Search Results (735)

Background/Objectives: Musculoskeletal disorders and their surgical treatment significantly affect global disability, healthcare utilization, and costs. Artificial intelligence (AI) is a key enabler of data-driven musculoskeletal care. Their applications include diagnostic imaging, surgical planning, risk prediction, rehabilitation, and digital health ecosystems. This narrative review synthesizes current evidence on the use of AI in orthopaedics and musculoskeletal care across five areas: diagnostic imaging, surgical planning and intraoperative augmentation, predictive analytics and patient-reported outcomes, rehabilitation intelligence and teleorthopaedics, and system-level management. An additional task is to identify translational gaps and priorities for safe, ethical, and equitable implementation of AI. Methods: A structured narrative review was conducted using targeted searches in PubMed, Scopus, and Web of Science supplemented by semantic and citation-based explorations in Semantic Scholar, OpenAlex, and Google Scholar. The main search period was January 2019 to December 2025. The retrieved peer-reviewed articles were analyzed for clinical relevance to human musculoskeletal care, quantitative outcomes, and the translational implications of the results. From the broader pool of eligible publications, 40 clinically relevant studies were selected for detailed synthesis covering imaging, surgical planning, predictive modeling, rehabilitation, and system-level applications. Owing to the significant heterogeneity in the model architectures, datasets, and endpoints, the results were organized into five predefined thematic areas. Results: The most mature evidence is for AI-assisted detection of bone fractures on radiographs, identification of implants, and use of sizing templates in preoperative planning for arthroplasty, where deep learning systems have achieved expert-level diagnostic performance (e.g., fracture detection sensitivity of approximately 90% and specificity of approximately 92% and implant identification accuracy of 97–99%) and improved the accuracy of preoperative planning compared to conventional templating. AI-based planning increases the likelihood of reducing intraoperative corrections, shortening surgery time, reducing blood loss, and improving the final functional outcomes. Predictive models can support the stratification of risk for complications, rehospitalizations, and patient-reported outcomes, although external validation remains limited and is often single-center at this stage of research. Emerging applications in rehabilitation and teleorthopaedics, including sensor-based monitoring and learning systems integrated with Patient-Reported Outcome Measures (PROMs), are conceptually promising, but are mainly limited to feasibility or pilot studies. Conclusions: AI is beginning to influence musculoskeletal care, moving beyond pattern recognition toward integrated, patient-centered decision support throughout the perioperative and rehabilitation periods. Its widespread use remains constrained by limited multicenter validation, dataset bias, algorithmic opacity, and immature regulatory and governance frameworks. Future work should prioritize prospective multicenter impact studies, repeatable revalidation of local models, integration of PROM and teleorthopedic data with health learning systems, and adaptation to changing regulatory requirements to enable safe, ethical, effective, and equitable implementation in routine orthopedic practice. Full article

Three-dimensional (3D) bioprinting has rapidly emerged as a transformative technology at the interface of biomedical engineering and regenerative medicine. By enabling the spatially controlled deposition of living cells, biomaterials, and bioactive molecules, it offers an unprecedented potential to fabricate functional tissues and potentially whole organs in the future. This review explores recent advances in bioprinting materials, processes, and applications, emphasizing the integration of bioinks, printing methods, and mechanical design principles that underpin tissue functionality. Natural and synthetic biomaterials such as hydrogels (e.g., collagen, alginate), polyethylene glycol (PEG), and polyesters like PLGA are evaluated in terms of biocompatibility, printability, and degradation behavior. Key bioprinting modalities, including extrusion, inkjet, and laser-assisted bioprinting, are compared based on printing resolution, cell viability, and scalability. Structural considerations such as scaffold architecture, mechanical stability, and biomimetic design are discussed in relation to native tissue mechanics and requirements. The review also surveys emerging applications in tissue engineering (e.g., bone, cartilage, skin replacements), organ-on-a-chip systems for drug testing, and patient-specific implants, while addressing persistent challenges such as standardization of biofabrication, regulatory and ethical considerations, and manufacturing scale-up. Finally, future trends, including the integration of artificial intelligence (AI) and robotic automation, multi-material and four-dimensional (4D) bioprinting, and the maturation of personalized bioprinting strategies, are highlighted as pathways toward more autonomous and clinically relevant bioprinting systems. Collectively, these developments signify a paradigm shift in how biological constructs are designed and manufactured, bridging the gap between laboratory research and clinical translation. Full article

