Search Results (495)

Accurate classification of lymphoma cell subtypes is essential for disease diagnosis and therapeutic decision-making, yet conventional approaches often rely on fluorescence labeling, labor-intensive sample preparation, and specialized instrumentation, limiting their applicability for rapid, label-free single-cell analysis. Here, we present an AI-assisted microfluidic light-scattering imaging platform for label-free classification of lymphoma cells. The platform integrates hydrodynamic focusing within a microfluidic chip, continuous acquisition of two-dimensional (2D) light-scattering patterns, automated image preprocessing, and transfer learning based on a pretrained ResNet50 network for intelligent optical feature extraction and classification. Human B lymphoma (Daudi) and T lymphoblastic lymphoma (SUP-T1) cells were used to evaluate the proposed framework. The optical imaging system was first validated using standard microspheres, demonstrating reliable acquisition of light-scattering patterns under continuous-flow conditions. A dataset comprising 800 single-cell scattering patterns was subsequently established and evaluated using stratified five-fold cross-validation. The proposed framework achieved an average classification accuracy of 94.75% with an average area under the receiver operating characteristic (ROC) curve of 0.986. By integrating microfluidic optical biosensing with deep learning, this work enables automated interpretation of intrinsic optical scattering signatures and provides a promising AI-enabled strategy for rapid, label-free lymphoma screening and intelligent healthcare applications. Full article

Agentic artificial intelligence (AI) systems—software that plans, acts, and decides without step-by-step human approval—are already deployed in finance, healthcare, recruitment, and public services. They differ from earlier AI in one critical way. They do not produce outputs for a human to accept or reject. They take actions. Those actions may be irreversible. They may affect people who never used the system. Existing governance frameworks were not designed for this. Current alignment approaches—including reinforcement learning from human feedback, constitutional AI, and preference aggregation—assume that a well-aligned system satisfies what users ask for. This paper argues that the assumption fails in agentic contexts. What people ask for is not what they value. Preferences are volatile and user-centric. Values are stable, culturally grounded, and other-regarding. This paper proposes the Human Values Alignment Framework for Agentic AI (HVAF-A), grounded in Schwartz’s cross-culturally validated Basic Human Values theory. The framework connects value inputs, three alignment mechanisms (elicitation, arbitration, and propagation), and governance outcomes at individual, organizational, and societal levels. The paper positions the HVAF-A against the main international policy instruments—the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, and the OECD and UNESCO principles—and specifies an empirical program of constructs, measures, validity tests, and study designs. An illustrative case study of an agentic hiring system shows how the framework would operate. This is a purely conceptual study. No study was conducted and no instrument was administered to validate the model. A dedicated section states the framework’s boundary conditions. Empirical validation is set out as future research. Full article

Purpose: Explainable artificial intelligence (XAI) is increasingly promoted as a mechanism for improving transparency, trust, and accountability in AI-assisted decision-making. However, its ethical value remains uncertain when AI systems are embedded within human teams and organizational workflows. This systematic literature review examines ethical issues associated with XAI in human–AI team contexts and explores how trust, decision-making, and accountability are associated with the use of XAI systems across organizational domains. Methodology: Guided by the SALSA framework and PRISMA 2020 guidelines, the review synthesized 25 studies drawn from healthcare, business and finance, cyber–physical systems, Earth Observation, and broader human–AI collaboration contexts. Findings: The findings revealed four interrelated themes: the paradox of explainability, in which transparency does not necessarily produce understanding; trust as a dynamic and fragile process shaped by user expertise, task stakes, and workflow integration; distributed accountability and the unresolved ethics of responsibility in human–AI collaboration; and bias, fairness, and the limits of explainability as a standalone ethical solution. Across the reviewed literature, XAI did not function as a self-sufficient safeguard against ethical risk. Rather, its ethical value depended on how explanations were designed, interpreted, coordinated within teams, and governed by organizations. Based on these findings, the study proposes the Sociotechnical Explainability Alignment (SEA) framework, which conceptualizes ethical XAI as requiring alignment across four levels: design, user, team, and organization. The review contributes to XAI scholarship by reframing explainability as a sociotechnical capability rather than a purely technical feature. It also offers practical guidance for organizations seeking to implement AI systems responsibly through human-centered design, trust calibration, role-sensitive explanation practices, and governance mechanisms that support accountability and fairness. Full article

