Nicholas Raison

Nonoperative tasks in the operating room, such as instrument handling, sterile packaging, and logistics, are demanding yet have received less robotics attention than autonomous surgery. This review, the first to synthesize this literature, maps the perception methods, autonomy levels, methodological quality, and clinical readiness of vision‐based robotic assistants for scrub nurse, circulating nurse, and OR logistics roles. Searches of PubMed, IEEE Xplore, Scopus, ACM Digital Library, Web of Science, and Google Scholar returned 465 records; 20 publications describing 11 robotic system families met inclusion criteria. Most evidence, 17 publications across eight system families, concerns scrub nurse assistance; circulating nurse support, OR logistics, and dental assistance are each represented by one system. Vision methods have moved from classical image processing and predefined gestures toward deep learning, multimodal perception, and vision–language–action models, but autonomy has not kept pace: one system family reaches LoA 1, seven LoA 2, and three LoA 3, with Technology Readiness Levels spanning TRL 4 to TRL 6. Reported success rates are often high but rarely comparable and mostly generated in laboratory or simulated settings. Closing that gap requires stronger evidence on safety, sterility, failure recovery, workflow integration, execution speed, reproducibility, and, notably absent, manipulation of deformable sterile materials.

Objective: To develop and evaluate an artificial intelligence (AI)‐assisted, multimedia journal club model that improves accessibility by transforming scientific articles into on‐demand educational formats distributed through digital platforms. Subjects and Methods: This prospective single‐centre educational evaluation was conducted at King's College Hospital (London). Six AI‐enhanced journal club sessions were implemented between June and September 2025. Peer‐reviewed urology articles were processed using AI‐assisted tools to generate podcasts, slide presentations, short‐form video summaries, question‐and‐answer materials, and visual infographics. Content was distributed through clinician‐facing platforms including YouTube, Spotify, and social media. Two surveys evaluated educational impact: a format‐specific survey completed by 48 participants and a second survey assessing overall perceptions of the AI‐enhanced journal club completed by 19 participants. Results: The AI‐generated podcast was the most preferred format (62.5%), followed by video summaries (16.7%) and slide presentations (8.3%). Participants reported greater engagement compared with traditional journal clubs, with 54.2% selecting the response option ‘much more engaged’. Format‐specific ratings were consistently high, particularly for podcasts (mean [standard deviation, SD] score for engagement 4.6 [0.6] and for clarity 4.8 [0.5]). In the second survey, overall ratings of the AI‐enhanced journal club were high across all domains, including engagement (mean [SD] score 4.8 [0.4]), clarity (mean [SD] score 4.8 [0.5]), time efficiency (mean [SD] score 4.7 [0.6]), and overall preference (mean [SD] score 4.9 [0.3]). Conclusion: An AI‐assisted, multimodal journal club integrating podcasts, slides, and digital dissemination was associated with improved participant‐reported engagement and accessibility. This scalable model supports asynchronous learning and may complement traditional journal club formats in postgraduate medical education.

Objective: To conduct a systematic review and meta‐analysis to indirectly compare the diagnostic performance of contrast‐enhanced ultrasonography (CEUS) and magnetic resonance imaging (MRI) in the evaluation of indeterminate testicular lesions and to explore their potential roles in guiding clinical decision‐making. Methods: PubMed, Web of Science, and Scopus were searched in August 2025. Eligible studies were prospective or retrospective cohorts evaluating CEUS or MRI in small, impalpable, or incidentally detected testicular lesions, with histopathology or clinical/radiological follow‐up as reference standard. Two reviewers independently performed selection, data extraction, and Quality Assessment of Diagnostic Accuracy Studies‐2 tool assessment. Random‐effects meta‐analyses and meta‐regression were conducted in R (R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria). This review was registered in the Prospective Register of Systematic Reviews (CRD420251113920). Results: A total of 12 studies including 912 patients with 920 lesions (279 malignant, 641 benign) were analysed. CEUS showed a sensitivity of 90% (95% confidence interval [CI] 80–95%) and specificity of 74% (95% CI 47–91%), while MRI achieved a sensitivity of 94% (95% CI 88–97%) and specificity of 84% (95% CI 72–91%). CEUS provided a higher negative predictive value (NPV; 96% vs 88%), whereas MRI demonstrated superior positive predictive value (PPV; 83% vs 65%) and accuracy (91% vs 80%). Differences in PPV and accuracy significantly favoured MRI ( P = 0.025 and P = 0.031). Limitations included protocol heterogeneity, operator dependence, and small sample sizes. Conclusion: Both CEUS and MRI demonstrated a high sensitivity and NPV, supporting their role in excluding malignancy. CEUS, with accessibility, low cost, and particularly high NPV, may serve as the first‐line adjunct, while MRI is best reserved for equivocal or higher‐risk cases. No funding was received for this study.