Background and Objectives: Artificial intelligence (AI) and machine learning (ML) are increasingly integrated into audiology, supporting diagnosis, screening, rehabilitation, and digital health. Despite rapid growth, the literature remains methodologically and clinically heterogeneous, limiting a consolidated view of research trajectories and translational readiness. This scoping review examined the evolution of AI and ML applications in audiology and hearing disorders, focusing on thematic development, research productivity, collaboration patterns, and clinical orientation. Methods: A scoping review was conducted using the Web of Science Core Collection (Science Citation Index Expanded). Original and review articles published between 1995 and 2025 were included. Bibliometric and thematic mapping were applied to analyze publication trends, citation patterns, keyword evolution, and collaboration networks. A structured translational categorization assessed clinical domains and validation maturity. Findings reflect the Web of Science-indexed segment of the literature. Results: A total of 127 publications were analyzed. Research output increased markedly after 2020, with an estimated doubling time of approximately 2.1 years. China, the United States, and South Korea contributed the highest publication volumes, although citation impact did not consistently parallel productivity. Thematic analyses revealed a shift toward AI-driven methodological frameworks, particularly in machine learning, deep learning, and cochlear implant-related applications. Most studies remain at proof-of-concept or internally validated stages, with limited external validation. Emerging areas include tele-audiology and personalized hearing aid optimization. Conclusions: AI and ML research in audiology is increasingly application-oriented; however, broader external validation and prospective implementation are required to support routine clinical integration. Full article

Background: Heart failure (HF) is a progressive, multisystem syndrome characterized by recurrent decompensation, high hospitalization rates, and substantial mortality. Conventional HF management is mainly episodic and often fails to detect worsening conditions in advanced disease. Digital medicine and remote patient monitoring (RPM) hold promise for advancing HF care by enabling earlier detection, proactive action, and personalized care. Methods: We conduct a narrative review to summarize evidence from randomized clinical trials, real-world registries, and emerging digital health technologies regarding the present and future utility of digital medicine in HF care. There is greater emphasis on pathophysiology-based surveillance, personalized care models, and integration into planned health care pathways. Results: Integrated digital interventions, such as implantable hemodynamic monitoring, organized telemedicine programs, or device-based diagnostic technologies, can minimize HF hospitalizations, prolong life, improve quality of life, and optimize resource utilization in health care systems when incorporated into coordinated care. Crucially, trials emphasize that clinical benefit depends not on technology but on a prompt clinical response, multidisciplinary cooperation, and ongoing interaction between the patient and the doctor. New technologies—including voice-based biomarkers, smartphone-derived photoplethysmography, ballistocardiography, and artificial intelligence–driven data integration—may help transition RPM from a hardware-based system to a scalable, “deviceless” approach. Conclusions: Digital medicine is a game-changer for reimagining HF care, involving not only continuous monitoring of physiological changes but also personalized, proactive clinical decision-making. To implement truly patient-centered, predictive HF management in the years to come, technological innovation must be combined with human connection, ethical governance, and health-system readiness. Full article