AI-driven smart healthcare platforms increasingly combine multimodal data acquisition, remote monitoring, predictive analytics, longitudinal dashboards, and role-specific information services. While these capabilities enable more continuous and personalized health monitoring, they also introduce accessibility challenges that extend beyond conventional user-interface design, particularly in how monitoring results and AI-generated outputs are structured, contextualized, and communicated. This paper addresses the gap in operationalizing accessibility at the system level by proposing a standards-mapped Accessibility-by-Design framework for AI-enabled smart healthcare platforms. Requirements derived from international accessibility, human-centred design, and software quality standards are translated into traceable architectural and component-level constraints and integrated into the development workflow. The methodology combines standards-to-requirement-to-component traceability mapping, architectural documentation analysis, component inspection, and controlled workflow walkthroughs. The framework is applied to the NeuroPredict platform, an AI-based environment for longitudinal and multimodal monitoring of neurodegenerative disorders. Accessibility is operationalized through cross-layer architectural mechanisms, including semantic interface components, predictable interaction workflows, accessible presentation mechanisms, and structured representation of monitoring and AI outputs. AI-generated results are presented in a structured form together with contextual, temporal, source-related, and explanatory information to support role adaptation; uncertainty-related fields are reserved for future validated user-facing integration. Implementation evidence from selected platform workflows demonstrates that the proposed mechanisms can be incorporated into the current platform architecture and provides preliminary support for their technical feasibility. The evidence remains limited to implemented and inspected mechanisms, without user-based accessibility validation, formal conformance auditing, clinical deployment, or regulatory certification. These results illustrate that, while maintaining the distinction between accessibility mechanisms and the underlying analytical computation, accessibility-by-design can offer an engineering foundation for integrating accessibility requirements into the architecture and development lifecycle of AI-driven smart healthcare platforms. Full article

Artificial intelligence (AI), especially generative AI, is becoming deeply embedded in healthcare, making doctor–AI collaborative diagnosis a common mode of medical decision-making. This development raises a critical question about responsibility attribution: When people perceive that a diagnostic error has occurred, how does AI involvement shape patients’ and observers’ judgments of the doctor’s responsibility? Drawing on responsibility attribution theory, we examine this question across five studies—one event-related potential (ERP) experiment and four scenario experiments. We find that when a diagnostic error is perceived, doctor–AI collaborative diagnosis (vs. doctor-only diagnosis) reduces perceived doctor responsibility by increasing perceived shared agency. This responsibility-reducing effect is weaker when the doctor rejects correct AI advice than when the doctor accepts incorrect AI advice. Theoretically, our findings show that responsibility attribution in human–AI collaboration involves two stages: agent identification and responsibility allocation. This account extends responsibility attribution theory to human–AI collaboration and identifies perceived shared agency as a key psychological mechanism underlying responsibility judgments in these settings. Practically, the findings can inform technology deployment, responsibility communication, and governance mechanisms in hospitals, AI firms, and regulatory agencies. Full article

The integration of artificial intelligence (AI) into digital histopathology has the potential to improve the accuracy and efficiency of metastatic cancer diagnosis. We evaluated LYDIA (LYmph noDe assIstAnt), an AI-based decision-support system, for both standalone diagnostic performance and its impact on histopathologists’ workflow, diagnostic accuracy, and resource utilization. LYDIA was evaluated on a blinded dataset of 366 whole-slide images (WSIs) from breast, colorectal, lung, and skin cancers. Standalone performance demonstrated excellent discrimination, achieving ROC-AUC values of 0.995, 0.963, 0.973, and 0.983 for breast, colorectal, lung, and skin cancers, respectively. Clinical utility was further assessed in a multi-reader study involving four experienced histopathologists interpreting 105 WSIs with and without AI assistance. AI-assisted diagnosis significantly reduced time-to-diagnosis across all metastasis sizes, with a maximum 1.59-fold acceleration for micro-metastases, corresponding to a mean time saving of 26.5 s per WSI. LYDIA also improved diagnostic sensitivity from 77.3% to 87.3%. These findings demonstrate that LYDIA can enhance both the efficiency and accuracy of lymph node metastasis detection while reducing diagnostic workload and the need for ancillary testing. Beyond improving routine pathology workflows, the system’s rapid inference capabilities and human-expert level performance may support future intraoperative diagnostic applications, enabling timely and automatic or semi-automatic assessment of nodal status to inform surgical decision-making. The resulting reductions in diagnostic time and ancillary testing costs have the potential to improve healthcare resource utilization and patient care, mainly in soft tissue reconstruction and surgical repair decisions. Full article

