varying assumptions across healthcare systems, although these are based on the high degree of improvement in ADR seen in RCTs with CADe. Potential harms include increased polypectomy of non-neoplastic lesions, higher lifetime colonoscopy burden, and the risk of deskilling among endoscopists. Concerns remain about bridging the gap between trial efficacy and real-world effectiveness, optimizing surveillance intervals, and mitigating deskilling and human-AI interaction issues. (1) CADe improves ADR in RCTs, but real-world effectiveness is inconsistent and often lacklustre. (2) Gains in ADR are largely derived from diminutive adenomas, and less with advanced adenomas, with uncertain impact on clinically significant outcomes such as colorectal cancer incidence and mortality. (3) Cost-effectiveness analyses are generally favourable, but dependent on assumptions about ADR improvement, CADe cost, and surveillance policies. (4) Deskilling and altered endoscopist behaviour represent important considerations that require further study. (5) Future integration of CADe with computer-aided diagnosis (CADx) and quality-assurance (CAQ) tools may maximize clinical benefit and cost-effectiveness, but evidence gaps must be addressed before widespread implementation.

This paper discusses the evolution of digital transformation in banking beyond the simple application of the latest technologies. It argues that long-term success requires an integrated strategy focused on people, technology and business implementation, balancing enhanced customer experience with operational efficiency and embedding the ‘human touch’ in digital architectures, while remaining inclusive across the customer adoption curve. The paper examines transformation building blocks (people and skills, technology infrastructure and business implementation); looks at typical pitfalls of partial archetype scenarios in contrast with an ideal, integrated approach; and integrates insights from recent industry reports.1,2,3,4,5,6,7 It further details four strategic transformation paths defined by the Massachusetts Institute of Technology Center for Information Systems Research,8,9 with guidance on selection. The discussion is grounded in practical transformation experience, including the strategic application of artificial intelligence (AI). The paper finds that comprehensive digital transformation requires tackling cultural and organisational agility, in addition to needed technological upgrades and successful business implementation. It is essential to select the appropriate strategic pathway and thoughtfully integrate AI technology. Achieving a healthy balance between high-tech capability and high-touch, inclusive engagement continues to be a major challenge. The paper brings real-world insights, condensed into top imperatives for banking professionals to drive their transformation initiatives, and points out the importance of balancing people, technology and implementation, as well as human-centred design, adaptive leadership and AI, for value creation. This article is also included in The Business &amp; Management Collection which can be accessed at https://hstalks.com/business/.

Introduction The applications of artificial intelligence (AI) in healthcare are very diverse. AI-based systems can assist with diagnosis and decision-making, particularly in intensive care medicine. However, physicians must accept these systems to fully exploit their potential. We investigated attitude and perception toward AI among physicians with intensive care experience. Methods A cross-sectional questionnaire survey was conducted between August and October 2024 among 7,475 physicians with intensive care experience. Participants were recruited via the hospital operator Knappschaftskliniken GmbH, the German Sepsis Society and via an address register. The questionnaire collected background information on the participants as well as their attitude toward and perception to AI. Their general attitudes toward AI were assessed using the validated Attari-12 tool. Questions specifically addressing attitude and perception of AI in healthcare were developed independently. Descriptive statistics and subgroup analysis were conducted. Results Of the 7,475 physicians initially contacted, 620 returned the questionnaire. Of these, 445 questionnaires were included in the evaluation. Most were male (81.8%) aged over 50 years in leadership positions (92.1%). In both cases, general and health care specific, the attitude toward AI was rather positive. The majority of physicians asked for AI applications that are comprehensible to the treating physicians (87.1%) and agreed that objective values alone are not always sufficient for making medical decisions (87.3%). Furthermore, physicians faced problems in finding reliable information about AI in healthcare (52.6%) and only 21.6% considered communication about AI in the medical community as appropriate. Subgroup analysis revealed few differences for age and gender. The correlation between conscious use of AI in a professional context and attitude toward it was notable. Discussion Physicians with intensive care experience generally hold a positive attitude toward AI, particularly in healthcare. However, the sample was predominantly male, older, and in leadership positions, so these findings may not fully reflect the attitudes of younger or female physicians. Several considerations were highlighted: AI outputs should be interpretable, clinical decisions cannot rely solely on objective data, and physicians need reliable information and guidance for further AI education. Leveraging the positive attitude could help make healthcare systems more efficient, effective, and sustainable.

