Diabetes is a chronic condition and global health challenge, while prediabetes represents a critical stage for preventing disease progression. As prediabetes affects millions worldwide, accurate and early risk prediction tools are needed beyond blood sugar levels or HbA1c. Identifying at-risk individuals can support timely lifestyle and clinical interventions using individualized, noninvasive clinical features. This study proposes a novel multiclass Siamese Neural Network (SNN) approach for prediabetes prediction. By learning complex feature relationships from multidimensional healthcare data, the SNN model achieved 78.2\% accuracy, 88.9\% specificity, and an AUC of 0.91 using the DiabHealth dataset. These results demonstrate its ability to improve diagnostic precision despite imbalanced data and limited labeled samples. The findings highlight the potential of SNN-based predictive models for early detection, including previously unrecognized prediabetes risk features. Variable importance identified autonomic nervous system testing, oxidative stress, and diet as leading predictors ahead of fasting blood glucose. Integrating AI-driven diagnostics can improve decision-making, reduce long-term complications, and strengthen diabetes prevention efforts.

This paper explores the use of Artificial Intelligence (AI)-driven voice biomarkers for early detection and continuous monitoring of schizophrenia. Traditional diagnostic approaches are often subjective, timeconsuming, and limited in scalability. In contrast, AI offers automated, non-invasive alternatives that can detect vocal indicators linked to core symptoms. A systematic literature review was conducted using the PRISMA framework. Thereafter, the selected studies were evaluated through a systems approach guided by eight Human Factors and Ergonomics (HF/E) principles. The review revealed promising but heterogeneous model performance across classification, prediction, and correlation-based tasks using features extracted from speech across multiple languages and contexts. However, limitations related to generalizability, explainability, workload, and ethical issues were noted. While AI-based voice biomarkers show high potential for enhancing psychiatric diagnosis and monitoring, most solutions remain at the proof-of-concept stage. A systems approach is essential to bridge the gap between technological innovation and real-world clinical application, ensuring usability, safety, and scalability.

Background: Patient satisfaction is a crucial indicator of healthcare quality, which encompasses multiple dimensions of patient care. This research analyzes survey data from 720,000 patients across 42 Integrated Care Systems in the UK on patients' experiences with their GP practices by applying six machine learning regression algorithms to assess key drivers of patient satisfaction. Methods: We used Random Forest, XGBoost, CatBoost, Lasso, Ridge, and Elastic Net regression models. Cross-validation and hyperparameter tuning were conducted to optimize model performance and prevent overfitting. Results: Receptionist help emerged as the most significant factor influencing patient satisfaction, followed by listening skills of healthcare professionals and efficient appointment scheduling. Ridge and Elastic Net regression models demonstrated better performance, with R-squared values of 0.964, 0.956, respectively. Conclusions: Focusing on receptionist training, improving communication skills of healthcare professionals, and optimizing appointment scheduling systems can significantly increase patient satisfaction. Machine learning provides valuable information to improve healthcare quality.

Medical question answering systems must provide responses that are not only understandable, but also grounded in reliable sources. This paper presents the MedQuAD Healthcare Assistant, a retrieval-augmented chatbot for patient-facing educational medical question answering. The system uses the MedQuAD dataset as a medical knowledge source, retrieves relevant question-answer records using dense vector search, and generates cited responses with Qwen2.5-7B-Instruct. The evaluation compares a No-RAG baseline with MiniLM and BGE-small retrieval configurations across multiple top-k settings, using retrieval metrics, RAGAS answer-quality metrics, latency, and human evaluation. BGE-small with k = 10 achieved the strongest overall balance, reaching 1.000 Exact QA Hit@10 and 0.911 Exact QA MRR, while also providing the highest context recall among RAG configurations. These results show that dense-retrieval RAG can improve reliable information access, while still requiring expert validation before clinical use.

University counseling centers increasingly face high demand, limited service hours, lengthy waitlists, and constrained staffing capacity. This study presents a safety-aware psychoeducation chatbot designed to complement, rather than replace, professional counseling services by providing evidence-based mental health information and guided self-reflection outside regular service hours. The deployed system runs the base Qwen3-8B model with retrieval-augmented generation over 17 curated public-health documents from sources including NIMH and WHO, and a four-tier safety-routing layer for general questions, sensitive topics, out-of-scope requests, and crisis disclosures. High-risk messages are routed to UAE-specific emergency and crisis resources before language-model generation is invoked. QLoRA fine-tuning on 750 filtered CounselChat examples was evaluated and found to degrade response quality relative to the prompted base model, an ablation finding that informed the deployed configuration. Safety routing is evaluated on a 181-case adversarial benchmark with per-tier metrics, developed with independent labeling by university counseling staff (inter-rater agreement κ = 0.80). The results quantify the failure modes of lexical crisis detection, particularly for indirect ideation, and highlight the importance of deterministic safety controls in mental health chatbot design.