Recent industry reporting indicates that meantime to exploit has become negative in several observed datasets, implying that exploitation may occur before patch availability for some classes of vulnerabilities. Adversarial use of artificial intelligence (AI) is a documented accelerant of this trend. This paper addresses the operational problem that follows in healthcare cybersecurity: the volume and velocity of vulnerability disclosure exceed human analytic capacity, which leads practitioners to under-prioritize, or defer entirely, individual Common Vulnerabilities and Exposures (CVEs) at precisely the moment their risk is rising. The study develops and evaluates a purposeful information technology artifact intended to resolve this problem within a mid-sized United States healthcare system. The artifact is a three-application automated CVE intelligence, prioritization, and remediation-tracking pipeline implemented in Microsoft Azure Logic Apps, integrating the National Vulnerability Database (NVD), the CISA Known Exploited Vulnerabilities (KEV) catalog, the Microsoft Security Response Center (MSRC) CVRF API, Microsoft Defender, Claroty xDome, Microsoft Security Copilot, and ServiceNow, and operationalizing the four risk factors codified in CISA Binding Operational Directive (BOD) 26-04. In naturalistic operations across six CISA Weekly Vulnerability Summary bulletins, the artifact processed 12,855 unique CVE references and reduced them to 1640 environment-relevant findings, an 87.2 percent exposure-first reduction, before expensive per-CVE enrichment and ticketing. The findings indicate that governed automation demonstrably increases CVE coverage, reduces low-value enrichment volume, and produces a deterministic, BOD 26-04-conformant prioritization that is fully traceable in the SharePoint tracker, where every assigned tier is reconstructable from its KEV, ransomware, xDome-exploited, EPSS, CVSS, and exposure inputs. Because no controlled before-and-after time-and-motion study was conducted and no independent ground-truth exploitation labels were collected, three distinct outcomes remain future validation targets rather than demonstrated results: analyst productivity, comparative predictive prioritization accuracy against independent ground-truth exploitation outcomes, and remediation speed. The contribution reported here is therefore operational scale, coverage, and auditable prioritization traceability, not measured improvement in analyst decision-making or patient-safety outcomes. Full article

Background: Artificial intelligence (AI) is one of the most influential technological innovations in contemporary mental healthcare. Advances in machine learning, natural language processing, conversational agents, and large language models have accelerated the integration of AI into clinical practice, professional education, and healthcare. Despite its increasing adoption, important questions remain regarding its clinical effectiveness, implementation, safety, and ethical governance. Objective: This systematic review aimed to synthesize the current evidence on the application of artificial intelligence in mental health, with particular emphasis on clinical practice, educational transformation, and ethical governance. Methods: This systematic review was conducted in accordance with the PRISMA 2020 guidelines. PubMed/MEDLINE, Scopus, Web of Science, PsycINFO, and Google Scholar were systematically searched. The electronic database search was last conducted on 31 December 2025, and studies published between January 2019 and December 2025 were considered eligible. Eligible studies examined the application of artificial intelligence in mental health across clinical practice, educational contexts, and ethical governance. Study selection, data extraction, and methodological quality assessment were carried out independently by two reviewers using predefined eligibility criteria and standardized extraction forms. Owing to substantial methodological heterogeneity across the included studies, the findings were synthesized narratively. Results: A total of 88 studies met the eligibility criteria and were included in the final qualitative synthesis. The findings showed that AI demonstrated potential to improve diagnostic support, risk prediction, treatment planning, symptom monitoring, and access to psychological support. AI also supported educational innovation and workforce development while highlighting the importance of ethical governance for responsible implementation in mental healthcare. Conclusions: Future progress will depend on interdisciplinary collaboration to ensure that AI complements rather than replaces human expertise. Although AI demonstrates substantial potential, many systems remain experimental, with limited external validation. Prospective multicenter evaluation, transparent algorithm development, and robust ethical governance are therefore essential before widespread clinical implementation. Full article

