Robot-assisted total knee arthroplasty (TKA) with functional alignment enables precise bone cuts and soft-tissue preservation; however, array placement often requires additional or extended incisions, which may increase surgical invasiveness. To address these limitations, we developed the Minimal-Incision and Minimal-Soft-Tissue-Injury (MISI) technique, combining robotic precision with true minimally invasive principles. The MISI technique utilizes an approximately 10-cm midline incision with a mini-medial parapatellar approach. It allows secure array fixation through the primary incision while applying functional alignment principles to preserve the soft-tissue envelope. Femoral pins are inserted intra-incisionally, and tibial fixation employs a hybrid approach: one intra-incisional pin and a second pin placed through a 5-mm stab incision to reduce skin tension, particularly in flexion. Bone registration and intraoperative planning are performed using the MAKO robotic system within a functional alignment philosophy. All bone cuts are performed using a mobile window technique at 90° flexion, except for the anterior chamfer cut, which is carried out at 120° flexion to allow adequate clearance between the saw blade and the tibial array. This technique was performed in 82 patients, through which we achieved encouraging early wound healing and high patient satisfaction. The MISI technique offers a reproducible, incision-sparing approach to minimize soft-tissue trauma in robotic TKA. Prospective studies evaluating complication rates, recovery, and patient-reported outcomes are warranted to validate its clinical benefits and determine optimal patient selection.

Multiportal robotic-assisted thoracic surgery (mRATS) is the standard technique for which robotic systems were originally designed, while uniportal robotic-assisted thoracic surgery (uRATS) combines robotic precision with a single-incision approach. This study provides the first unicentric comparison, evaluating whether uRATS is non-inferior in postoperative recovery and safety. This retrospective cohort study was conducted at a single center and included 402 patients undergoing RATS anatomical pulmonary resections for resectable NSCLC (February 2019-August 2024). Inverse probability weighting was applied to balance baseline characteristics, between uRATS (n = 58) and mRATS (n = 344). postoperative complications. Secondary endpoints: operative times, chest drainage duration and hospital stay. Complication rates were comparable (uRATS: 39.7%, mRATS: 44.5%, p = 0.588), with prolonged air leak (PAL) being the most frequent (34.0% vs. 34.5%, p = 1.000). Operative times were similar (median 195 vs. 190min, p > 0.05). Recovery metrics, including chest drainage duration (median: 4 days) and hospital stay (4-5 days), showed no significant differences. After adjustment for TNM stage and functional variables, FEV1% emerged as the main predictor of recovery outcomes. In this unicentric experience, uRATS and mRATS showed comparable short-term safety and recovery outcomes. uRATS appears to be a feasible alternative, particularly for surgeons familiar with uniportal techniques. Prospective studies are needed to confirm these findings and assess long-term oncological outcomes and patient-centered metrics.

Abstract Remanufacturing of electronics is essential for circular and sustainable manufacturing but faces challenges due to product diversity, varying conditions, and usage histories. Purely technical or automated solutions are insufficient; instead, adaptive systems combining human expertise and intelligent technologies are needed. This conceptual paper addresses how efficient remanufacturing can be enabled through a Hybrid Intelligence System integrating dynamic planning, AI-based personalization, and human-robot collaboration. Using a Human-Technology-Organization framework, the study situates technological developments within a broader socio-technical context to ensure efficiency gains align with human acceptance, organizational feasibility, and sustainability goals. From a human perspective, the focus is on personalization and cognitive load reduction, where AI models adapt to worker needs in a non-intrusive, privacy-preserving manner. From a technological perspective, hybrid intelligence enables dynamic planning, reinforcement learning for uncertain product states, and AR/VR-supported communication between humans and robots. The organizational dimension explores new models of work sharing that align human expertise with robotic precision, allowing scalable solutions for diverse products. Related work indicates that dynamic planning can reduce throughput times by up to 30% under uncertainty, while human-robot collaboration has the chance to improves productivity compared to manual processes. Embedding hybrid intelligence in organizational routines raises new questions of transparency, trust, and adaptability, addressed through experiments in the RePAIR Lab. Overall, the approach reframes remanufacturing as a socio-technical system where circularity, digital transformation, and human-centered design converge-advancing sustainable, human-centered manufacturing and bridging the gap between the ambitions of a circular economy and industrial practice.