Background: Digital implant impressions using intraoral scanners are increasingly adopted; however, their accuracy remains challenging in complete-arch and extended edentulous scenarios due to limited anatomical reference points and cumulative stitching errors. Various splinting techniques, scan-body modifications, and auxiliary geometric devices have been proposed to enhance digital accuracy, yet the available evidence is highly heterogeneous and lacks comprehensive synthesis. Methods: This scoping review was conducted according to PRISMA-ScR guidelines. A systematic search of PubMed/MEDLINE, Embase, Scopus, and Web of Science databases identified studies evaluating materials, designs, or techniques intended to splint, stabilize, or geometrically augment intraoral scan bodies in digital implant workflows. In vitro, clinical, and mixed-design studies were included. Data were extracted descriptively and synthesized narratively. Results: Seventy-three studies met the inclusion criteria, the majority of which were in vitro investigations focused on fully edentulous arches. Splinting strategies included direct resin-based connections, rigid or semi-rigid auxiliary geometric devices, modified scan bodies with extensional geometries, and artificial landmarks. Most studies reported improved trueness, precision, or scanning efficiency when rigid or geometrically enriched devices were used, particularly in long-span or angulated implant configurations. However, flexible or optically interfering splints occasionally reduced accuracy, and outcomes were strongly scanner-dependent. Conclusions: Splinting and auxiliary scanning strategies generally enhance the accuracy of complete-arch digital implant impressions, especially when rigid, well-engineered, or geometrically complex designs are employed. Modified scan bodies and calibrated auxiliary devices appear particularly promising, while flexible splints may be counterproductive. Standardized protocols and further in vivo validation are required to optimize digital implant workflows. Full article

Background/Objectives: It is essential to test the accuracy of artificial intelligence-assisted tools that detect dental pathologies from radiographs. This study aimed to evaluate the test characteristics of an artificial intelligence-assisted convolutional neural network-based prototype used for automated radiographic detection. Methods: A total of 300 panoramic and 100 intraoral periapical radiographs were collected between January 2020 and 2024 and then analyzed by two trained, independent specialist evaluators. The diagnostic consensus, “ground truth”, was labeled as follows: BL: bone loss; C: caries; F: filling; I: implants; IT: impacted teeth; P: prosthesis; PC: post-core; PR: periapical radiolucency; RF: root fillings; and RR: retained roots. The radiographs were uploaded to the prototype, and the results were compared. Sensitivity, specificity, positive predictive value, and negative predictive value were calculated using Stata version 15.0 (StataCorp). Results: Overall, most of the outcomes demonstrated sensitivity greater than 82%, with values ranging from 66.41% (65.47,67.36) for BL to 100% (100.00,100.00) for I. For all outcomes, specificity was greater than 93%, with values ranging from 93.61% (93.12,94.10) for BL to 100% for I. The overall values for all the test characteristics for the periapical radiographs were above 85%. The key errors identified in the qualitative analysis were errors in tooth identification, failure to detect recurrent caries under fillings and crowns, impacted canines, and inaccurate identification of extensive fillings as crowns. Conclusions: The prototype demonstrated high sensitivity and specificity in identifying dental pathologies. Accuracy in identifying bone loss, teeth that have migrated, including impacted canines, secondary caries, and differentiating extensive fillings from crowns requires further improvement. Full article

The integration of 3D printing and artificial intelligence is transforming healthcare management by driving innovations in personalized care, supply chain operations, and clinical workflows. This review offers a comprehensive overview and in-depth analysis of recent (2018–2025) applications where AI technologies enhance 3D printing within healthcare. We explore how AI-powered design and optimization facilitate the creation of patient-specific medical devices, implants, and even bioprinted tissues, while intelligent process control increases both quality and efficiency. Additionally, we examine regulatory and ethical considerations, including the evolution of frameworks for AI-enabled devices, as well as challenges in data governance, validation, and equitable access. The review takes a global perspective, presenting real-world case studies that showcase both successful implementations and ongoing challenges. We also discuss various perspectives and controversies, such as the balance between innovation and safety in autonomous AI design, and highlight areas where further research is needed. In contrast to previous narrative reviews that focus solely on clinical applications or technical aspects, this review uniquely evaluates the combined impact of AI and 3D printing on healthcare management—including cost-effectiveness, governance, decision-making processes, and point-of-care manufacturing. This work is particularly valuable for hospital administrators, clinical operations leaders, health policymakers, and biomedical innovation teams seeking to understand the broader implications of AI-enhanced 3D printing in healthcare management. Nevertheless, despite promising advancements, the field is constrained by heterogeneous evidence, a lack of standardized evaluation metrics, and insufficient long-term outcome data, which together limit the ability to fully assess the sustained impact of AI-integrated 3D printing in healthcare environments. Full article