AI-powered conversational agents are becoming part of the everyday Internet information ecosystem, reshaping how users seek, interpret, and act on health-related information outside clinical encounters. As large language model (LLM)-based chatbots are increasingly used as on-demand digital health information tools, understanding how users perceive their credibility, usefulness, and limitations is essential for the responsible design of future Internet-based health services. This mixed-method survey study examined how general adults evaluated healthcare-related question–answer pairs provided by physicians and generated by AI chatbots. A sample of U.S.-based adults recruited through Prolific (N = 62) rated each answer on clarity, usefulness, appropriateness of detail, trustworthiness, and perceived evidence, and provided open-ended explanations of their judgments. Primary mixed-effects analyses showed that both ChatGPT- and Claude-generated responses received higher overall participant ratings than physician-provided responses, although the estimated difference was substantially larger for Claude (ChatGPT–physician estimate = 0.250, 95% CI [0.135, 0.364]; Claude–physician estimate = 0.825, 95% CI [0.710, 0.939]). ChatGPT received higher ratings on four of the five dimensions but not on clarity, whereas Claude received higher ratings across all five dimensions. However, physician, ChatGPT, and Claude responses were always presented first, second, and third, respectively. Response source was therefore confounded with presentation position, and the observed differences cannot be attributed exclusively to source. The responses were also not matched for length or format. Qualitative findings showed that participants valued detailed, specific, and evidence-like explanations. Participants also expressed concerns about hallucination, privacy, over-reliance, and the need for clinician verification. These findings suggest that LLM-based chatbots may be perceived as useful supplemental information tools within future Internet health ecosystems, but their deployment should include safeguards that support transparency, verification, and appropriate reliance. Full article

Falls are a major cause of severe injury and mortality among older adults, requiring rapid state recognition and alerts in healthcare and caregiving environments. However, many existing fall-recognition approaches primarily focus on post-fall detection or assume access to cloud/GPU computing, leaving limited evidence for short-term pre-fall recognition on low-end CPU-based edge devices. This study proposes a lightweight CNN–TCN–Attention framework for recognizing fall and pre-fall states on resource-constrained edge devices. Using MediaPipe, three-dimensional coordinates and visibility scores of 33 human body landmarks are extracted from input videos. Raw RGB frames are not directly used by the classifier, thereby reducing the processing of visually identifiable information. A total of 2300 fall-related videos from the AI-Hub dataset were reorganized into three classes: normal, pre-fall, and fall. The pre-fall onset was defined as the point at which the downward vertical velocity of the nose landmark exceeded a dataset-specific threshold. The model uses a CNN to extract frame-level skeletal patterns, a temporal convolutional network (TCN) to learn temporal changes in posture, and an attention module to aggregate temporal features. It was deployed on a Raspberry Pi Zero 2 W using ONNX Runtime. The proposed model achieved 94.71% accuracy and a macro F1-score of 93.63%, with an average state-decision latency of 325.88 ms/decision. It improved accuracy by 2.83 percentage points over the lightweight CNN–LSTM–Attention baseline while maintaining comparable latency and achieved approximately 1.75× faster processing than the MobileNetV3–TCN–Attention model. Full article

Background: The rapid integration of Artificial Intelligence (AI), specifically Clinical Decision Support Systems (CDSS), into healthcare offers substantial efficiency but introduces critical ethical and legal challenges, particularly the perpetuation of systemic bias against older adults (“Digital Ageism”). While technological advances may improve care, they can violate fundamental bioethical principles when models are trained on unrepresentative data. Aim: This article argues that traditional clinical risk-management models are structurally insufficient to address opaque algorithmic bias and presents a conceptual, multidimensional governance framework designed to prevent the codification of human ageism into AI infrastructure. Methods: Drawing on systemic failures observed during the COVID-19 pandemic, the normative model integrates legal and governance standards aligned with the EU AI Act, Explainable AI (XAI) tools, and a three-phase implementation protocol. Results: To illustrate potential application without overburdening medical staff, the article introduces a theoretical Targeted Escalation Protocol and an Autonomous High-Load Safety Mode. The latter applies deterministic hardcoded constraints to contain age-dominant outputs during acute surges while preserving attending-clinician authority. The framework is explored through an Intensive Care Unit (ICU) thought experiment. Conclusions: The framework provides a structured roadmap for policymakers, ethicists, and healthcare administrators to move from reactive defensive medicine toward proactive ethical safety, safeguarding the dignity of the aging population while aiming to mitigate institutional legal exposure. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article