High-intensity focused ultrasound (HIFU) for prostate cancer - step by step technique

Artificial intelligence (AI) technologies are increasingly being trialled with applications spanning imaging, robotic assistance and early risk prediction. Children's unique legal status presents distinct ethical and legal concerns. The objective of this review was to outline key ethical and legal challenges from AI integration into paediatric surgery and propose practical strategies for clinicians and policymakers. This article is a narrative review of peer-reviewed and grey literature (2015-2025), and statutory guidance (including the Information Commissioner's Office [ICO] Children's Code, Medicines and Healthcare products Regulatory Agency [MHRA] Software as a Medical Device Roadmap, General Medical Council guidance), and documentation from paediatric AI hubs. Ethical analysis was framed using principlism (autonomy, beneficence, non-maleficence, justice), with modifications for paediatric contexts (evolving capacity, best interests). Five core challenges were identified: safeguarding sensitive paediatric data; facilitating informed consent; mitigating algorithmic bias; clarifying liability in adaptive systems; and harmonising regulatory oversight. Recommendations include federated learning networks, transparent model cards, a pooled indemnity fund, AI literacy training and ongoing performance audits. AI holds promise for safer, more equitable paediatric surgery with robust ethical safeguards, updated legal frameworks and sustained regulatory vigilance. What is already known: (i) AI enhances image guidance, robotic precision and risk prediction in surgery; (ii) children's data require special protection under the UK General Data Protection Regulation and ICO Children's Code; and (iii) NHS England funds AI pilots, but paediatric-specific governance remains underdeveloped. This study: (i) maps UK-specific ethical (privacy, consent, bias) and legal (Montgomery, Consumer Protection Act 1987, MHRA) duties in AI-assisted paediatric surgery; (ii) proposes a federated data-sharing consortium and tiered liability model, and offers a practical governance framework for clinicians, regulators, and NHS boards.

Endoscopic thoracic sympathectomy, while effective for palmar hyperhidrosis, results in devastating compensatory sweating and autonomic dysfunction affecting >80% of patients. We present our institutional evolution of robotic-assisted sympathetic trunk reconstruction (STR) for post-sympathectomy complications. Our prospective series of 23 patients underwent robotic STR with free nerve grafting (mean follow-up: 2 years). Six-month outcomes demonstrated significant improvement: Chest severity 9.4 ± 0.9 to 6.0 ± 2.4 ( p < 0.001), back severity 9.3 ± 0.8 to 6.1 ± 2.6 ( p < 0.001), with sustained gains at 2 years. To minimize donor site morbidity, we progressively transitioned to free intercostal nerve autografts, followed by vascularized intercostal nerve (vICN) grafting beginning January 2025. Vascularized grafts maintained immediate perfusion, enabling continuous Schwann cell proliferation and accelerated recovery. A propensity score-matched analysis of vICN versus free intercostal grafts achieved 100% technical success with no vascular complications. Six-month vICN recipients demonstrated continuous improvement without temporary worsening observed in controls. Recently, single-port robotic systems substantially reduced postoperative chest wall morbidity. These innovations demonstrate that precisely executed microsurgical technique, enabled by robotic precision and interdisciplinary expertise, offers viable treatment for carefully selected patients with intolerable post-sympathectomy complications.

