Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of robotic surgery
Progress and perspectives in soft robotics for surgery and rehabilitation
Soft robotic systems have become a promising technology in the biomedical field due to their higher safety, dexterity and adaptability compared to traditional rigid robotic systems. This review focuses on recent advances in soft robotic systems applied to surgical assistance and rehabilitation. In surgery, soft grippers and flexible endoscopes show better adaptability, dexterity, and miniaturization, enabling safer and more precise manipulation of delicate tissues. In rehabilitation, wearable soft devices show great potential to help patients with neurological injuries to regain movement. Key innovations in actuation technology are examined, along with recent advances in multifunctional, self-healing, and environmentally responsive materials. Meanwhile, sensing systems are evolving from unimodal sensing to multimodal fusion, self-perception, and sense-drive integration, enabling robots to sense the body state and external environment with higher accuracy and realize closed-loop control. Finally, this paper points out that soft robots still face key challenges such as material durability, biosafety, and stability during clinical translation. In the future, the focus should be on the construction of systems with self-diagnosis, self-adaptive adjustment and closed-loop control, and promote the efficient landing and personalized application of soft robots from experimental research to real medical scenarios.
21 June 2026
Read appraisal →Neurosurgical review
Modified tubular retractor for deep brain tumour resection: enhanced surgical outcomes in resource-constrained settings
Tubular retractors permit minimally invasive access to deep-seated intracranial tumours; however, limitations in visualisation and manoeuvrability reduce their suitability for lesions ≥ 3 cm, particularly in resource-limited centres. We evaluated a modified, low-cost tubular retractor incorporating enhanced visualisation and ergonomic features, comparing its performance with Leyla spatula-based retraction and traditional syringe-derived tubular systems. A prospective comparative cohort of 105 patients with deep brain tumours ≥ 3 cm was analysed. Patients underwent Leyla retraction (Group 1, n = 38), conventional tubular systems (Group 2, n = 32), or a modified tubular retractor (Group 3, n = 35). Outcomes included intraoperative visualisation, retractor stability, manoeuvrability, extent of resection, complications, and three-month Glasgow Outcome Scores. Owing to non-normal distribution, continuous variables were analysed using non-parametric tests and binary logistic regression. The modified tubular retractor demonstrated greater stability, broader visualisation, and enhanced instrument handling compared with conventional tubular systems. Gross total resection was achieved in 91.4% with the modified device, 78.9% with Leyla-based retraction, and 28.9% with traditional tubular systems. Logistic modelling identified retractor type (OR 2.59, p = 0.035), intraoperative visualisation (OR 2.01, p = 0.036), and histopathology (OR 1.41, p = 0.036) as independent predictors of residual tumour. The modified retractor group exhibited fewer complications, shorter ICU stays, and superior functional outcomes (GOS ≥ 4 in 91%). The modified tubular retractor demonstrated favourable operative handling and outcome trends for deep-seated tumours ≥ 3 cm, with improved visualisation, stability, and ergonomic access compared with conventional systems. However, limitations related to non-randomised design, learning-curve effects, advanced mapping, fluorescence integration, and supramaximal resection warrant cautious interpretation pending multicentre long-term validation.
29 May 2026
Read appraisal →Proceedings of the National Academy of Sciences of the United States of America
Neural reservoir control of a bio-hybrid soft arm
A long-standing engineering problem, the control of soft robots is difficult because of their highly nonlinear, heterogeneous, anisotropic, and distributed nature. Here, bridging engineering and biology, neural reservoirs are employed for the dynamic control of a bio-hybrid model arm made of multiple muscle-tendon groups enveloping an elastic spine. We show how the use of reservoirs facilitates simultaneous control and self-modeling across challenging tasks, outperforming classic neural network approaches. Further, through the use of spiking reservoirs on neuromorphic hardware, energy efficiency gains of up to 75 and 45 times are obtained relative to standard and high-efficiency CPUs, with implications for the on-board control of untethered, small-scale systems.
28 Apr 2026
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