During the last ten years the use of robotic-assisted rehabilitation has increased significantly. Compared with traditional care, robotic rehabilitation has several potential advantages. Platform-based robotic rehabilitation can help patients recover from musculoskeletal and neurological conditions. Evidence on how platform-based robotic technologies can positively impact on disability recovery is still lacking, and it is unclear which intervention is most effective in individual cases. This systematic review aims to evaluate the effectiveness of platform-based robotic rehabilitation for individuals with musculoskeletal or neurological injuries. Thirty-eight studies met the inclusion criteria and evaluated the efficacy of platform-based rehabilitation robots. Our findings showed that rehabilitation with platform-based robots produced some encouraging results. Among the platform-based robots studied, the VR-based Rutgers Ankle and the Hunova were found to be the most effective robots for the rehabilitation of patients with neurological conditions (stroke, spinal cord injury, Parkinson’s disease) and various musculoskeletal ankle injuries. Our results were drawn mainly from studies with low-level evidence, and we think that our conclusions should be taken with caution to some extent and that further studies are needed to better evaluate the effectiveness of platform-based robotic rehabilitation devices.

Artificial Intelligence (AI) and Machine Learning (ML) have expanded their utilization in different fields of medicine. During the SARS-CoV-2 outbreak, AI and ML were also applied for the evaluation and/or implementation of public health interventions aimed to flatten the epidemiological curve. This systematic review aims to evaluate the effectiveness of the use of AI and ML when applied to public health interventions to contain the spread of SARS-CoV-2. Our findings showed that quarantine should be the best strategy for containing COVID-19. Nationwide lockdown also showed positive impact, whereas social distancing should be considered to be effective only in combination with other interventions including the closure of schools and commercial activities and the limitation of public transportation. Our findings also showed that all the interventions should be initiated early in the pandemic and continued for a sustained period. Despite the study limitation, we concluded that AI and ML could be of help for policy makers to define the strategies for containing the COVID-19 pandemic.

Background: IntraUterine Growth Restriction (IUGR) is a global public health concern and has major implications for neonatal health. The early diagnosis of this condition is crucial for obtaining positive outcomes for the newborn. In recent years Artificial intelligence (AI) and machine learning (ML) techniques are being used to identify risk factors and provide early prediction of IUGR. We performed a systematic review (SR) and meta-analysis (MA) aimed to evaluate the use and performance of AI/ML models in detecting fetuses at risk of IUGR. Methods: We conducted a systematic review according to the PRISMA checklist. We searched for studies in all the principal medical databases (MEDLINE, EMBASE, CINAHL, Scopus, Web of Science, and Cochrane). To assess the quality of the studies we used the JBI and CASP tools. We performed a meta-analysis of the diagnostic test accuracy, along with the calculation of the pooled principal measures. Results: We included 20 studies reporting the use of AI/ML models for the prediction of IUGR. Out of these, 10 studies were used for the quantitative meta-analysis. The most common input variable to predict IUGR was the fetal heart rate variability (n = 8, 40%), followed by the biochemical or biological markers (n = 5, 25%), DNA profiling data (n = 2, 10%), Doppler indices (n = 3, 15%), MRI data (n = 1, 5%), and physiological, clinical, or socioeconomic data (n = 1, 5%). Overall, we found that AI/ML techniques could be effective in predicting and identifying fetuses at risk for IUGR during pregnancy with the following pooled overall diagnostic performance: sensitivity = 0.84 (95% CI 0.80–0.88), specificity = 0.87 (95% CI 0.83–0.90), positive predictive value = 0.78 (95% CI 0.68–0.86), negative predictive value = 0.91 (95% CI 0.86–0.94) and diagnostic odds ratio = 30.97 (95% CI 19.34–49.59). In detail, the RF-SVM (Random Forest–Support Vector Machine) model (with 97% accuracy) showed the best results in predicting IUGR from FHR parameters derived from CTG. Conclusions: our findings showed that AI/ML could be part of a more accurate and cost-effective screening method for IUGR and be of help in optimizing pregnancy outcomes. However, before the introduction into clinical daily practice, an appropriate algorithmic improvement and refinement is needed, and the importance of quality assessment and uniform diagnostic criteria should be further emphasized.

Introduction Access to essential medicines is a critical element of health systems and an important measure of their performance. Migrants may face barriers in accessing healthcare, including essential medicines, throughout the migration cycle, which includes the stages of departure from home or residence countries, transit through non-European or European countries, reception and settlement in a country in Europe and deportation. We aim to provide an overview of research and grey literature concerning access to essential medicines for asylum seekers, refugees and undocumented migrants in or heading to Europe (European Union, European Economic Area, Switzerland and the UK). Methods To delineate and conceptualise access to medicines, we considered the definition of the Lancet Commission on Essential Medicines and the Pharmaceutical Management framework. These frameworks were combined to guide several critical steps in our review, including defining the search terms, data extraction, data analyses and reporting. Relevant studies and reports were identified through searches in bibliographic and grey literature databases. Results Out of 5760 studies and 66 grey literature reports, 108 met the inclusion criteria, with 72 focusing on medicine access. Overall, medicine use and medicine expenditure were found to be lower in migrant populations compared with the host population in many European countries. Although many studies focused on the use of infectious disease and psychotropic medicines, the most frequently used medicines by migrants were analgesics, hypertension and diabetes medicines. Determinants of medicine access were legal restrictions, language and transit times, which all contributed to interruption of and inequities in access to medicines among this population. This scoping review also indicated significant gaps in the literature regarding the evidence on access to medicine at different stages of the migration cycle, specifically in departure, transit and deportation stages. Conclusion Overall, our findings highlighted significant unmet medicine needs among migrants in or on the way to Europe and access disparities attributable to various interconnected barriers. Urgent access is needed to address such inequities, particularly legal barriers, including registration of certain medicines required for treatment. Future research should prioritise investigating medicine access during departure, transit and deportation stages. Policy discussions around migrants’ access to medicines should be centred on framing healthcare as a fundamental right.