Artificial intelligence (AI) is rapidly reshaping adult cardiac surgery, enabling more accurate diagnostics, personalized risk assessment, advanced surgical planning, and proactive postoperative care. Preoperatively, deep-learning interpretation of ECGs, automated CT/MRI segmentation, and video-based echocardiography improve early disease detection and refine risk stratification beyond conventional tools such as EuroSCORE II and the STS calculator. AI-driven 3D reconstruction, virtual simulation, and augmented-reality platforms enhance planning for structural heart and aortic procedures by optimizing device selection and anticipating complications. Intraoperatively, AI augments robotic precision, stabilizes instrument motion, identifies anatomy through computer vision, and predicts hemodynamic instability via real-time waveform analytics. Integration of the Hypotension Prediction Index into perioperative pathways has already demonstrated reductions in ventilation duration and improved hemodynamic control. Postoperatively, machine-learning early-warning systems and physiologic waveform models predict acute kidney injury, low-cardiac-output syndrome, respiratory failure, and sepsis hours before clinical deterioration, while emerging closed-loop control and remote monitoring tools extend individualized management into the recovery phase. Despite these advances, current evidence is limited by retrospective study designs, heterogeneous datasets, variable transparency, and regulatory and workflow barriers. Nonetheless, rapid progress in multimodal foundation models, digital twins, hybrid OR ecosystems, and semi-autonomous robotics signals a transition toward increasingly precise, predictive, and personalized cardiac surgical care. With rigorous validation and thoughtful implementation, AI has the potential to substantially improve safety, decision-making, and outcomes across the entire cardiac surgical continuum.

Objective: This study investigated the efficacy and neural mechanisms of robot-assisted mirror therapy (RMT) for post-stroke upper limb rehabilitation. RMT integrates the multimodal feedback of mirror therapy with robotic precision and repetition to enhance cortical activation and neuroplasticity. Methods: Seventy-eight stroke patients were randomly assigned to control, mirror therapy (MT), or RMT groups. All received conventional rehabilitation; the MT group additionally underwent mirror therapy, and the RMT group received robot-assisted mirror therapy combined with functional electrical stimulation. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity (FMA-UE), with secondary measures including spasticity, dexterity, daily living, and quality of life. Functional near-infrared spectroscopy (fNIRS) was applied to assess cortical activation and connectivity at baseline, post-intervention, and one-month follow-up. Results: All groups showed significant time effects, though between-group differences were limited. Subgroup analysis revealed that patients at Brunnstrom stages I-II in the MT group achieved greater improvements in upper limb function, dexterity, and daily living ability. fNIRS findings showed enhanced activation in the right sensory association cortex and increased prefrontal-sensory connectivity. Conclusions: While all interventions improved motor outcomes, MT yielded slightly superior recovery associated with neuroplastic changes. RMT demonstrated high safety, compliance, and potential benefit for patients with severe motor deficits.

Human-Robot Collaboration (HRC) is an emerging paradigm in healthcare that leverages robotic systems to improve patient care, assist medical professionals, and optimize clinical workflows. As healthcare demands increase due to aging populations and resource limitations, HRC offers a promising solution by combining robotic precision with caregivers’ adaptability. This review provides a comprehensive analysis of HRC in healthcare, categorizing its key influencing factors into three components: (1) Healthcare Professional-Oriented, focusing on task allocation, communication, teamwork, and trust; (2) Patient-Centric, emphasizing patient safety, acceptability, interaction, and feedback; and (3) System-Critical, addressing system autonomy, adaptability, integration, and safety in medical environments. The review explores recent advancements in enabling technologies, including sensor developments, immersive interfaces, digital twin modeling, and artificial intelligence (AI), which drive more efficient and adaptive HRC. Despite these innovations, challenges such as ethical concerns, interoperability, and cost-related barriers remain obstacles to widespread implementation. Future research should focus on developing robust ethical frameworks, enhancing safety and reliability, improving interoperability, and fostering patient and caregiver acceptance through interdisciplinary collaboration.

ASO Author Reflections: Synergizing Robotic Precision with Mesoesophageal Anatomy to Refine Esophageal Cancer Surgery.

ABSTRACTIn the advent of Industry 5.0, the harmonious integration of human ingenuity and robotic precision in complex work environments is pivotal for sustainable industrial growth. The six‐axis industrial robot, as an essential part of carrying out cyclic pick‐and‐place tasks in Industry 5.0, usually works in an extremely complex working environment. This intricate working environment makes the six‐axis industrial robot difficult to reach the task points effectively, resulting in a lot of energy consumption. This not only impacts productivity but also leads to excessive energy consumption, which stands at odds with the Industry 5.0 principles of resource conservation. To solve this problem, a novel method to optimize the layout scheme of the six‐axis industrial robot with the goal of minimizing the energy loss is creatively proposed in this paper. First, the reachable workspace and feasible workspace under constraints are mathematically modeled and then obtained. Second, the operability and the minimum singular value are utilized to evaluate the energy loss of the feasible workspace. Third, the whale algorithm is designed and improved to obtain the optimal layout scheme of the six‐axis industrial robot. Finally, a case of the recliner's production line with the six‐axis industrial robot (IRB140; ABB) is provided to validate the effectiveness of the proposed method. The results show that after optimization, the optimal layout scheme has been successfully obtained, and the energy loss has reduced from 0.2917 to 0.2309, a decrease of 20.84%, proving that the proposed method can obtain the optimal layout scheme with lower energy consumption.