With more robotic platforms approved for clinical use, interest in assessing skill transferability has increased. Skill transfer has yet to be studied amongst trainees in early stages of robotic training. This systematic review evaluated skill transferability of robotic platforms amongst trainees in simulated settings. The systematic review was conducted according to PRISMA guidelines. Five databases were searched from inception until 20 January 2025. Inclusion criteria had studies using simulation-based modalities, more than one robotic platform, and objective metrics or global rating scales for skills assessment. Search identified 609 unique studies. Five studies were included. Platforms analysed were Da Vinci X, Si and single-port system, Hugo RAS and CMR Versius. Consistent performances were seen across platforms amongst novices and experts, whilst intermediates exhibited reduced scores (GEARS − 2.9). Prior robotic surgical experience was the main driver for better baseline performance and skills progression. Skill transferability was demonstrated in cross-platform simulated training amongst experts and novices, indicating that core robotic surgical skills can be applied across systems. Less evidence of skill transfer in intermediate-level participants suggests that those still consolidating their technical proficiency may be more sensitive to platform-specific differences. Console design variations appeared to influence transferability. Incorporating cross-platform training could enhance trainees’ understanding of the fundamental principles of robotic surgery, equipping them with more adaptable skillsets suited to various platforms. This has important implications for surgical training, as during the trainees’ learning phase, they should be aware of the potential decline in technical performance when transitioning between platforms.

Background: Robot‐assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade‐off between performing a wider resection in order to reduce the risk of positive surgical margins (PSMs) and performing minimal resection of the nerve bundles that determine functional outcomes, such as incontinence and potency, which affect patients’ quality of life. In order to achieve favourable outcomes, a precise understanding of the three‐dimensional (3D) anatomy of the prostate, nerve bundles and tumour lesion is needed. Study Design: This is the protocol for a single‐centre feasibility study including a prospective two‐arm interventional group (a 3D virtual and a 3D printed prostate model), and a prospective control group. Endpoints: The primary endpoint will be PSM status and the secondary endpoint will be functional outcomes, including incontinence and sexual function. Patients and Methods: The study will consist of a total of 270 patients: 54 patients will be included in each of the interventional groups (3D virtual, 3D printed models), 54 in the retrospective control group and 108 in the prospective control group. Automated segmentation of prostate gland and lesions will be conducted on multiparametric magnetic resonance imaging (mpMRI) using ‘AutoProstate’ and ‘AutoLesion’ deep learning approaches, while manual annotation of the neurovascular bundles, urethra and external sphincter will be conducted on mpMRI by a radiologist. This will result in masks that will be post‐processed to generate 3D printed/virtual models. Patients will be allocated to either interventional arm and the surgeon will be given either a 3D printed or a 3D virtual model at the start of the RARP procedure. At the 6‐week follow‐up, the surgeon will meet with the patient to present PSM status and capture functional outcomes from the patient via questionnaires. We will capture these measures as endpoints for analysis. These questionnaires will be re‐administered at 3, 6 and 12 months postoperatively.

Objective: To compile and evaluate the heritability and inheritance patterns of lower urinary tract symptoms (LUTS) in adult cohorts. Methods: Searches of five databases (PubMed, Embase, APA PsycInfo, Global Health, and OVID Medline) commenced on 6 July 2024, resulting in 736 articles retrieved after deduplication. Studies evaluating heritability patterns, gene frequencies, and familial aggregation of symptoms were included for review. Screening and predefined eligibility criteria produced 34 studies for final review. A descriptive analysis of synthesised data was performed, adhering to the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses guidelines. The Cochrane Risk of Bias in Non‐Randomised Studies of Interventions (ROBINS‐I) tool and the Johanna Briggs Institute checklist were used to evaluate these studies. Results: Ten of the 34 studies (29%) described general LUTS, 14 (41%) described symptoms due to benign prostatic enlargement (BPE), nine (26%) described urinary incontinence (UI; urge UI [UUI], stress UI [SUI] and mixed UI [MUI]), four (12%) described nocturia alone, two (6%) described overactive bladder (OAB), and four (13%) described other specific symptoms (frequency, postvoid residual urine volume). BPE symptoms, UI (MUI and UUI), nocturia alone, and frequency alone were associated with genetic predisposition, whilst OAB and SUI had more modest inheritance. Conclusion: The pathogenetic and pharmacological mechanisms fundamental to LUTS manifestation are highly heterogeneous. Further work is required to evaluate the inheritance patterns of LUTS more extensively.