Introduction: The expanding complexity of dental diseases has exposed the limitations of conventional heuristic-based therapeutic planning. Artificial Intelligence (AI) has evolved beyond diagnostic assistance to become a powerful tool in therapeutic decision-making, enabling data-driven, predictive, and personalized dental care. Material and Methods: This narrative review critically evaluates contemporary applications of artificial intelligence in therapeutic planning, prognostic assessment, and surgical execution across major dental specialties, including periodontology, endodontics, prosthodontics, orthodontics, and implantology. Evidence from machine learning, deep learning, computer vision, and robotics-based systems was synthesized to assess clinical relevance beyond diagnostic accuracy. Results: Artificial intelligence-based clinical decision support systems demonstrated improved precision in treatment planning, outcome prediction, and procedural execution. Applications such as generative prosthetic design, Artificial intelligence -guided endodontic access, implant navigation, orthodontic treatment simulation, and robotic-assisted surgery showed potential to reduce operator variability and enhance therapeutic outcomes. However, challenges related to data heterogeneity, algorithmic bias, explainability, and medico-legal accountability persist. Conclusion: Artificial Intelligence is redefining therapeutic decision-making in dentistry by augmenting clinical judgment rather than replacing it. When integrated within a human-in-the-loop framework, artificial intelligence serves as a high-level therapeutic assistant capable of improving accuracy, efficiency, and personalization of dental care. Future research must prioritize longitudinal clinical validation and ethical governance to enable safe and effective clinical translation.

Artificial intelligence applications for enhancing patient self-care education following sternotomy: Development and initial evaluation.

This research aims to identify the reality of the use of artificial intelligence (AI) applications by teachers of students with learning disabilities (LD) in education. The descriptive approach was used to achieve the objectives of the research, in which 40 female teachers who teach students with LD were participated. A questionnaire was applied and it included 30 items classified into three themes (cognitive awareness, use, and obstacles). The results shown that there is weak cognitive awareness for AI among the study sample, the results also indicated a weak level of use for AI applications among the study sample, and that there were multiple obstacles in using these applications from the point of view of teachers of students with LD; most importantly, the challenge is in the financial constraints and the prices of AI applications in general. The study referred to a set of recommendations and implications based on the findings.

Porous Zn scaffolds for bone regeneration: A synergy of experiment and artificial intelligence

The application of artificial intelligence in computational pathology depends on both robust algorithms and high-quality, clinically reliable data. Progress in this field has been limited by the scarcity of large, diverse, and well-validated whole slide image (WSI) datasets. To address this gap, HISTAI introduced an open-source resource comprising over 112,000 WSIs across multiple organ systems with associated clinical metadata. Here, we present a pathologist-led evaluation of label accuracy, metadata completeness, and dataset composition across 328 selected cases from this resource. Although HISTAI reports 47,279 cases, we identified only 44,564 unique cases after accounting for missing entries and duplicate records. Basic demographic information, including age and sex, was available for only 55% of cases. Dataset composition was uneven, with dermatopathology accounting for 47.1% of cases and gastrointestinal pathology for 24.0%; however, primary specialty was explicitly reported for only 39.6% of cases, obscuring this imbalance within the provided metadata. Notably, clinical ground truth is recorded in the Conclusion column. Concordance between the dataset's Conclusion and Diagnosis fields was observed in only 20.7% of cases, while 27.1% contained conflicting diagnoses. In a focused review of 198 cases, 30.3% were found to contain unclear or ambiguous diagnostic conclusions, including eight cases in which the diagnosis was incorrect. Assessment of molecular annotation revealed that only 18.9% of analyzed lung and colorectal cancer cases included molecular information. Furthermore, among adult-type diffuse gliomas, none of the 55 cases met current World Health Organisation Classification of Tumors of the Central Nervous System 5th Edition (WHO CNS5) diagnostic criteria, with IDH mutation status reported in only 15 cases. Together, these findings highlight substantial ambiguities in ground-truth labeling, incomplete molecular annotation, and limited documentation of dataset provenance and ethical oversight. While HISTAI represents a valuable open-source resource, its effective and responsible use requires careful clinical validation and close collaboration between computational researchers and pathologists.

