In this paper, we propose a device for the Position and Orientation (P&O) reconstruction of human segmental locomotion tasks. It is based on three mono-axial accelerometers and three angular velocity sensors, geometrically arranged to form two orthogonal terns. The device was bench tested using step-by-step motor-based equipment. The characteristics of the six channels under bench test conditions were: crosstalk absent, non linearity </spl plusmn/0, 1% fs, hysteresis <0, 1% fs, accuracy 0, 3% fs, overall resolution better than 0, 04 deg/s, 2*g*10/sup -4/. The device was validated with the stereophotogrammetric body motion analyzer during the execution of three different locomotion tasks: stand-to-sit, sit-to-stand, gait-initiation. Results obtained comparing the trajectories of the two methods showed that the errors were lower than 3*10/sup -2/ m and 2 deg during a 4s of acquisition and lower than 6*10/sup -3/ m and 0.2 deg during the effective duration of a locomotory task; showing that the wearable device hereby presented permits the 3-D reconstruction of the movement of the body segment to which it is affixed for time-limited clinical applications.

(Background) Autism increasingly requires a multidisciplinary approach that can effectively harmonize the realms of diagnosis and therapy, tailoring both to the individual. Assistive technologies (ATs) play an important role in this context and hold significant potential when integrated with artificial intelligence (AI). (Objective) The objective of this study is to analyze the state of integration of AI with ATs in autism through a review. (Methods) A review was conducted on PubMed and Scopus, applying a standard checklist and a qualification process. The outcome reported 22 studies, including 7 reviews. (Key Content and Findings) The results reveal an early yet promising interest in integrating AI into autism assistive technologies. Exciting developments are currently underway at the intersection of AI and robotics, as well as in the creation of wearable automated devices like smart glasses. These innovations offer substantial potential for enhancing communication, interaction, and social engagement for individuals with autism. Presently, researchers are prioritizing innovation over establishing a solid presence within the healthcare domain, where issues such as regulation and acceptance demand increased attention. (Conclusions) As the field continues to evolve, it becomes increasingly clear that AI will play a pivotal role in bridging various domains, and integrated ATs with AI are positioned to act as crucial connectors.

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.

The integration of artificial intelligence (AI) into assistive technologies is an emerging field with transformative potential, aimed at enhancing autonomy and quality of life for individuals with disabilities and aging populations. This overview of reviews, utilizing a standardized checklist and quality control procedures, examines recent advancements and future implications in this domain. The search for articles for the review was finalized by 15 December 2024. Nineteen review studies were selected through a systematic process identifying prevailing themes, opportunities, challenges, and recommendations regarding the integration of AI in assistive technologies. First, AI is increasingly central to improving mobility, healthcare diagnostics, and cognitive support, enabling personalized and adaptive solutions for users. The integration of AI into traditional assistive technologies, such as smart wheelchairs and exoskeletons, enhances their performance, creating more intuitive and responsive devices. Additionally, AI is improving the inclusion of children with autism spectrum disorders, promoting social interaction and cognitive development through innovative devices. The review also identifies significant opportunities and challenges. AI-powered assistive technologies offer enormous potential to increase independence, reduce reliance on external support, and improve communication for individuals with cognitive disorders. However, challenges such as personalization, digital literacy among the elderly, and privacy concerns in healthcare contexts need to be addressed. Notably, AI itself is expanding the concept of assistive technology, shifting from traditional tools to intelligent systems capable of learning and adapting to individual needs. This evolution represents a fundamental change in assistive technology, emphasizing dynamic, adaptive systems over static solutions. Finally, the study emphasizes the growing economic investment in this sector, forecasting significant market growth, with AI-driven assistive devices poised to transform the landscape. Despite challenges such as high development costs and regulatory hurdles, opportunities for innovation and affordability remain. This review underscores the importance of addressing challenges related to standardization, accessibility, and ethical considerations to ensure the successful integration of AI into assistive technologies, fostering greater inclusivity and improved quality of life for users globally.

This study delves into the transformative potential of integrating augmented reality (AR) within imaging technologies, shedding light on this evolving landscape. Through a comprehensive narrative review, this research uncovers a wealth of literature exploring the intersection between AR and medical imaging, highlighting its growing prominence in healthcare. AR’s integration offers a host of potential opportunities to enhance surgical precision, bolster patient engagement, and customize medical interventions. Moreover, when combined with technologies like virtual reality (VR), artificial intelligence (AI), and robotics, AR opens up new avenues for innovation in clinical practice, education, and training. However, amidst these promising prospects lie numerous unanswered questions and areas ripe for exploration. This study emphasizes the need for rigorous research to elucidate the clinical efficacy of AR-integrated interventions, optimize surgical workflows, and address technological challenges. As the healthcare landscape continues to evolve, sustained research efforts are crucial to fully realizing AR’s transformative impact in medical imaging. Systematic reviews on AR in healthcare also overlook regulatory and developmental factors, particularly in regard to medical devices. These include compliance with standards, safety regulations, risk management, clinical validation, and developmental processes. Addressing these aspects will provide a comprehensive understanding of the challenges and opportunities in integrating AR into clinical settings, informing stakeholders about crucial regulatory and developmental considerations for successful implementation. Moreover, navigating the regulatory approval process requires substantial financial resources and expertise, presenting barriers to entry for smaller innovators. Collaboration across disciplines and concerted efforts to overcome barriers will be essential in navigating this frontier and harnessing the potential of AR to revolutionize healthcare delivery.

A clinical tool and an associated test that can assess fall-risk in elderly patients have been designed. The clinical tool was based on a wearable device with accelerometers and rate gyroscopes to identify trunk kinematic parameters. The test was based on a posturography protocol with different constraints and statistical analysis of the kinematic parameters. Statistical clustering based on the Mahalanobis distance was carried out using three groups of 30 subjects (1, age < 65 years; 2, age > or = 65 years and 3, age > or = 65 years and a fall history). The method was statistically validated using three groups of 100 subjects. The test allowed discrimination of elderly subjects with a high fall-risk with high specificity > or = 0.930 and sensitivity > or = 0.939.