Artificial intelligence (AI) is increasingly being applied in healthcare, with growing relevance to clinical nutrition. This narrative review examines current and emerging uses of AI in nutrition care within the Nutrition Care Process framework, with attention to assessment, monitoring and evaluation, diagnosis, intervention, and clinical support tools. Current applications include AI-assisted dietary assessment using image recognition, wearable sensors, analysis of continuous glucose and other physiologic data for early risk detection, and support for malnutrition screening and diagnosis. AI is also being explored for identifying micronutrient deficiencies and complications of nutrient excess, as well as for screening and early intervention in eating disorders. In nutrition intervention, AI has potential to support personalized dietary planning, nutrition support in intensive care settings, behavioral interventions, and precision nutrition approaches such as digital twins. Additional applications include clinical decision support and documentation assistance. However, despite its usefulness, concerns about AI systems exist. Its performance depends on the quality of the data used to train it; it can introduce bias, and it can produce inaccurate or misleading outputs. In addition, overreliance on AI may also reduce clinician attentiveness and contribute to cognitive errors. For these reasons, AI should be regarded as a support tool rather than a replacement for human clinical care. Overall, AI offers substantial opportunities to improve the personalization, efficiency, and scalability of clinical nutrition practice, but its safe and effective implementation will require continued validation, careful oversight, and integration with clinical expertise. Full article

Large language models (LLMs) are expanding the capabilities of socially assistive robots (SARs) through natural dialogue, personalisation, multimodal reasoning, retained interaction context, and adaptive behaviour in healthcare. Integrating generative language models into robots, however, complicates evaluation because fluent output may exaggerate perceived competence and increase the risks of hallucination, overtrust, privacy exposure, relationship dependency, and unsafe reliance on advice or actions. This PRISMA-informed review synthesises healthcare robotics, human–robot interaction, LLM-enabled systems, ethics, implementation, and care delivery. Database searches returned 128 records, of which 110 were unique after deduplication. Supplementary retrieval and assessment yielded 85 substantive sources spanning background mapping, primary analysis, and governance. Studies focused mainly on feasibility, usability, acceptability, dialogue quality, and short-term engagement, whereas longitudinal safety, governance of retained interaction context, comparative effectiveness, workflow integration, and sustained healthcare value received limited attention. These gaps indicate that evaluation of LLM-enabled SARs must account for physical presence, social role, interaction memory, and potential actions rather than focus on conversational performance alone. The review therefore proposes HEART, a healthcare-specific evaluative architecture comprising Human-Centred Communication, Ethical and Trustworthy Deployment, Adaptive and Embodied Intelligence, Relationship Continuity, and Translational Healthcare Value. HEART uses boundary rules, operational indicators, qualitative labels, and non-additive deployment gates to separate evaluative domains, define assessable outcomes, summarise reported support, and prevent strengths in one area from masking critical safety or governance failures. Future research should validate HEART through longitudinal and comparative assessment of hallucination severity, language-to-action safety, long-term effects, equity, and post-deployment monitoring. Full article

The growth of single-person older adult households has heightened concerns about social isolation, chronic disease management, and diminished quality of life (QoL). A systematic review with meta-analysis was conducted to examine the efficacy of human-centered AI healthcare interventions on QoL among older adults living alone. Five electronic databases (PubMed, Web of Science, CINAHL, Embase, and Cochrane CENTRAL) were searched (January 2020–March 2026) following PRISMA 2020 guidelines. Only randomized controlled trials (RCTs) were eligible. Methodological quality was appraised with the Cochrane RoB 2 tool; pooled effect estimates were derived via random-effects modeling. Fourteen RCTs (N = 2840) were included. Intervention types comprised conversational agents, socially assistive robots, remote monitoring systems, integrated platforms, and AI-driven mHealth applications. Meta-analysis demonstrated significant improvements in overall QoL (SMD = 0.40, 95% CI: 0.27–0.52, I² = 59%), depression (SMD = −0.35, 95% CI: −0.43 to −0.26), and social connectedness (SMD = 0.38, 95% CI: 0.26–0.51). Subgroup analyses showed stronger effects for interventions with personalized feedback and human interaction. GRADE certainty was moderate for all three primary outcomes. GRADE certainty for secondary outcomes was moderate for physical health but low for self-management and cognitive function. Human-centered AI healthcare interventions significantly improve QoL and psychosocial outcomes among older adults living alone. Future research should prioritize long-term effectiveness, ethical implementation, digital inclusion, and culturally adaptive models. Full article