Bicuspid Aortic Valve (BAV) disease is recognized as the most common congenital heart condition and is frequently associated with complex valvular and aortic disorders. Cardiovascular computed tomography (CT) has become essential for diagnosing BAV, planning procedures, and evaluating patients after treatment. This is largely due to CT’s high spatial resolution and its ability to perform volume imaging effectively. This review provides an up-to-date overview of the increasing role of cardiovascular CT in the management of bicuspid aortic valve (BAV). It covers various aspects, including BAV morphology, optimal sizing for transcatheter aortic valve replacement (TAVR), and post-procedural monitoring. We highlight significant innovations, such as supra-annular sizing techniques and artificial intelligence (AI)-guided analysis, that position CT at the nexus of anatomy, function, and targeted treatment. Additionally, we address controversies concerning inconsistencies in sizing algorithms, recent classification challenges, and radiation exposure. Future development areas include AI predictive tools, radiomic phenotyping, and CT-guided precision medicine. This synthesis aims to provide clinicians and researchers with a high-level guide to the clinical integration of cardiovascular CT and its future in the BAV population. This review provides the most current, comprehensive synthesis on the pivotal role of cardiovascular CT in BAV management, offering a roadmap for integrating advanced imaging into clinical practice and guiding future research priorities. Full article

This study presents Care-MOVE, a smartphone-based application designed for continuous, passive, and unobtrusive monitoring of mobility, environmental exposure, and cognitive status in older adults within a telemedicine framework. The system integrates movement-related data collected through smartphone sensors (GPS, activity recognition, and caloric expenditure estimation) with contextual air quality information and standardized neuropsychological assessments, resulting in a comprehensive multimodal dataset (Care-MOVE Dataset). An exploratory proof-of-concept study was conducted on a subsample of 53 participants aged over 65, each monitored continuously for five days, contributing on average more than 30,000 longitudinal records. To investigate whether daily motor behavior can serve as a digital biomarker of cognitive functioning, several Machine Learning and Deep Learning models were evaluated using a Leave-One-User-Out (LOUO) cross-validation strategy. The comparative analysis included traditional classifiers (Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbors, and Support Vector Machines) as well as temporal deep learning architectures (1D CNN, LSTM, GRU, and Transformer). Among all of the evaluated approaches, the Support Vector Machine with RBF kernel achieved the best performance, reaching an accuracy of 98.1%, a balanced accuracy of 0.988, and an F1-score of 0.981, demonstrating robust generalization across unseen subjects. For this reason, the study was designed and presented as an exploratory proof-of-concept rather than a definitive clinical validation. This integrated approach not only enables the collection of detailed and contextualized data but also opens new perspectives for proactive digital healthcare, focused on risk prevention, improving quality of life, and promoting autonomy in elderly patients. Full article

The growing demand for sustainable and distributed energy solutions has driven increasing interest in triboelectric nanogenerators (TENGs) as platforms for energy harvesting and self-powered sensing. Biowaste-based triboelectric nanogenerators (BW-TENGs) represent an attractive strategy by coupling renewable energy generation with waste valorization under the principles of the circular bioeconomy. This review provides a comprehensive overview of BW-TENGs, encompassing fundamental triboelectric mechanisms, material categories, processing and surface-engineering strategies, device architectures, and performance evaluation metrics. A broad spectrum of biowaste resources—including agricultural residues, food and marine waste, medical plastics, pharmaceutical waste, and plant biomass—is critically assessed in terms of physicochemical properties, triboelectric behavior, biodegradability, biocompatibility, and scalability. Recent advances demonstrate that BW-TENGs can achieve electrical outputs comparable to conventional synthetic polymer TENGs while offering additional advantages such as environmental sustainability, mechanical compliance, and multifunctionality. Key application areas, including environmental monitoring, smart agriculture, wearable and implantable bioelectronics, IoT networks, and waste management systems, are highlighted. The review also discusses major challenges limiting large-scale deployment, such as material heterogeneity, environmental stability, durability, and lack of standardization, and outlines emerging solutions involving material engineering, hybrid energy-harvesting architectures, artificial intelligence-assisted optimization, and life cycle assessment frameworks. Full article