The dynamic performance of parallel robots directly determines the machining accuracy in large optical mirror processing (LOMP). However, limitations in traditional dynamic modeling methods hinder their application in real-time control, constraining further improvements in robotic precision. This paper aims to establish a high-precision and practical dynamic model that considers joint friction for parallel robots used in LOMP, and to design an efficient real-time friction compensation control strategy to effectively enhance trajectory tracking and repetitive positioning accuracy. The novelty of this work lies in proposing a dynamic modeling approach that integrates the static mechanics-based “Disassembly Method” with a “Coulomb + Viscous” friction model. First, static analysis of the mechanism is conducted using the “Disassembly Method” to accurately compute the joint constraint reactions in any pose, providing critical input for friction calculation. Subsequently, a complete dynamic model incorporating friction in joints such as Hooke joints, composite spherical hinges, and ball screws is developed based on the Newton–Euler formulation. This method overcomes the shortcomings of traditional approaches in solving joint reactions and managing model complexity. Numerical simulations demonstrate that, compared to conventional friction-neglected models, the proposed model reveals a maximum increase of approximately 350 N in driving chain joint reaction forces and significant peaks in driving forces at motion reversal instants (e.g., 0.28 s, 0.45 s), quantitatively proving that neglecting friction severely underestimates the actual system loads. Experimental validation shows that the feedforward PD friction compensator designed based on this model reduces the rotational tracking errors of the moving platform around the X- and Y-axis from 0.295° and 0.286° to 0.134° and 0.128°, respectively, achieving an error reduction of about 55% and effectively improving motion control accuracy. This study provides a reliable dynamic modeling foundation and an effective real-time control compensation solution to address force output errors and trajectory deviations caused by joint friction in high-precision LOMP.

With the increasing demand for customized manufacturing, human-robot collaborative (HRC) systems combine human adaptability with robotic precision, offering a promising solution for flexible production. Unfortunately, real-time scheduling remains a significant challenge due to high demand variability, frequent disruptions, and complex task allocation. To address these issues, we propose an evolutionary scheduling framework utilizing a local large language model (LLM). This framework enhances domain-specific understanding by supervising the fine-tuning of the LLM on scheduling data. Additionally, we introduce a population self-evolution mechanism that incorporates individual co-evolution, self-evolution, and collective evolution to improve the generation of heuristic dispatching rules (HDRs). By leveraging the local LLM, our approach generates dynamic HDRs with lower computational overhead, facilitating effective task allocation and sequencing in HRC scenarios while ensuring data privacy. Validated across 54 real-world HRC scenarios, our method achieves a 21.52% average makespan reduction, compared to baseline methods, demonstrating its potential for flexible manufacturing systems.

This paper introduces a novel robotic endoscope equipped with an optical microphone for non-destructive testing (NDT) of structural components using ultrasound guided waves. Conventional NDT often requires disassembling components, a labor-intensive and time-consuming process, especially for defects in carbon fiber reinforced polymers that are not visible externally. To address these challenges, a concept from minimally invasive medicine is transferred and a robotic prototype is developed that integrates endoscopic mobility, robotic precision and ultrasonic inspection capabilities. A test stand demonstrates the robot’s ability to navigate confined areas and perform internal ultrasonic measurements. By scanning the surface of components, the system generates full wavefield images and post-processing reveals alterations in the structure, offering insights into structural damage that traditional visual inspections may not detect. This technology is particularly promising for applications like the inspection of hydrogen storage tanks, where conventional methods are limited.

