Background: Artificial intelligence (AI), in the form of machine learning (ML) or deep learning (DL) models, can aid clinicians in the diagnostic process and/or in the prognosis of critically medical conditions, as for patients with a disorder of consciousness (DoC), in which both aspects are particularly challenging. DoC is a category of neurological impairments that are mainly caused by severe acquired brain injury, like ischemic or hemorrhagic strokes or traumatic injuries. The aim of this scoping review is to map the literature on the role of ML and DL in the field of diagnosis and prognosis of DoCs.Materials and methods: A scoping search, started from 3rd October 2024, was conducted for all peer-reviewed articles published from 2000 to 2024, using the following databases: PubMed, Embase, Scopus and Cochrane Library.Results: We found a total of 49,417 articles. After duplicate removal and title/abstract screening, 613 articles met the inclusion criteria, but 592 articles were excluded after full-text review. Therefore, only 21 studies involving DoC subjects were included in the review synthesis.Conclusion: Advancing AI in the field of DoC requires standardized data protocols and consideration of demographic variations. AI could enhance diagnosis, prognosis, and differentiation between states like unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). Additionally, AI-based applications personalize rehabilitation by identifying key recovery factors, optimizing patient outcomes.

Heart failure (HF) is a growing epidemic, affecting millions of people worldwide, and is a major cause of mortality, morbidity, and impaired quality of life. Traditional cardiac rehabilitation is a valuable approach to the physical and quality-of-life recovery of patients with cardiovascular disease. The innovative approach of remote monitoring through telemedicine offers a solution based on modern technologies, enabling continuous collection of health data outside the hospital environment. Remote monitoring devices present challenges that could adversely affect patient adherence, resulting in the risk of dropout. By applying a cognitive-behavioral model, we aim to identify the antecedents of dropout behavior among patients adhering to traditional cardiac rehabilitation programs and remote monitoring in order to improve the latter. Our study was conducted from October 2023 to January 2024. In the first stage, we used data from literature consultation. Subsequently, data were collected from the direct experience of 49 health workers related to both remote monitoring and traditional treatment, recruited from the authors’ workplace. Results indicate that patients with cardiovascular disease tend to abandon remote monitoring programs more frequently than traditional cardiac rehabilitation therapies. It is critical to design approaches that take these barriers into account to improve adherence and patient satisfaction. This analysis identified specific antecedents to address, helping to improve current monitoring models. This is crucial to promote care continuity and to achieve self-management by patients in the future.

Cardiovascular and neurological diseases are a major cause of mortality and morbidity worldwide. Such diseases require careful monitoring to effectively manage their progression. Artificial intelligence (AI) offers valuable tools for this purpose through its ability to analyse data and identify predictive patterns. This review evaluated the application of AI in cardiac and neurological diseases for their clinical impact on the general population. We reviewed studies on the application of AI in the neurological and cardiological fields. Our search was performed on the PubMed, Web of Science, Embase and Cochrane library databases. Of the initial 5862 studies, 23 studies met the inclusion criteria. The studies showed that the most commonly used algorithms in these clinical fields are Random Forest and Artificial Neural Network, followed by logistic regression and Support-Vector Machines. In addition, an ECG-AI algorithm based on convolutional neural networks has been developed and has been widely used in several studies for the detection of atrial fibrillation with good accuracy. AI has great potential to support physicians in interpretation, diagnosis, risk assessment and disease management.

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the world. In view of the close correlation between the functions of the body, which cannot be examined in a piecemeal manner but as an integrated system, a holistic approach allows for a comprehensive assessment of the patient. Our study aims to (i) examine the connection between CVD and cognitive deficits; (ii) examine the presence of anxiety and depression; (iii) evaluate the presence of dysphagia, pneumophonic coordination disorders, respiratory and sleep disorders; (iv) analyze the impact of CVD on the caregiver; and (v) evaluate the effectiveness of psychoeducational intervention. At least 218 patients will participate in the survey. At T0, they will undergo neuropsychological, psychological and associated clinical condition assessment through standardized tests. In addition, at least one psychoeducation meeting will be held. After 6 months (T1), the same tests will be repeated, and the effectiveness of psychoeducation will be evaluated. Assessment of associated disorders will provide a comprehensive view of the patient. In addition, it will be observed how psychoeducational intervention can make changes on general well-being. Comprehensive, multi-step observation allows for identification of risk factors, tailoring of treatment, and prevention of future complications.Clinical Trial Registration: registered on Clinicaltrials.gov (ID: NCT06413823).

Background: Cardiovascular diseases (CVD) affect the heart and blood vessels. Cardiorenal syndrome (CRS) highlights the interaction between the heart and kidneys, worsening the clinical course. Assessing renal function is essential for risk stratification and guiding therapeutic decisions. Furthermore, cognitive and psychological aspects are often impaired in these patients. Aim: To compare clinical, cognitive, emotional, and quality of life parameters between patients with CRS and those with heart failure (HF) alone, and to assess the agreement between estimated glomerular filtration rate equations (Cockcroft–Gault and CKD-EPI). Methods: This observational study was conducted at the Cardiology Unit of the IRCCS Centro Neurolesi Bonino Pulejo “Piemonte” Hospital (Messina, Italy) between June 2024 and March 2025. Thirty participants aged 45–85 years were enrolled: 15 with type 1 cardiorenal syndrome (CRS group) and 15 with heart failure without cardiorenal syndrome (HF group). All participants had a confirmed diagnosis and provided informed consent. Clinical evaluation and standardized tests (MoCA, BDI-II, BAI, and SF-12v2) were administered. Statistical analyses were performed using t-tests, chi-square tests, and Bland–Altman analysis, with significance set at p < 0.05. Results: The two groups were comparable in body mass index and left ventricular ejection fraction. CRS patients had significantly higher serum creatinine and lower GFR with both equations. The two GFR equations were strongly correlated (r = 0.94; p < 0.0001). Bland–Altman analysis showed a mean difference of 5.80 mL/min (95% limits of agreement: –12.4 to +24.0 mL/min), indicating wide individual variability. No significant differences were found in cognitive performance or quality of life. However, CRS patients exhibited significantly higher depressive symptoms (BDI-II mean 11.33 ± 8.19 vs. 5.40 ± 6.68; p = 0.0384) and a trend toward higher anxiety (BAI mean 8.13 ± 4.73 vs. 4.67 ± 5.79; p = 0.0834). Conclusions: A multidisciplinary approach, including psychological support, is necessary for patients with CRS.