Objectives: To compare artificial intelligence (AI) crown design with expert or non-AI computer-aided (CAD) design for single-unit tooth and implant-supported crowns across efficiency, marginal and internal fit, morphology and occlusion, and mechanical performance. Materials and Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. PubMed MEDLINE, Scopus, Web of Science, IEEE Xplore, and Dentistry and Oral Sciences Source were searched from 2016 to 2025 with citation chasing. Eligible studies directly contrasted artificial intelligence-generated or artificial intelligence-assisted crown designs with human design in clinical, ex vivo, or in silico settings. Primary outcomes were design time, marginal and internal fit, morphology and occlusion, and mechanical performance. Risk of bias was assessed with ROBINS-I for non-randomized clinical studies, QUIN for bench studies, and PROBAST + AI for computational investigations, with TRIPOD + AI items mapped descriptively. Given heterogeneity in settings and endpoints, a narrative synthesis was used. Results: A total of 14 studies met inclusion criteria, including a clinical patient study, multiple ex vivo experiments, and in silico evaluations. Artificial intelligence design reduced design time by between 40% and 90% relative to expert computer-aided design or manual workflows. Marginal and internal fit for artificial intelligence and human designs were statistically equivalent in multiple comparisons. Mechanical performance matched technician designs in load-to-fracture testing, and modeling indicated stress distributions similar to natural teeth. Overall risk of bias was judged as some concerns across tiers. Conclusions: Artificial intelligence crown design delivers efficiency gains while showing short-term technical comparability across fit, morphology, occlusion, and strength for single-unit crowns in predominantly bench and in silico evidence, with limited patient-level feasibility data. Prospective clinical trials with standardized, preregistered endpoints are needed to confirm durability, generalizability, and patient-relevant outcomes, and to establish whether short-term technical advantages translate into clinical benefit. Full article

Background and Objectives: Mechanical alignment (MA) has long been the gold standard in total knee arthroplasty (TKA), aiming for neutral hip–knee–ankle alignment with proven long-term survivorship. However, up to 20% of patients remain dissatisfied, often due to neglect of individual constitutional limb variation and subsequent soft tissue imbalance. This has driven the development of alternative alignment philosophies. This current concepts review aims to determine the various evolving alignment strategies, elucidate their underlying principles, and demonstrate the available clinical outcomes data. Materials and Methods: This review examines MA and the paradigm shift towards personalized alignment techniques, including Kinematic Alignment (KA), restricted Kinematic Alignment (rKA), inverse Kinematic Alignment (iKA), adjusted mechanical alignment (aMA), and the most recent evolution, Functional Alignment (FA). Results: Kinematic alignment and its derivatives (rKA, iKA) seek to better replicate native joint morphology and tension, often reducing the need for soft tissue releases and improving functional outcomes compared to MA. rKA and iKA introduce protective boundaries to avoid extreme phenotypes and possible instability. FA leverages robotic platforms and integrates these principles with real-time gap balancing, demonstrating promise for consistent, personalized outcomes. Some reports, however, advise caution with adjusted Mechanical Alignment (aMA), particularly those that result in phenotypes such as Coronal Plane Alignment of the Knee (CPAK) VII or VIII, which may increase the risk of revision. Conclusions: The philosophy of TKA has evolved from a uniform mechanical target (MA) to a more nuanced, patient-specific strategy. While promising mid- to long-term outcomes and comparable survival data support the viability of KA and its derivatives, critical needs remain, including standardizing nomenclature (especially for FA) and conducting high-quality comparative trials. Future directions involve leveraging high-volume intraoperative data and Artificial Intelligence (AI) to refine decision-making and further personalize alignment strategies, without compromising long-term implant survivorship. Full article

