Researchers at Khalifa University and ASPIREPMRIAD applied nested cross-validation on de-identified SEHA EHR data, training nine ML models with both automated and expert-driven feature selection. A Naive Bayes classifier achieved 0.96 AUC, highlighting dental and respiratory codes for cost-effective early mucopolysaccharidosis detection.

Key points

• Domain-expert feature selection identifies dental and respiratory codes (e.g., acute gingivitis, bronchitis) critical for MPS prediction.
• Naive Bayes classifier achieves 0.96 AUC, 0.93 accuracy, and 0.91 F1-score using EHR-derived features.
• Nested cross-validation with SMOTE balancing validates nine ML models across five feature selection strategies on 1186 EHR covariates.

Why it matters: This non-invasive, AI-driven screening transforms rare disease diagnostics by flagging mucopolysaccharidosis risk from routine EHR data, enabling earlier intervention and better outcomes.

At Shanghai University, a research team identifies 29 Chinese AI clusters using location quotients and social network analysis of patent data, then employs dynamic panel System-GMM to assess how industry policies influence technological innovation. They find policies significantly stimulate innovation but that clusters with high closeness centrality experience diminished policy impact. This suggests policymakers should balance cluster network structures when devising supportive measures for AI development.

Key points

• Identified 29 AI clusters in China using location quotients and social network analysis on patent data.
• Applied dynamic panel System-GMM to quantify the positive effect of industry policies on invention patent output (coefficient 0.037, p<0.05).
• Discovered a significant negative interaction: high cluster closeness centrality weakens policy-driven innovation gains.

Why it matters: This study reveals how AI policy efficacy depends on cluster network structure, guiding targeted strategies that optimize innovation outcomes in emerging technologies.

A team from University College of Dentistry at the University of Lahore conducted a cross-sectional survey among 451 medical and dental clinicians in Pakistan. Employing the General Attitude towards Artificial Intelligence Scale and a self-formulated readiness questionnaire, they quantified practitioners’ positive and negative perceptions, familiarity, and confidence in operating AI systems to facilitate informed AI adoption in resource-constrained settings.

Key points

• Surveyed 451 public and private medical/dental practitioners in Pakistan using GAAIS and a custom readiness tool.
• Positive attitude mean score was 3.6±0.54; negative attitude mean score was 2.8±0.71 on a 5-point Likert scale.
• Dental practitioners showed significantly higher confidence in AI operation (38.4% vs. 29.8%, p=0.047) and willingness for AI in diagnosis (68.5% vs. 57%, p=0.004).

Why it matters: This study underscores critical practitioner readiness and ethical considerations necessary to guide successful AI integration in resource-limited healthcare systems.

Market Reports Insights employs comprehensive market analysis to project that the global Toy Robots Market will achieve a 13.5% CAGR from 2025 to 2032, attaining a valuation of USD 4.8 billion. This forecast highlights AI and ML integration in enhanced interactive play, personalized learning applications, and the growing emphasis on STEM education as key growth drivers across developed and emerging regions.

Key points

• Market Reports Insights projects a 13.5% CAGR for the Toy Robots Market from 2025-2032, reaching USD 4.8 billion.
• AI and ML integration enable voice recognition, personalized learning modules, and autonomous navigation for enhanced user engagement.
• Growing STEM education emphasis and expanding e-commerce channels drive market growth across North America, Europe, and Asia-Pacific.

Why it matters: As AI-driven toy robots enhance interactive STEM education from early ages, they accelerate digital literacy and innovation pipelines.