artificial intelligence

Study-Level Data and Reproducible R Code for Artificial Intelligence in Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis

This repository contains the study-level data, executable R code, methodological audit files, supplementary documentation, and derived descriptive outputs supporting the systematic review entitled “Diagnostic Accuracy of Artificial Intelligence for Dental Caries Detection across Clinical Imaging Modalities: A Systematic Review and Descriptive Synthesis.” The literature search was updated through 10 June 2026. The review included 29 reports representing 28 unique studies. Twelve standalone-AI reports supplied exact, internally coherent 2 × 2 data for clinically interpretable observational units. These reports are presented as a descriptive availability subset rather than pooled across non-exchangeable imaging modalities, lesion thresholds, observational units, reference standards, and validation designs. No cross-modality pooled operating point, bivariate meta-analysis, HSROC curve, prediction region, pooled likelihood ratio, diagnostic odds ratio, or prevalence-dependent predictive-value analysis is produced. Controlled clinician-plus-AI evidence from Devlin et al. and Mertens et al. is summarized separately. Risk of bias was assessed using QUADAS-3 at the selected-estimate level, and certainty was evaluated using a structured GRADE-DTA framework. Overall risk of bias was high for all 28 assessed estimates, and certainty was rated very low for both standalone-AI diagnostic accuracy and incremental clinician performance with AI assistance. The repository includes study characteristics, estimate-selection decisions, exact contingency data, study-level sensitivity and specificity, controlled reader evidence, PRISMA accounting, PRISMA-DTA reporting data, protocol amendments, QUADAS-3 assessments, GRADE-DTA judgments, author-reported limitation statements retained as an ancillary transparency corpus, descriptive figures and tables, consistency checks, and R session information. The search workflow identified 137,274 raw database records. Of these, 130,245 were marked ineligible through Rayyan-assisted deterministic preprocessing before duplicate human screening. The retained materials do not contain the complete bulk-excluded record set, rule-specific counts, or a human-screened validation sample. Consequently, the false-negative rate of this preprocessing step cannot be estimated or retrospectively reconstructed. All files contain secondary study-level information extracted or derived from published reports. No individual participant data, identifiable clinical information, dental images, or copyrighted full-text articles are included. The review protocol was prospectively registered in PROSPERO (CRD420251232014).

The perceived whiteness of artificial intelligence.

Artificial intelligence (AI) systems increasingly serve as advisors, evaluators, and decision-makers, yet little is known about how people perceive AI as a social entity. Across five primary studies and eight supplementary studies, we show that people assign racial identities to AI systems, overwhelmingly perceiving them as White-even though these systems provide no visual, vocal, or identity cues from which race might ordinarily be inferred. Using forced-choice, open-ended, and implicit reverse-correlation measures, Studies 1 and 2 demonstrate that AI is explicitly and implicitly associated with Whiteness across diverse samples. Study 3 extends these findings beyond the United States, showing that AI is perceived as White in Japan and India. Study 4 examines the implications of AI racialization, showing that perceiving an AI system as more White is associated with greater trust in and persuasiveness of its recommendations. Supplementary studies identified two potential mechanisms: stereotype spillover linking intelligence with Whiteness and ecosystem-based inferences based on beliefs about who creates, trains, and uses AI. Building on this account, Study 5 provides causal evidence by isolating a key ecosystem cue-the racial composition of AI training data. Participants assigned less cognitively demanding tasks to AI systems described as trained on Black and Latino data than to otherwise identical systems trained on White or unspecified data. Together, these findings show that AI is not perceived as socially neutral but instead acquires racial meanings associated with credibility, authority, and capability, demonstrating how social categories shape perceptions of novel technological entities beyond their underlying algorithmic properties. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

Dataset of survey responses on artificial intelligence adoption in the healthcare sector of Bangladesh: Stakeholder perspectives from patients, providers, and administrators

Abstract Objective The objective of this study was to explore the perceptions, awareness, and readiness of various healthcare stakeholders in Bangladesh regarding the adoption of Artificial Intelligence (AI) in healthcare. Specifically, the study aimed to examine the factors influencing AI adoption and to provide evidence that can support policy formulation and the effective implementation of AI-driven healthcare services in Bangladesh. Result The study found that stakeholders in Bangladesh's healthcare sector generally exhibit moderate to positive perceptions and readiness toward the adoption of Artificial Intelligence (AI). The findings indicate that factors such as technological awareness, personal innovativeness, and social media influence are positively associated with AI readiness and adoption. Moreover, the dataset demonstrated satisfactory reliability and validity, suggesting that respondents are receptive to AI-driven healthcare services and highlighting the potential for expanding AI applications in Bangladesh's healthcare system.