Chronic liver disease remains a leading cause of global mortality, yet organ shortages and transplant complications limit the efficacy of orthotopic liver transplantation. While extracorporeal support systems serve as temporary bridges, they fail to restore long-term patient autonomy or replicate complex biosynthetic functions. This systematic review, conducted in accordance with PRISMA 2020 guidelines, evaluates recent advancements in implantable artificial livers (IALs) designed for permanent functional integration. We analyzed 71 eligible studies, assessing cellular sources, fabrication strategies, maturation processes, and functional readiness. Our findings indicate significant progress in stem-cell-derived hepatocytes and bioactive scaffolds, such as decellularized extracellular matrix (dECM). However, a critical technological gap remains in scaling current sub-centimeter prototypes toward clinically relevant volumes (~200 mL). Key engineering challenges include integrating hierarchical vascular networks, requiring primary vessels exceeding 2 mm in diameter for surgical anastomosis, and functional biliary systems to prevent cholestatic injury. Furthermore, while micro-vascularization and protein synthesis are well documented, higher-order functions such as spatial zonation and coordinated metabolic stability remain underreported. Future clinical translation necessitates advancements in multi-cellular patterning, microfluidic-driven maturation, and autologous reprogramming. This review provides a comprehensive roadmap for bridging the gap between biofabricated constructs and organ-scale hepatic replacement, emphasizing the need for standardized functional benchmarks to ensure long-term success. Full article

Background and aim: Artificial intelligence (AI) is gaining increasing relevance in orthopaedic surgery, particularly in prosthetic surgery, due to its ability to support preoperative planning through advanced imaging analysis, implant size prediction, and outcome forecasting. However, recent literature shows considerable variability in employed models, evaluated outcomes, and clinical applicability. The objective of this scoping review is to map AI applications in preoperative planning for orthopaedic arthroplasties and to assess their impact on radiographic and clinical outcomes, also discussing key ethical and medicolegal implications within both Italian and international contexts. Materials and methods: A literature review was conducted following scoping review methodology. The bibliographic search (10 September 2025) was performed in PubMed and Scopus using the query “preoperative planning WITH artificial intelligence AND prosthesis orthopaedic surgery AND outcomes”, restricted to the years 2020–2025, English-language studies, and research focused specifically on real-world AI techniques applied to preoperative planning in prosthetic surgery, reporting radiographic and/or clinical outcomes related to planning. Exclusion criteria included intra/postoperative studies, non-orthopaedic applications, robotic surgery, studies lacking clinical outcomes, case reports, and articles without full-text availability. After PRISMA screening and selection, 42 primary studies were included. Results: Of the 42 studies included, 20 focused on the hip, 19 on the knee, and 3 on the shoulder. Available evidence indicates that AI may improve templating accuracy and prosthetic component positioning, with more robust results in hip and knee arthroplasty, while applications in shoulder arthroplasty remain emerging. Nonetheless, important methodological limitations persist, including algorithm heterogeneity. Discussion: Overall, the findings suggest a promising role for AI in preoperative planning; however, the heterogeneity and variable quality of the evidence call for caution in interpretation and highlight the need for more rigorous prospective research. These considerations also carry relevant medicolegal implications, as the reliability and standardisation of AI-based tools represent essential prerequisites for their safe and conscious integration within diverse regulatory frameworks. Conclusions: AI appears to be a promising tool in the preoperative planning of orthopaedic arthroplasties, although further clinical validation and methodological standardisation are required. The evidence gathered also provides a useful foundation for addressing the associated medicolegal and regulatory implications, particularly in light of evolving Italian and European regulations and their differences from U.S. models. Full article

The memristor has long been known as a nonvolatile memory technology alternative and has recently been explored for neuromorphic computing, owing to its capability to mimic the synaptic plasticity of the human brain. The architecture of a memristor synapse device allows ultra-high-density integration by internetworking with crossbar arrays, which benefits large-scale training and learning using advanced machine-learning algorithms. In this review, we present a statistical analysis of neuromorphic computing device publications from 2018 to 2025, focusing on various memristive systems. Furthermore, we provide a device-level perspective on biomimetic properties in hardware neural networks such as short-term plasticity (STP), long-term plasticity (LTP), spike timing-dependent plasticity (STDP), and spike rate-dependent plasticity (SRDP). Herein, we highlight the utilization of optoelectronic synapses based on 2D materials driven by a sequence of optical stimuli to mimic the plasticity of the human brain, further broadening the scope of memristor controllability by optical stimulation. We also highlight practical applications ranging from MNIST dataset recognition to hardware-based pattern recognition and explore future directions for memristor synapses in healthcare, including artificial cognitive retinal implants, vital organ interfaces, artificial vision systems, and physiological signal anomaly detection. Full article