Integra Artificial Skin

Nanocrack Encoded Electronic Skin for Vectorial Strain Sensing.

Yan Xinran X, Yang Yuhang Y, Li Xuan X, Wang Chuchao C et al.

Human skin perceives both the magnitude and direction of mechanical stimuli, an essential capability for dexterous interaction yet fully unrealized in humanoid robotics. Here we introduce a metallic nanocrack-based electronic skin (Crack-eSkin), a strain vector perceptron that encodes the complete strain vector through the algorithmic geometry of nanocracks in the 50 nm thick gold film. When mechanically stretched, orthogonal cracks open differentially depending on orientation, producing distinct signals that simultaneously encode both strain magnitude and direction as a two-dimensional resistive fingerprint. This mechanism transforms mechanical stretch into an ultra-sensitive (gauge factor: 3609) material-level response that preserves both parameters at their source. Feeding encoded signal sequences into the developed geometry-aware multitask temporal network (RMT-Net) enables the joint prediction of strain magnitude and loading angle, achieving R2 values of 0.982 for strain and 0.985 for angle. Installed onto an artificial face model, the Crack-eSkin can quantitatively resolve sequential strain vectors of 2.60% at 87.4°, 1.84% at 7.7°, and 3.20% at 45.2° that human subjects could only describe qualitatively, paving the way for next-generation robotic systems with human-like or superior mechanosensation.

Framework for developing explainable artificial intelligence models for neglected tropical disease diagnosis in low-resource settings.

Innocent David Chinaecherem DC, Innocent Rejoicing Chijindum RC, Innocent Increase Praise IP

Neglected tropical diseases (NTDs) continue to affect more than one billion people globally, disproportionately impacting populations living in low-resource settings characterized by limited diagnostic infrastructure, shortages of trained healthcare personnel, and restricted access to specialist services. Recent advances in artificial intelligence (AI), particularly deep learning and computer vision, have demonstrated significant potential for improving disease detection through the analysis of clinical images and microscopy data. However, despite encouraging diagnostic performance, many AI systems remain difficult to interpret, creating barriers to clinical trust, adoption, regulatory acceptance, and sustainable implementation in endemic regions. This narrative review examines the current landscape of AI applications in NTD diagnosis and critically evaluates the role of explainable artificial intelligence (XAI) in addressing challenges associated with transparency and trustworthiness. Evidence from studies involving malaria, schistosomiasis, soil-transmitted helminth infections, leishmaniasis, and skin-related NTDs demonstrates the growing capacity of AI to support diagnostic decision-making in resource-constrained environments. Nevertheless, persistent challenges related to limited datasets, poor data quality, algorithmic bias, model drift, infrastructure constraints, and ethical governance continue to impede translation into routine healthcare practice. Existing explainability approaches, including Gradient-weighted Class Activation Mapping (Grad-CAM), heatmaps, Shapley Additive Explanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), and attention mechanisms, were reviewed to assess their relevance for NTD diagnostic systems. Drawing upon current evidence in explainable AI, digital health implementation, and global health systems research, a seven-stage framework is proposed comprising: (1) problem definition, (2) data acquisition, (3) model development, (4) explainability layer integration, (5) clinical validation, (6) deployment in low-resource settings, and (7) continuous learning and monitoring. The framework embeds explainability throughout the AI development lifecycle to enhance transparency, accountability, clinical relevance, and equity. Artificial intelligence has considerable potential to improve NTD diagnosis in low-resource settings, but successful adoption depends on trust, transparency, and usability. The proposed framework provides a structured pathway for developing explainable AI systems that are technically robust, clinically meaningful, ethically responsible, and implementable within resource-constrained health systems, thereby supporting future NTD control and elimination efforts.

The Importance of Artificial Intelligence in Nursing: A Fundamentals of Care Perspective.

Oliveira João J, Nogueira Paulo P, Baixinho Cristina C, Costa Andreia A

To analyze the integration of Artificial Intelligence in nursing through the lens of the Fundamentals of Care framework. A discursive paper. This discursive paper synthesizes current literature and theoretical perspectives to examine the Relationship, Integration and Context dimensions of the Fundamentals of Care framework in the era of Artificial Intelligence. Artificial Intelligence offers substantial benefits in optimizing workflow (Context) and clinical precision (Integration) through predictive analytics and automated documentation. However, these technologies cannot replace the psychosocial and relational core of nursing. When used to reduce administrative burden, Artificial Intelligence can theoretically release time for nurses to deepen the therapeutic bond. Artificial Intelligence should be viewed not as a competitor but as an enabler for providing person-centred fundamental care. To ensure ethical integration, nurse leaders must cultivate Artificial Intelligence literacy and maintain the nurse-patient relationship as the central mediator of care. Nurses must actively participate in the Artificial Intelligence lifecycle to prevent 'infocratic care' and ensure algorithms support rather than supplant human connection. None. None. However, the discussion of this topic is important to ensure quality and safe care for citizens.

Impact of artificial intelligence on heterogeneous catalysis: general discussion.

Alexandrova Anastassia N AN, Árnadóttir Líney L, Beaumont Simon K SK, Dannar Audrey A et al.