In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician’s workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians’ roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

Background/Objectives: Huntington’s disease (HD) is an autosomal dominant genetic disorder causing progressive neurodegeneration which, aside from symptomatic therapies for controlling psychological and motor problems, currently has no effective treatment. People who receive this diagnosis often feel disoriented and lost without guidance. Furthermore, HD patients are estimated to have a two to seven times greater risk of suicide death compared to the general population. The current review investigates the complex relationship between HD and suicide, seeking to identify key risk factors influencing suicidal ideation and behaviour in affected individuals. Methods: We conducted a systematic review following the PRISMA guidelines. Studies were searched for on the PubMed, Cochrane, and Web of Science databases, and 17 articles met the inclusion criteria. Results: The findings reveal that emotional strain, neuropsychiatric symptoms, and the absence of a cure contribute to heightened suicidal tendencies in HD patients. Critical periods for suicide risk coincide with early symptomatic stages of disease or the successive phase, with the loss of independence impacting on daily functioning. Risk factors associated with HD include a depressive mood, cognitive impairments, and a history of suicide attempts. Conclusions: From a prevention perspective, a comprehensive multidisciplinary and multidimensional approach could enhance the overall well-being of people with HD. In particular, screening for suicidal thoughts in people with HD could mitigate suicide risk.

Background: Spinal cord injury (SCI) is a severe and progressive neurological condition caused by trauma to the nervous system, resulting in lifelong disability and severe comorbidities. This condition imposes serious limitations on everyday life, interfering with patients’ social lives and compromising their quality of life, psychological well-being, and daily living activities. Rehabilitation is essential to helping SCI patients gain more independence in their daily routines. Home automation (HA) systems provide personalized support to users, allowing them to manage various aspects of their living environment, promoting independence and well-being. This study aims to demonstrate the efficacy of an HA system in enhancing personal and social autonomies in SCI patients, resulting in improved cognitive function and reduced anxiety–depressive symptoms compared to traditional training. Methods: We enrolled 50 SCI patients undergoing neurorehabilitation at IRCCS Centro Neurolesi (Messina, Italy). These patients were randomly assigned to one of two groups: a control group (CG) and an experimental group (EG). The CG received traditional training, while the EG underwent HA training. We evaluated the patients before (T0) and after (T1) rehabilitation using various scales, including the Montreal Cognitive Assessment (MoCA), the Beck Depression Inventory (BDI), the Hamilton Rating Scale for Anxiety (HRS-A), the 12-Item Short-Form Survey (SF-12), the Functional Independence Measure (FIM), Activities of Daily Living (ADL), Instrumental Activities of Daily Living Scale (IADL), and the EQ-5D-5L. Results: The effect of the experimental treatment showed an improvement in all patients test scores in the EG, especially regarding cognitive functions, mood disorders, activities of daily living, and quality of life. Conclusion: Our findings suggest that HA may be effective in improving daily autonomy and, in turn, alleviating mood disorders and enhancing psychological well-being.

Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

Introduction: Severe acquired brain injury (SABI) is a leading cause of death and disability, and it is defined as a brain injury that occurs after birth due to traumatic or non-traumatic causes. Reality orientation therapy (ROT) uses repeated time–place–person orientation and meaningful stimuli to develop a better understanding of the environment and has great potential as an effective strategy to improve cognitive and behavioral functioning. Objective: This study aims to investigate the feasibility and potential effects of virtual reality orientation therapy (VR-rot) on optimizing cognitive and behavioral functioning and depressive symptoms post-SABI. Method: Forty patients with SABI were enrolled from October 2022 to December 2023 and divided into two groups: the experimental group (EG, n = 20) received VR_rot, while the control group (CG, n = 20) received standard ROT (S_rot). All patients were evaluated with a psychometric battery, including the Mini-Mental State Examination (MMSE) and the Hamilton Rating Scale for Depression (HRS-D), administered before (T0) and after the end (T1) of rehabilitation. Results: Within-group comparisons indicated a statistically significant change in MMSE scores from T0 to T1 in the EG and CG, with the EG showing a greater improvement than the CG. Regarding HRS-D scores, the EG showed a statistically significant change. VR-ROT could be a valuable tool for improving cognitive–behavioral functioning in SABI patients. Conclusions: The VRRS can help reduce depressive symptoms and improve the reality orientation deficit caused by traumatic brain injury and stroke on brain tissue. This study highlights the benefits of virtual reality.

Background/Objectives: Cardiovascular diseases (CVDs) encompass a wide range of heart and vascular conditions and remain the leading cause of death worldwide. Acceptance and Commitment Therapy (ACT) is a psychotherapeutic approach that integrates acceptance, mindfulness, and commitment to value-based actions. This systematic review aims to explore the current evidence on the potential role of ACT interventions in supporting psychological well-being among individuals with CVDs. Methods: A systematic review was conducted in accordance with PRISMA guidelines. A search of the literature was conducted through Scopus, PubMed, Web of Science, Cochrane, and PsycINFO databases. Six studies met the inclusion criteria. Results: The reviewed studies suggest that ACT may promote psychological flexibility, emotion regulation, and self-care behaviors in patients with CVDs. Reported outcomes include improved mindfulness, reduced distress, and enhanced quality of life. However, the evidence base is limited in both size and methodological rigor, with included studies varying in design and population. Conclusions: While preliminary findings indicate that ACT shows promise in addressing psychological aspects of CVDs, the current evidence remains insufficient to draw definitive conclusions. Further high-quality, large-scale studies are needed to evaluate the effectiveness and clinical applicability of ACT in cardiovascular populations.