Artificial intelligence in clinical trials: A comprehensive review of opportunities, challenges, and future directions

Background Clinical trials face unprecedented challenges including recruitment delays affecting 80% of studies, escalating costs exceeding $200 billion annually in pharmaceutical R&D, success rates below 12%, and data quality issues affecting 50% of datasets. Artificial intelligence (AI) offers transformative solutions to address these systemic inefficiencies across the clinical trial lifecycle. Objective To evaluate the current state, future potential, and implementation challenges of AI technologies in clinical trials, providing evidence-based guidance for responsible AI integration while maintaining patient safety and scientific integrity. Method Comprehensive narrative review following established guidelines for literature synthesis. Systematic search of PubMed, Embase, IEEE Xplore, and Google Scholar databases from January 2015 to December 2024. Data extraction and narrative synthesis organized thematically according to clinical trial lifecycle stages. Results Analysis of relevant studies demonstrated substantial AI benefits: patient recruitment tools improved enrollment rates by 65%, predictive analytics models achieved 85% accuracy in forecasting trial outcomes, and AI integration accelerated trial timelines by 30–50% while reducing costs by up to 40%. Digital biomarkers enabled continuous monitoring with 90% sensitivity for adverse event detection. However, significant implementation barriers emerged, including data interoperability challenges, regulatory uncertainty, algorithmic bias concerns, and limited stakeholder trust. Conclusion AI represents a transformative force in clinical research with proven capabilities to enhance efficiency, reduce costs, and improve patient outcomes. Realizing this potential requires addressing technical infrastructure limitations, developing explainable AI systems, establishing comprehensive regulatory frameworks, and fostering collaborative efforts between technology developers, clinical researchers, and regulatory agencies to ensure responsible implementation

Machine learning prediction of kangaroo mother care in Sierra Leone: a comparative study of feature selection techniques and classification algorithms

Background Kangaroo Mother Care (KMC) is a critical intervention for improving neonatal outcomes, particularly for low-birth-weight infants. Identifying predictors of KMC practice remains essential for targeted health interventions and policy development. Objective This study utilizes data from the 2019 Sierra Leone demographic and health survey to identify predictors of KMC using different feature selection techniques and classification algorithms. Methods We analyzed 7,377 maternal and child health records from the 2019 Sierra Leone demographic and health survey, applying three feature selection techniques and seven classification algorithms. Data preprocessing included class balancing and cross-validation. Three feature selection techniques employed were: Adaptive Ant Colony Optimization (ACO), Recursive Feature Elimination (RFE), and Backward Feature Selection. Seven machine learning algorithms implemented were: Logistic Regression, Support Vector Machine variants, K-Nearest Neighbours, Random Forest, XGBoost, Stacking Ensemble, and Voting Ensemble. Data preprocessing included SMOTE for class imbalance, 5-fold and 10-fold cross-validation, and hyperparameter optimization using GridSearchCV. Results Random Forest and XGBoost consistently achieved the highest performance across all feature selection methods. Using consensus features from multiple selection techniques, Random Forest achieved an accuracy of 0.72, F1-score of 0.78, and ROC-AUC of 0.7689, whilst XGBoost demonstrated similar performance (accuracy: 0.72, F1-score: 0.78, ROC-AUC: 0.7685). Backward Feature Selection and ACO outperformed RFE in identifying discriminative features. Ensemble methods showed robust generalization capabilities. Conclusion Machine learning models, particularly ensemble methods combined with comprehensive feature selection techniques, demonstrate strong predictive capability for KMC practice, offering valuable insights for maternal and child health interventions in Sierra Leone

Artificial Intelligence in Computational and Materials Chemistry: Prospects and Limitations

Computational chemistry, at the intersection of theoretical chemistry and computer science, employs various models to analyze molecular structures and properties, enabling the understanding and prediction of intricate chemical processes. The integration of artificial intelligence (AI) has revolutionized several fields, particularly in materials chemistry, with applications spanning drug discovery, materials design, and quantum mechanics. However, challenges related to quantum system complexity, model interpretability, and data quality remain a few of the Achilles’ heel of AI applications. This paper provides an overview of AI’s evolution in computational and materials chemistry, focusing on several applications. AI’s transformative potential in materials chemistry is emphasized, facilitating precise material property predictions, crucial for industries reliant on materials innovation. In materials chemistry, AI has led to substantial advancements, enabling the rapid discovery of materials with tailored properties. Yet, the challenges of modeling complex quantum systems, achieving model interpretability, and accessing high-quality data remain. The integration of AI into computational and materials chemistry promises to reshape the field, revolutionizing chemical research, materials design, and technological innovation. In order to harness AI’s full potential, transparent AI models, advanced quantum simulations, optimized data utilization, scalable computing, interdisciplinary collaboration, and ethical AI practices are essential

From function to feeling: exploring the effects of human-like traits in service robots on perceived authenticity and value

The potential for integrating service robots into hospitality operations calls for exploring customer perceptions of robotic service delivery from the perspective of its acceptance. Research is necessitated to establish the determinants of positive perception of service robots among hospitality consumers. This study examined the effect of three human-centric robot traits, including personalization, efficiency, and anthropomorphism on the perceived authenticity and value of a restaurant service. The survey results (n = 358) revealed significant effects of personalization, efficiency, and anthropomorphism on perceived authenticity and four dimensions of perceived value, including emotional, social, functional, and epistemic values. Perceived authenticity partially mediated the relationship between personalization, efficiency, anthropomorphism and four value dimensions. The results demonstrated the importance of designing service robots with human-centric traits to provide more authentic and valuable service to hospitality customers

How efficient is your robot server? Examining the antecedents of perceived efficiency of service robots in restaurants

Purpose – This study aims to examine the factors shaping the perceived efficiency of service robots in restaurant environments, as well as the mediating roles of functional, emotional, social and epistemic values. Design/methodology/approach – A survey (n = 155) was conducted with restaurant customers who had prior experience with robotic service. Data were analysed using regression and mediation analysis (PROCESS model) in SPSS 29. Findings – Personalisation, authenticity and the service environment significantly increased perceived efficiency. Among the perceived value dimensions, only functional and epistemic values were found to mediate these relationships significantly. Originality/value – This study highlights the importance of practical utility and novelty in shaping customer evaluations of service robots. Theoretically, it integrates the technology acceptance model, service-dominant logic and expectancy-confirmation theory to offer a more detailed understanding of customer–robot interaction in the context of robotic restaurant services. Practically, it provides guidance for designing robotic services that enhance both functional and epistemic value

Supporting and encouraging authors to publish Open Access Monographs with no REF2029 mandate

This online webinar explored how libraries are making the case for Open Access Monographs in the absence of a REF mandate. In August of last year, REF confirmed that the 2029 Research Excellence Framework will not have a mandate for Open Monographs, instead open access requirement for submission of longform outputs will be in place for the next assessment exercise, with implementation from 1 January 2029. Whilst this has delayed a sector wide transition to OA Books, there is still a need to support and encourage authors to publish open access monographs to encourage culture of Open Research and ensure the sector is ready for the 1st of January 2029