Cemented fixation remains the standard in total knee arthroplasty (TKA), but cementless techniques are gaining popularity, particularly in younger, more active patients. Robotic assistance may improve the accuracy of cementless implantation and promote favorable outcomes. A retrospective review was conducted of 130 cruciate-retaining primary TKAs performed using the ROSA® robotic-assisted system between October 2021 and September 2023 by a single high-volume surgeon. Patients received either a cementless Persona Trabecular Metal® (n = 80) or cemented Persona® (n = 50) prosthesis. Patient demographics, perioperative data, complications, and revisions were recorded. Patient-reported outcome measures (PROMs) WOMAC, Oxford Knee Score, Knee Society Score, and Forgotten Joint Score-12 were collected preoperatively and at minimum one-year follow-up. Patients in the cementless group were younger (p < 0.001) and more frequently men (p = 0.003). Both groups showed significant improvement in all PROMs from baseline (p < 0.001), with no statistically significant differences in final PROMs between groups. One revision occurred in the cemented group (2.0%) due to stiffness and pain; two manipulations under anesthesia (MUA) were required in the cementless group (2.5%). No differences were observed in operative time or hospital length of stay. At short-term follow-up, cementless and cemented robotic-assisted TKAs demonstrated equivalent improvements in PROMs and survivorship. Cementless implants may represent a viable option in appropriately selected patients, particularly younger individuals, when combined with robotic precision. Long-term data are needed to confirm durability.

This study presents a safety-aware shared control strategy that combines proximity sensing and force guidance to achieve precise and stable teleoperation under dynamic interference. Based on the sensing information of the proximity sensor, a safety-aware controller is designed to enable the manipulator to anticipate and respond to potential collisions in its surroundings while ensuring precise task execution. By incorporating a prior-to-contact reaction strategy into the safety-aware controller, the manipulator can switch to a compliant mode, safely and effectively reacting to inevitable collisions, thereby mitigating the collision impact. Additionally, a force-constrained controller is integrated into the shared control strategy to enable stable movement by providing the operator with force guidance through haptic devices. The effectiveness and feasibility of this strategy are validated through experiments, which involve circular motion tracking and needle threading tasks under dynamic random interference. Compared to conventional teleoperation approaches that lack an active safety strategy or force guidance, the proposed strategy outperforms in reducing contact force by up to 78.81% while improving target tracking accuracy by up to 66.91%. The proposed strategy also enhances the stability of task execution under dynamic interference.

ABSTRACT The incorporation of AI in conjunction with secure 5G MIMO networks enhances the precision of consumer and industrial robotics. Ultra‐reliable, low‐latency communication paired with autonomous control enables faster, safer, and more accurate action execution in dynamic environments. However, contemporary robotic communication systems face challenges such as being highly susceptible to signal interference, network delays, cyber‐attacks, and lack of adaptive capability. These obstacles particularly hinder remote control teleoperation and robotic efficiency in conditions which are highly volatile or constantly changing. The framework proposed, AI‐Driven Secure 5G MIMO for Robotic Precision (AI‐5G‐MIMO‐RP), uses AI adaptive signal processing to manage assistive cyber defense systems and strong 5G MIMO communications to overcome such challenges. MIMO technology not only increases data transmission speed, but also enhances dependability, while machine learning helps optimize data routing within the signals. AI‐fortified cyber defenses detect and mitigate real‐time and pre‐emptive breaches, ensuring system communications cannot be tampered with. This approach supports application areas with smart precision like manufacturing, robotics for healthcare, facilitating automation in remote assistance, and serving in automated logistics. This technology enables dependably safe control and low‐latency communication, guaranteeing accurate robot operation in complex tasks without human oversight. AI‐5G‐MIMO‐RP creates a new standard in precision robotics control, network resilience, and operational efficiency. This technology reduces communication delays, increases network flexibility, and enhances system reliability, making industrial and service settings safer and more efficient than previous systems.

