Rapid innovation leading to better patient outcomes have been driven by recent breakthroughs in the Internet of Things (IoT), which have drastically changed the healthcare sector. In order to highlight the importance of IoT in healthcare applications, this article presents a user-friendly and integrated approach for bibliometric analysis. Traditional bibliometric methods often rely solely on Web of Science (WoS) or Scopus, limiting the scope of analysis. To address this issue, the proposed approach uses the R program Bibliometrix to combine data from seven databases, namely Scopus, WoS, IEEE, ACM Digital Library, PubMed, Science Direct, and Google Scholar (GS). After having developed an inclusion/exclusion criterion, 2,990 journal papers published between 2011 and 2022 were subjected to a thorough literature review and bibliometric analysis. This study demonstrates that the healthcare industry is highly interested in the IoT, as well as the rapid growth of research into IoT healthcare applications, blockchain, Artificial Intelligence (AI), 5G telecoms, and data analytics. Authentication methods, fog computing, cloud-IoT integration, cognitive smart healthcare, and other essential topics are further examined by employing co-citation network analysis. In addition to illuminating potential avenues for further investigation, these results provide academics with a comprehensive picture of where IoT research in healthcare is standing at the moment. The output of the conducted analysis shows that there has been a dramatic uptick in publishing since 2017, with most of the articles appearing in prestigious journals related to computer science. By integrating data from multiple databases, the proposed methodology represents a significant advancement in bibliometric analysis, enabling a more comprehensive exploration of IoT's impact on healthcare, and facilitating a deeper understanding of the emerging trends and critical themes in this rapidly evolving field.

Background: Autism Spectrum Disorder (ASD) presents challenges in early diagnosis and personalized treatment due to the subjectivity of traditional screening methods and the lack of tailored therapeutic approaches. Artificial Intelligence (AI) and Big Data Analytics offer innovative solutions for enhancing diagnostic accuracy and optimizing treatment pathways. Objective: This study explores the application of AI in ASD diagnosis and predictive modeling for personalized stem cell therapy, aiming to improve early detection and treatment effectiveness. Methods: A dataset of 704 individuals was analyzed using machine learning models, including LightGBM, Random Forest, Neural Networks, XGBoost, and Stacking Ensemble. Performance was assessed using ROC-AUC, Accuracy, Precision, Recall, and F1-score, while AI models were further applied to predict therapy response patterns for stem cell treatment optimization.Results: The Stacking Ensemble model (ROC-AUC = 0.9989, F1 = 0.9125) demonstrated superior performance in ASD classification. Neural Networks exhibited the highest recall (95.76%), making them ideal for early screening. AI-driven insights facilitated the identification of key ASD biomarkers, enabling personalized treatment strategies.Conclusion: AI significantly enhances ASD detection and treatment planning, providing a data-driven, personalized approach. Future research should focus on real-world validation, integrating genetic biomarkers, and ensuring ethical AI deployment in clinical settings. These advancements pave the way for precision medicine in neurodevelopmental disorders.