Background Artificial intelligence (AI) has fundamentally transformed tourism and hospitality marketing through enhanced data-driven decision-making, personalized customer experiences, and intelligent destination management. However, the field lacks a comprehensive synthesis of its intellectual landscape and thematic evolution, limiting understanding of research trajectories and emerging directions. Methods A systematic literature review following the SPAR-4-SLR procedure was conducted on 320 peer-reviewed papers published between 2003 and 2025, sourced from the Scopus database. Publication trends, leading journals, prolific authors, trending areas, and bibliographic coupling of documents and countries were visualized using bibliometric analysis tools (VOSviewer and Biblioshiny). Thematic analysis employed keyword co-occurrence networks to identify emerging research themes. Results Academic publications on AI in tourism and hospitality demonstrated a significant surge during 2017–2020, reflecting the industry’s growing emphasis on smart marketing applications. Thematic analysis identified four major research clusters: (i) Digital Influence and Tourist Behaviour Analytics; (ii) AI-Enabled Smart Tourism and Commerce Ecosystems; (iii) Technology-Driven Hospitality and Experience Innovation; and (iv) Data-Driven Decision Making in Predictive Tourism Modelling. Conclusions This bibliometric and thematic assessment reveals the evolving intellectual landscape of AI applications in tourism and hospitality marketing, documenting substantive research growth and the emergence of distinct thematic clusters that shape current and future research agendas in this dynamic field.

Artificial intelligence (AI) is increasingly used in medicine, including pediatric endocrinology. AI models have the potential to support clinical decision-making, patient education, and guidance. However, their accuracy, reliability, and effectiveness in providing medical information and recommendations remain unclear. The aim was to evaluate and compare the performance of four AI models, ChatGPT, Bard, Microsoft Copilot, and Pi, in answering frequently asked questions related to pediatric endocrinology. Nine questions commonly asked by parents regarding short stature in pediatric endocrinology were selected, based on literature reviews and expert opinions. These questions were posed to four AI models in both Turkish and English. The AI-generated responses were evaluated by 10 pediatric endocrinologists using a 12-item Likert-scale questionnaire assessing medical accuracy, completeness, guidance, and informativeness. Statistical analyses, including Kruskal-Wallis and post-hoc tests, were conducted to determine significant differences between AI models. Bard outperformed other models in guidance and recommendation categories, excelling in directing users to medical consultation. Microsoft Copilot demonstrated strong medical accuracy but lacked guidance capacity. ChatGPT showed consistent performance in knowledge dissemination, making it effective for patient education. Pi scored the lowest in guidance and recommendations, indicating limited applicability in clinical settings. Significant differences were observed between AI models (p<0.05), particularly in completeness and guidance-related categories. The present study highlights the varying strengths and weaknesses of AI models in an area of pediatric endocrinology. While Bard was effective in guidance, Microsoft Copilot excelled at accuracy, and ChatGPT was informative. Future AI improvements should focus on balancing accuracy and guidance to enhance clinical decision-support and patient education. Tailored AI applications may optimize the role of AI in specialized medical fields.

This study presents a systematic literature review examining the transformative role of artificial intelligence (AI) in corporate tax risk management. As globalization, regulatory scrutiny, and digitalization increase corporate tax complexity, AI technologies including machine learning, predictive analytics, and automated compliance systems are reshaping how organizations identify, assess, and manage tax-related risks. The review synthesizes research across taxation, corporate governance, risk management, and algorithmic decision-making, exploring AI applications in enhancing tax compliance, fraud detection, tax planning, and governance frameworks. It reveals that AI offers significant potential to improve efficiency and effectiveness in tax risk management, yet successful implementation requires robust governance structures, ethical organizational cultures, and supportive regulatory environments. Critically, the study examines challenges associated with algorithmic decision-making, including transparency, fairness, accountability, and trust. It identifies persistent research gaps concerning developing economies, long-term organizational impacts, and unintended consequences of automated tax decisions. By consolidating fragmented literature streams, the paper provides a conceptual foundation for strategically integrating AI into corporate tax risk management and offers directions for future research and policy development in this evolving field.