Abstract Augmented reality (AR)-enabled human–robot collaboration (HRC) is emerging as a critical paradigm in architectural design and fabrication, particularly for supporting real-time interaction, creative agency, and situated decision-making. As collaborative robots (cobots) become more integrated into exploratory design workflows, AR offers a means to bridge the gap between robotic precision and human intuition. This paper investigates how AR interfaces can facilitate adaptive, embodied collaboration between designers and cobots in spatially unconstrained, exploratory assembly tasks. We developed and evaluated an AR-enabled HRC system across two user studies involving architectural designers. The system allows users to preview, modify, and execute cobotic actions within a shared workspace, incorporating dynamic visual feedback and real-time spatial tracking. Drawing on principles of situated cognition and interactive fabrication, we analyse how AR supports spatial awareness, enhances user agency, and enables intuitive, adaptive interactions. The findings reveal that AR interfaces contribute to HRC through three interconnected themes: (1) improving predictive coordination by externalising cobot intentions and constraints, (2) reinforcing user agency via real-time decision-making tools, and (3) scaffolding situated learning through adaptive visual feedback. We conclude by outlining three key future directions: expanding the spatial and structural complexity of AR-HRC systems, developing more nuanced models of user-cobot interaction in design contexts, and integrating real-time structural feedback to inform user decision-making.

Background. Multi-locus TMS (mTMS) enables precise electronic control of brain stimulation targeting, eliminating the need for physical coil movement. However, with a small number of coils, the stimulation area is constrained, and manual handling of the coil array is cumbersome. Combining electronic mTMS targeting with robotics enables automated, user-independent, and precise brain stimulation protocols.Objective. To characterize an open-source electronic-robotic mTMS platform for rapid and accurate brain stimulation targeting.Methods. We developed an automated robotic mTMS positioning platform. We used a 5-coil mTMS device coupled to a collaborative robot. The stimulation targeting accuracy of the system was quantified with a TMS characterizer that measures the TMS-induced electric field (E-field) on a model of a spherical cortex. The inducedE-field distortion generated by robot coupling was evaluated for each coil. We compared the repositioning accuracy of robotic-electronic system to the conventional manual positioning.Results. Our collaborative-robot-based system offers submillimeter precision and autonomy in positioning mTMS coil sets. The electronic-robotic mTMS platform was approximately 1.8 mm and 1.0° more accurate than the conventional manual positioning. Integrating robotics and mTMS automates brain stimulation procedures, resulting in minimal reliance on user expertise and subjective analysis.Conclusion. Our open-source platform combining rapid mTMS targeting with robotic precision enhances the safety and reproducibility of TMS, enabling more efficient and reliable outcomes than previous techniques.

The latest development of artificial intelligence has recast perioperative medicine, especially anesthesia. This review combined the knowledge gained from various studies of artificial intelligence applications in the development of models on risk stratification, event prediction, and intensive care in the perioperative context. Further, external validation of these models was performed to prove their reliability. Integration of electronic health records will offer real-time support for anesthesiologists to make better decisions and predict complications. This review has been performed with a systematic search through databases such as PubMed, Journal of Anesthesia &amp; Analgesia, Open Journal of Anesthesiology, MDPI Open Access Journal, National Library of Medicine, ResearchGate, and Korean Journal of Anesthesiology, by using appropriate keywords relevant to “AI in Anesthesiology.” Google Scholar was the primary tool. Inclusion criteria considered relevance, peer-reviewed status, and publication credibility. Data synthesis focused on current AI applications, challenges, future perspectives, and case studies. Quality assessment ensured reliability, and the iterative process allowed for continuous updates. Thorough documentation and citation practices maintained transparency. This approach aims to offer a concise, comprehensive analysis of AI’s role in anesthesiology’s perioperative landscape. The narrative review deals with the transformational role of AI in clinical anesthesia, ranging from machine learning applications to closed loops and robotic precision. Against all the enormous potential, a few challenges are discussed: ethical ones and those related to technology adoption. This changing role of the anesthesiologist in expanded perioperative care is powered by artificial intelligence for safer practices. This scoping review examines emerging uses of artificial intelligence in anesthesiology and reviews the impact on clinical care. From perioperative support and outpatient pain management in the case of anesthesiologists to the technology’s limitations, which very well place the onus back on the clinicians with respect to developing artificial intelligence itself – the implications all bear witness that artificial intelligence stands forth as the pivotal force upon which progresses in perioperative practices revolve.