Nurses' attitudes toward artificial intelligence applications and their clinical decision-making competence: A cross-sectional study.

Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.

Overcoming gastrointestinal mucosal barriers: Mechanistic innovations and technical advances in mucosal targeting strategies for animal oral vaccines.

Testing the Implementation and Acceptance of Generative Artificial Intelligence to Augment Vascular Surgery Journal Club.

Artificial intelligence in pharmaceutical manufacturing: Applications, case studies, and GxP implementation considerations.

The rapidly changing and increasingly complex processes enabled by artificial intelligence (AI) applications challenge the conventional concepts of innovation. In contrast to a general perception that AI adoption can augment innovation output, managers still lack empirical guidance on how to structure innovation processes with human-AI interaction across time and space. Drawing on observations from case studies in the aerospace, heavy engineering, information technology, and pharmaceutical sectors, this paper presents the development of a conceptual model for digital innovation to represent (<i>i</i>) <i>Learning Processes (LPs) focusing on knowledge creation and knowledge reuse and</i> (<i>ii</i>) <i>Product Development Processes (PDPs) leading to radical and incremental changes</i>. <i>The conceptual model is inductively developed based on</i> a theory building approach using multiple case studies. A set of transformative characteristics centralized on Originality, Reliability, Transferability, and Adaptability (ORTA) are identified to guide decision-making along multi-stage and cross-layer innovation processes involving cyclical handoffs between humans and machine agents. These ORTA characteristics form a base for strategic decision-making along the Human and AI decision spectrum suited to prepare companies for survival and prosperity in their journeys of digital transformation.

Explaining explainability: A comprehensive survey on explainable artificial intelligence and relevant industry applications

Artificial intelligence and machine learning applications in ambulatory surgery - A systematic review.

Artificial intelligence for metabolic dysfunction-associated steatotic liver disease diagnosis: A systematic review.

Artificial intelligence innovations in substance use prevention on social media: A scoping review.

Aquatic foods are essential sources of protein and micronutrients and play a critical role in global nutrition, trade, and livelihoods. However, their safety and sustainability are frequently compromised by microbial contamination and biofilm formation during farming, processing, storage, and retail. Biofilms persist on moist surfaces, resist conventional cleaning practices, and contribute to spoilage, cross-contamination, and economic loss. This article reviews emerging applications of artificial intelligence and Industry 4.0 technologies for biofilm prevention and control in aquaculture and seafood systems. Particular emphasis is placed on the use of continuous water quality sensing, imaging platforms for early detection and cleaning verification, genomic and omics tools for microbial trait-level insight, and digital twin frameworks for virtual simulation of sanitation strategies. Recent advances demonstrate that sensor telemetry can predict biofilm-favorable conditions, imaging can verify removal in real time, and genomic data can identify persistence traits and tolerance mechanisms. When integrated, these approaches enable facility-specific digital twins that anticipate surface-specific risks and recommend optimized interventions before implementation. The convergence of AI, sensor networks, imaging, and omics offers a shift from reactive to proactive biofilm management in aquatic food systems. Positioned within the transition to Industry 5.0, these innovations support earlier detection, targeted interventions, and measurable improvements in food safety, quality, sustainability, and resilience, while aligning production systems with human-centric goals.

Advancing Whole-Person Health through Informatics: A Narrative Review of Knowledge Resources for Complementary and Integrative Health.

Can artificial intelligence transform antiviral drug discovery?

Mapping knowledge landscapes and emerging trends in AI for coronary artery disease imaging biomarkers: A bibliometric and visualization analysis.

J³SPM AI: An integrated open-source platform for AI-assisted image analysis and image-guided workflows in scanning probe microscopy.