Artificial intelligence (AI) is transforming health care by enhancing diagnostics, improving patient outcomes, and reducing administrative burdens through advanced algorithms, with applications in medical imaging, virtual care, and automated data analysis. However, its role in palliative and hospice care remains underexplored. This review synthesizes research on AI applications in palliative and hospice care, examining its technological and clinical contributions to inform future research and guide clinical implementation. An integrative literature review, guided by Whittemore and Knafl's framework, analyzed qualitative, quantitative, and mixed-method studies. Registered with PROSPERO. A comprehensive search across 11 databases: Academic Search Complete, CINAHL, Cochrane Library, PubMed, Medline, Web of Science, Scopus, PsycINFO, ProQuest Dissertations & Theses Global, ACM Digital Library, and IEEE Xplore, identified English-language studies published from 2010 to 2024. Studies on AI applications in clinical settings, model validation, and key findings were included, with quality assessed using the Mixed Methods Appraisal Tool. Seventy studies (2018-2024) were included, primarily quantitative analyses of retrospective clinical and administrative data. AI applications supported mortality prediction, symptom monitoring, patient needs identification, communication facilitation, care planning, and resource allocation. Early tools included rule-based and structured-data models, while more recent approaches integrate unstructured clinical notes, wearable devices, and multimodal data for individualized prognostication and timely interventions. Key barriers included reliance on retrospective or single-center datasets, limited generalizability, ethical and equity concerns, and challenges in integrating AI into clinical workflows. AI holds potential in enhancing timely, patient-centered palliative and hospice care, supporting prognostication, symptom management, and decision-making. Successful integration requires attention to clinician trust, workflow alignment, equity, and ethical considerations. To maximize its impact on underutilization, future research should focus on multicenter validation, representative datasets, ethical deployment, and seamless integration into clinical practice.

Artificial Intelligence (AI) is changing how recruitment works by applying technologies such as LinkedIn’s talent matching algorithms and Applicant Tracking Systems (ATS) that will improve fairness, accuracy, and efficiency in hiring. This paper, Ethical and Efficient Recruitment: The Role of Artificial Intelligence in Reducing Bias and Enhancing Talent Acquisition Decisions, explores how platforms powered by AI technologies can enhance the recruitment process by sourcing, screening, and selecting candidates while reducing the impact of unconscious bias. Traditional recruitment relies on subjective judgments of various candidates along with manual recruitment processes that lead to inefficiencies and the potential for discrimination; however, tools like LinkedIn Recruiter and ATS leverage robust data analytics, predictive modelling, selection algorithms, and skills-based matching to create objective assessments of candidates that continuously yield higher quality talent acquisition. AI takes on mundane tasks, filters candidates based on viable competencies, and uses scoring metrics for standardizing assessments; thus, ensuring talent acquisition can include more time for strategic thinking. There are ethical concerns with AI recruitment methods, including algorithmic biases, privacy, and the use of data in general. This paper will examine best practices for ethically implementing AI recruitment strategies including bias detection protocols, transparency in algorithm design, and diversity monitoring. With human oversight applications and AI technologies working together in the recruitment process, organizations can have an efficient process that is ethically aligned as well. The study suggests that AI applications used in sites such as LinkedIn and ATS can reduce bias and improve talent acquisition results when used with appropriate ethical norms and ongoing evaluations of performance.

Current applications of artificial intelligence and machine learning in oncologic ICU management

Objectives Artificial intelligence (AI) is increasingly being utilized across various fields of medicine, presenting significant potential for the future of healthcare. This review is to systematically outline the current applications of AI in the field of oral health management and to provide an in-depth analysis of the associated challenges and future opportunities. Methods The review was based on a systematic electronic literature search conducted across databases (PubMed, Web of Science, and Scopus) with the keywords including “artificial intelligence,” “AI in dentistry,” “tele-dentistry,” “oral health education,” and “oral health management.” English-language studies relevant to the application of AI across various aspects of oral health management were included based on independent assessments by two reviewers. Results We concluded that in the realm of oral health management, AI technology has diverse applications, including oral health education and counseling, monitoring, screening, diagnosis, treatment, follow-up care of oral diseases, and the collection and management of oral health data. By enhancing public awareness of oral health and improving self-management capabilities, AI can increase diagnostic accuracy, facilitate personalized treatments, support tele-dentistry, optimize the allocation of dental resources, and provide early warnings for oral diseases. These advancements collectively contribute to the efficiency and quality of oral health management. While AI demonstrates considerable promise in this field, several challenges remain, including inconsistencies in oral health data, limited availability and accessibility of data, the reliability of AI-driven results, and issues of bias and fairness in AI algorithms. Addressing these challenges is essential to fully harness the transformative potential of AI in oral health management. Conclusion Oral health management encompasses the comprehensive handling of oral health risk factors in individuals, populations, and communities through a series of measures and activities aimed at maintaining and promoting oral health. The ultimate goal is to achieve the greatest societal benefit in oral health at the lowest possible cost. By addressing challenges such as data consistency, availability, and reliability, as well as issues of bias and fairness in AI algorithms, AI may play a significant role in oral health management. Clinical relevance This paper reviews the role of artificial intelligence in the prevention, diagnosis and treatment of oral diseases, providing an important reference for the later application of artificial intelligence in oral health management.