Background: Digital twin (DT) technology, integrated with artificial intelligence (AI) and machine learning (ML), holds significant potential to transform oncology care. By creating dynamic virtual replicas of patients, DTs allow clinicians to simulate disease progression and treatment responses, offering a personalized approach to cancer treatment. Aim: This narrative review aimed to synthesize existing review studies on the application of digital twins in oncology, focusing on their potential benefits, challenges, and ethical considerations. Methods: The narrative review of reviews (NRR) followed a structured selection process using a standardized checklist. Searches were conducted in PubMed and Scopus with a predefined query on digital twins in oncology. Reviews were prioritized based on their synthesis of prior studies, with a focus on digital twins in oncology. Studies were evaluated using quality parameters (clear rationale, research design, methodology, results, conclusions, and conflict disclosure). Only studies with scores above a prefixed threshold and disclosed conflicts of interest were included in the final synthesis; seventeen studies were selected. Results and Discussion: DTs in oncology offer advantages such as enhanced decision-making, optimized treatment regimens, and improved clinical trial design. Moreover, economic forecasts suggest that the integration of digital twins into healthcare systems may significantly reduce treatment costs and drive growth in the precision medicine market. However, challenges include data integration issues, the complexity of biological modeling, and the need for robust computational resources. A comparison to cutting-edge research studies contributes to this direction and confirms also that ethical and legal considerations, particularly concerning AI, data privacy, and accountability, remain significant barriers. Conclusions: The integration of digital twins in oncology holds great promise, but requires careful attention to ethical, legal, and operational challenges. Multidisciplinary efforts, supported by evolving regulatory frameworks like those in the EU, are essential for ensuring responsible and effective implementation to improve patient outcomes.

Background: Balance impairment is a common disability in post-stroke survivors, leading to reduced mobility and increased fall risk. Robotic gait training (RAGT) is largely used, along with traditional training. There is, however, no strong evidence about RAGT superiority, especially on balance. This study aims to determine RAGT efficacy on balance of post-stroke survivors. Methods: PubMed, Cochrane Library, and PeDRO databases were investigated. Randomized clinical trials evaluating RAGT efficacy on post-stroke survivor balance with Berg Balance Scale (BBS) or Timed Up and Go test (TUG) were searched. Meta-regression analyses were performed, considering weekly sessions, single-session duration, and robotic device used. Results: A total of 18 trials have been included. BBS pre-post treatment mean difference is higher in RAGT-treated patients, with a pMD of 2.17 (95% CI 0.79; 3.55). TUG pre-post mean difference is in favor of RAGT, but not statistically, with a pMD of −0.62 (95%CI − 3.66; 2.43). Meta-regression analyses showed no relevant association, except for TUG and treatment duration (β = −1.019, 95% CI − 1.827; −0.210, p-value = 0.0135). Conclusions: RAGT efficacy is equal to traditional therapy, while the combination of the two seems to lead to better outcomes than each individually performed. Robot-assisted balance training should be the focus of experimentation in the following years, given the great results in the first available trials. Given the massive heterogeneity of included patients, trials with more strict inclusion criteria (especially time from stroke) must be performed to finally define if and when RAGT is superior to traditional therapy.

Telemedicine represents a valid aid in rehabilitation process. A remote therapist in a telerehabilitation program could monitor daily motion activity and assign motion-rehabilitation tools on the basis of the fall risk. However, one problem is detection of the fall risk itself. Web-based video-camera images alone do not help the remote assessment of the fall risk using the most commonly used qualitative tests based on visual observation. A novel wearable system to assess fall risk in telerehabilitation has been proposed based on an Inertial Measurement Unit and a medical protocol. It provides a score in four levels (1: no fall risk; 4: major fall risk). The telemedicine tool is integrated to the Global System for Mobile communication (GSM) net. Each component of the wearable system has been designed and integrated. Each component in the system has been tested individually and in a closed loop. One subject was monitored in a telemedicine link. The test showed a high degree of acceptance. The tool will be furnished to subjects along with a homecare device for daily routine monitoring of motion activity and could eventually be integrated with other systems designed to monitor other physiological parameters along with different aids and monitoring tools.

Step counting is an important index of motion activity and it is currently used in the management of obesity, in applications for the prevention of cardiovascular problems, in diabetes care, and in motion rehabilitation. Commercial pedometers are not appropriate for telemonitoring subjects with motion disability such as Parkinson’s disease. The aim of this paper was to test the integration of a Gastrocnemius Expansion Measurement Unit (GEMU) for step counting in a telemedicine application involving subjects with Parkinson’s Disease. The test of GEMU in a new model of care was performed on 6 subjects with Parkinson’s disease at Level 3 of the Tinetti test of unbalance. They were asked to record binary information about the correct assumption of dopaminergic therapy. Results showed greater than 97% success of monitoring. A statistical analysis also shows (p < 0.05) the feasibility of correlating the decrement of mobility in terms of the step counting with an incorrect assumption of dopaminergic therapy. Furthermore, the methodology showed a high degree of acceptance, and the users gave suggestions for future improvements, especially related to the interface between GEMU and home care itself.

Over the past decade, the use of telehealth has garnered increasing attention. The focus on quality aspects has seen significant growth in tandem with the telehealth expansion. Having useful indicators in this area is becoming increasingly strategic for fully integrating the technology into the health domain. These indicators can help monitor and evaluate the quality of telehealth services, guiding improvements and ensuring that these digital solutions meet the necessary standards for effective healthcare delivery. The purpose of this study is to analyze Key performance indicators (KPIs) in telehealth within institutional websites and the scientific dissemination world by means of a narrative review. A narrative review was proposed with these two specific points of view based on a standardized checklist and a quality control procedure for including scientific papers in the analysis. Results from scientific studies emphasize KPIs such as patient outcomes, operational efficiency, technical reliability, and cost-effectiveness. These include measures like improvements in condition management, patient satisfaction, consultation numbers, waiting times, and cost savings. Institutional documents from entities like the WHO also show diverse perspectives, focusing on equitable access, clinical excellence, patient prioritization, response times, and patient and staff satisfaction. The findings suggest that adopting a comprehensive set of KPIs and continuously monitoring and evaluating telehealth services can enhance their effectiveness, efficiency, and equity, ultimately improving healthcare outcomes and accessibility.