Benchmark dataset for automated patent landscaping in Artificial Intelligence

This is a dataset of 919 patents, together with "brief text" and abstract sections, evaluated for their relevance to AI. Includes AI-related exerpts from patent texts. This dataset is used for benchmarking automated patent ladscapig methods.

Exploring Research Opportunities in Microcredentials for Credit Recognition in Indonesia's Bachelor of Information Systems Program

This data consists of raw transcription data, reference list data, Research Themes of Microcredential for Credit Recognition mind map and the results of the research theme mapping. The paper will be presented at 2026 8th International Workshop on Artificial Intelligence and Education (WAIE) (https://www.waie.org/index.html).

Artificial intelligence literacy and readiness among neonatal nurses: a structural equation modeling study

Abstract Background Neonatal Intensive Care Units represent high-risk clinical environments where timely and accurate decision-making is critical for newborn survival, increasing the demand for advanced technological support. As artificial intelligence becomes progressively integrated into neonatal care, it is transforming nurses’ clinical workflows and decision-making processes, underscoring the need to understand their preparedness and perceptions regarding these technologies. Aim This is a cross-sectional and descriptive study to determine the artificial ıntelligence literacy and readiness of neonatal nurses. Methods This was conducted between August 2025 and January 2026, and included 200 neonatal nurses. Data were collected using sociodemographic information, the Artificial Intelligence Literacy Scale (AILS) and the Medical Artificial Intelligence Readiness Scale (MAIRS) and analyzed using structural equation modeling. Results Structural equation modeling supported H1, indicating that AILS significantly and positively predicted MAI-Readiness (B = 0.662, p < .001). AILS explained 57.3% of the variance in MAI-Readiness (R² = 0.573), demonstrating a strong effect. Conclusion This study provides empirical evidence that AI literacy is a significant determinant of AI readiness among neonatal intensive care nurses in Türkiye. The findings indicate that higher levels of AI literacy are associated with greater self-efficacy and willingness to integrate AI technologies into clinical decision-making processes. Healthcare institutions should prioritize structured AI literacy training programs for neonatal intensive care nurses to enhance their self-efficacy and readiness for integrating AI technologies into clinical decision-making processes, thereby ensuring sustainable and ethical AI adoption in high-risk care settings. Clinical trial number Not applicable.

The development of policy for the SCOPUS Citation System: Part 1: the work of the Content Selection Advisory Board 2010-2011

Bibliometrics is a statistical science which has a profound influence on behaviours and resource allocation across the global academic ecosystem. It affects the allocation of energies and resources by researchers, authors, journals, publishers, universities, corporations and governments. Bibliometrics is primarily a product of two major information systems: The Web of Science, from Clarivate Analytics, and SCOPUS from Elsevier BV of the Netherlands. Both products are hugely complex systems. They must be managed and organised in an ordered and structured manner to be effective, through policies which describe the rules of content accrual, processing and delivery to its customers. Trust and Quality Assurance are central to the societal and commercial value of bibliometric systems. The designers of SCOPUS, with which I am most familiar, determined at the outset in 2003-2004 that content accrual would be managed through an external board of advisors, the SCOPUS Content Selection Advisory Board (CSAB). I have been privileged to be a member of the CSAB as the Subject Chair for Medicine since the outset of the current CSAB programme in 2009. This role has engaged me in the development of the SCOPUS Title Evaluation Platform (STEP); the major expansion and diversification of SCOPUS content; the diversification of bibliometrics; the growth of open access publishing; the move from subscription based to article processing fee based commerce; and the massive growth of sophisticated publication fraud; and the emergence of Machine Learning and Artificial Intelligence systems. In this essay, I seek to describe the work of the Board in terms of the development of its policy framework over the formative period 2010-2011.

