The integration of artificial intelligence (AI) in rehabilitation robotics (RR) can be regarded as a significant advancement in patient recovery processes. RR refers to robotic technologies that are designed to support therapy and/or healing processes by applying tailored, adaptive, and efficient machine learning algorithms. This paper is a methodological narrative review discussing the latest trends in the application of AI in RR and its effects on the recovery of patients with stroke, spinal cord injury, and musculoskeletal disorders. Technological advancements in real-time monitoring, feedback, and intelligent data analysis have enhanced the effectiveness of these systems, providing patients with personalized rehabilitation programs that are progressive in nature, based on the patients’ improvement. Research evidence demonstrates that the use of AI-driven robotic systems in clinical practice significantly improves motor skills, the rate of recovery, and overall patients’ well-being, in contrast to conventional rehabilitation procedures. Moreover, the review highlights the drawbacks and problems that present-day technologies have; these problems concern the costs, the absence of unified protocols, and legal requirements. Furthermore, the combination of AI with RR will likely progress even more, with ongoing research studying ways in which the system’s intelligence could be upgraded, patients’ involvement in the process could be increased, and the applicability of these technologies to various forms of needs could be increased as well. The present review thus highlights the importance and the role of AI-RR in enhancing the patient’s recovery and opens the way for therapeutic practices. This narrative review was informed by targeted searches in PubMed, Scopus, and IEEE Xplore covering 2015-2025, with an emphasis on English-language, peer‑reviewed studies in stroke, spinal cord injury, and musculoskeletal rehabilitation; no formal meta-analytic synthesis was undertaken, consistent with a narrative approach.

Predictive modeling for metastasis in oncology has gained significant traction due to its potential to improve prognosis, guide treatment strategies and enhance patient outcomes. Current methods leverage advancements in machine learning, genomics and imaging technologies to predict the likelihood of cancer spread. Techniques such as logistic regression, decision trees, support vector machines and neural networks have been employed to analyze clinical, pathological, and molecular data. Genomic profiling, liquid biopsies, and radiomics are increasingly integrated into these models to identify metastatic patterns and risk factors. Despite these advances, challenges persist, including data heterogeneity, model interpretability, and the need for larger, high-quality datasets for validation. Furthermore, the integration of artificial intelligence with precision medicine offers promising avenues for more personalized metastasis predictions. Future directions focus on enhancing model accuracy through deep learning, improving the interpretability of black-box models, and incorporating multi-omics data to capture the complexity of metastatic mechanisms. With the advent of advanced computational tools and growing datasets, predictive modeling in oncology is poised to revolutionize metastasis management, offering clinicians’ valuable insights for early detection and tailored treatment strategies.

Abdominal aortic aneurysms (AAAs) remain a major concern to the global society because of the associated risk of rupture and death. Currently, the management of AAAs entails clinical and imaging risk factors, which are not precise and accurate in terms of patient-specific risk assessment. Over the last decade, the utilization of artificial intelligence (AI) and machine learning (ML) algorithms has transformed the process of decision-making in the field of medicine by allowing for the creation of personalized models based on the patient’s characteristics. This review aims to discuss the current state and future directions of AI in the form of predictive modeling for aortic aneurysms, stressing the versatility and progression of the ML approaches in risk assessment, screening, and prognosis. We expand on the various strategies used in AI-based solutions and the differences between general and specific approaches such as supervised and unsupervised learning, deep learning, and others. Furthermore, we bring forward the problem of incorporating clinical, imaging, and genomic data into AI/ML to improve its predictiveness and applicability to clinical practice. In addition, we discuss the difficulties and prospects of turning the developed AI-based forecasting models into clinical practice, as well as the problems associated with data quality, model explainability, and legal and ethical concerns. This review aims to reveal the opportunities of AI and ML in enhancing the risk assessment and management of AAAs to shift the paradigm of cardiovascular care toward precision medicine.

Background: Even with advanced management involving pharmacologic and ventilatory strategies, respiratory dysfunction increases morbidity and reduces the quality of life. This narrative review examines how craniofacial and cervical manipulative interventions—including nasomaxillary skeletal expansion, breathing re-education, and structural techniques—may holistically optimize airway function by enhancing neurological and lymphatic dynamics, modulating vagal tone, reducing pharyngeal collapsibility, and supporting immune regulation across diverse clinical settings. Objectives: To explore manual techniques that influence respiratory and autonomic function and to evaluate their reported clinical efficacy and supporting evidence, particularly in the context of airway disorders such as asthma and pneumonia. Methods: A narrative review of the literature from PubMed and Google Scholar was conducted using search terms related to airway function and osteopathic manipulative techniques (OMTs). The inclusion criteria spanned 2010–2025 English-language peer-reviewed full-text articles on airway function, OMT, and emergency airway maneuvers. Clinical trials, observational studies, and reviews were included; non-peer-reviewed content and animal studies (unless mechanistically relevant) were excluded. Chapman’s reflexes related to respiratory function were incorporated to highlight somatic–visceral correlations. Key Findings: The techniques reviewed included frontal lift, vomer manipulation, maxillary and zygomatic balancing, and cervical adjustments. Thoracic OMT methods, such as diaphragm doming and lymphatic pump techniques, were also addressed. Emergency techniques, such as the BURP and Larson maneuvers, prone positioning, and high-frequency chest wall oscillation, were presented as comparative strategies to OMTs for acute airway management. Conclusions: Craniofacial and cervical manipulations can be a promising adjunct for enhancing airway function. However, the current literature displays heterogeneity and lack of large-scale randomized trials, which emphasize the necessity for standardized research and the establishment of clinical guidelines with the collected evidence.