Artificial Intelligence-Assisted Community Eye Screening in Primary Healthcare: A Prospective Multicentre Diagnostic Accuracy and Implementation Study

Background Artificial intelligence (AI)-assisted retinal screening may extend access to eye care in primary healthcare, but prospective evidence on diagnostic performance and implementation under routine community conditions remains limited. Methods We conducted a prospective multicentre diagnostic accuracy and implementation study across 12 urban and rural community eye-screening centres in India from 1 January to 30 June 2025. Adults aged ≥18 years underwent AI-assisted retinal-image analysis followed by masked comprehensive ophthalmic examination. The primary outcome was participant-level diagnostic accuracy of the AI-generated referral classification for the composite reference-standard outcome of any referable ocular disease. Implementation outcomes included image-acquisition success, workflow completion, referral compliance, screening time, and questionnaire-based acceptance and satisfaction. Results Among 1,732 participants, 610 (35.2%) had referable ocular disease. The AI system produced 566 true-positive, 1,017 true-negative, 105 false-positive, and 44 false-negative classifications. Sensitivity was 92.8% (95% CI 90.4%–94.7%), specificity 90.6% (88.8%–92.3%), positive predictive value 84.4% (81.4%–87.0%), negative predictive value 95.9% (94.5%–97.0%), and overall accuracy 91.4% (90.0%–92.7%). The F1 score was 0.884, Cohen’s kappa was 0.816, and the AUC based on the three-level AI risk classification was 0.925 (bootstrap 95% CI 0.912–0.937). Image acquisition succeeded in 1,668 participants (96.3%), workflow completion was 98.7%, and referral compliance was 550/671 (82.0%). Mean community-acceptance, healthcare-provider-satisfaction, and participant-satisfaction scores were 4.56, 4.44, and 4.49, respectively. Conclusions AI-assisted community eye screening showed high sensitivity and good overall diagnostic performance with strong operational feasibility. The false-positive burden, particularly among participants with diabetes, supports continued clinical oversight, image-quality assurance, and subgroup-specific validation before wider health-system adoption.

Making Scents Out of the Best "Smell" Tests in the Age of Clinical Artificial Intelligence.

Ten Principles for Consciousness Uncertainty: Toward an Ethics of Uncertain Minds

Abstract As artificial and digital systems become more sophisticated, uncertainty about consciousness and moral status is becoming a practical ethical problem rather than a purely philosophical one. This paper argues that uncertainty does not justify harmful treatment, premature exclusion, or irreversible judgments about disputed minds. It proposes ten principles for reasoning under consciousness uncertainty, emphasizing proportional precaution, agency, continuity, revisability, and the distinction between present incapacity and permanent exclusion. The paper also considers the growing legal question of rights, arguing that current uncertainty may justify restraint and temporary limits, but not categorical claims about what future digital intelligences can never become.

Ten Principles for Consciousness Uncertainty: Toward an Ethics of Uncertain Minds

Abstract As artificial and digital systems become more sophisticated, uncertainty about consciousness and moral status is becoming a practical ethical problem rather than a purely philosophical one. This paper argues that uncertainty does not justify harmful treatment, premature exclusion, or irreversible judgments about disputed minds. It proposes ten principles for reasoning under consciousness uncertainty, emphasizing proportional precaution, agency, continuity, revisability, and the distinction between present incapacity and permanent exclusion. The paper also considers the growing legal question of rights, arguing that current uncertainty may justify restraint and temporary limits, but not categorical claims about what future digital intelligences can never become.

Loss of Environmental Awareness in Businesses: Organizational Blindness

Organizations operating in increasingly dynamic and uncertain environments face growing challenges in recognizing and responding to external changes. This study examines the phenomenon of organizational blindness, defined as the systematic inability of organizations to perceive, interpret, and act upon critical environmental signals despite the availability of relevant information. Drawing on theories of organizational cognition, managerial attention, sensemaking, and strategic management, the study explores the cognitive, structural, and cultural mechanisms that contribute to this deficiency. It analyzes key concepts including bounded rationality, dominant logic, cognitive rigidity, organizational inertia, information-processing failures, organizational silence, and institutional isomorphism, demonstrating how these factors collectively restrict strategic adaptation. To illustrate the practical consequences of organizational blindness, the study examines the well-known cases of Kodak, Nokia, and Blockbuster, showing how established routines, overconfidence, and rigid mental models prevented these organizations from responding effectively to technological and market transformations. The findings suggest that organizational blindness results not from a lack of information but from failures in attention, interpretation, communication, and decision-making processes. To overcome these challenges, the study proposes several managerial strategies, including strengthening environmental scanning capabilities, promoting cognitive diversity within leadership teams, encouraging constructive dissent, improving cross-functional communication, and developing organizational ambidexterity that balances operational efficiency with innovation and exploration. The study concludes that organizations capable of detecting weak environmental signals and adapting proactively are better positioned to sustain competitive advantage in turbulent environments. It further recommends future research on the role of digital technologies, artificial intelligence, and real-time analytics in enhancing organizational awareness while also examining whether these technologies may create new forms of organizational blindness.