Adam Khan

Objective Surgical innovation has exponentially improved over the past three decades. However, efficacy assessment of surgical devices through randomised controlled trials requires significant resources. In silico modelling may provide a solution, utilising computational simulations to develop and improve devices. This study aims to evaluate the current use of in silico modelling for devices used in abdominal and thoracic surgery.Design A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.Data Resources PubMed, MEDLINE and Embase databases from January 2019 to August 2024 were searched.Eligibility criteria for selecting studies Inclusion criteria were defined as studies using mathematical/computational models to investigate invasive devices implanted or incorporated in abdominal and thoracic surgery.Results The search strategy yielded 2968 studies, with 42 included in the final analysis. Core themes identified included proof of concept (n=19), device improvement (n=22) and in silico clinical trials with simulated human patients (n=1). Specialities included: general surgery (n=4); hepato-pancreato-biliary (n=2); vascular (n=5); paediatric (n=9) and cardiothoracic (n=22). Most studies were at IDEAL stage 0 (preclinical) and 19 studies validated in silico results with in vitro/in vivo data.Conclusion In silico modelling can complement preclinical in vitro and in vivo testing for surgical devices. Its applications include simulating rare diseases, modelling diverse patient demographics, and generating early efficacy data for novel or repurposed devices. Standardising methodologies through a core outcome set and credibility framework is essential to reduce inter-study heterogeneity. Global collaboration and clear regulatory guidance are key for its integration into mainstream surgical device research.All data relevant to the study are included in the article or uploaded as supplementary information.

In the shadowy realm of cybercrime, DarkBard has emerged as a significant player, representing a crossover into non-OpenAI territory. As its name suggests, DarkBard is modeled as the dark counterpart to Google's Bard artificial intelligence (AI). This tool surfaced in mid-2023, riding the same wave that brought other malicious AI tools like FraudGPT and WormGPT to prominence. Notably, the cybercriminal seller known as “CanadianKingpin,” who launched FraudGPT, also advertised DarkBard on dark web forums and Telegram channels.

Cyberattackers are using generative AI to draft polished spam, create malicious code and write persuasive phishing lures.

Background Regional anesthesia (RA) is central to multimodal analgesia, yet many supporting randomized controlled trials (RCTs) may lack the rigor needed for evidence-based block selection.Objectives To evaluate the clinical usefulness and transparency of RCTs investigating RA interventions for lower extremity surgeries, using the van ’t Hooft et al framework.Methods Embase/MEDLINE were searched for RCTs (2014–2024) examining RA interventions for lower-extremity surgery. Data were extracted in duplicate using clinical-utility and transparency criteria. Analyses included descriptive statistics, linear regression for temporal trends, Pearson correlation, and multivariable regression to identify predictors of usefulness.Results Among 248 RCTs, patient-centeredness was universal, while feasibility (77.0%) and context placement (77.4%) were common. Pragmatism (2.0%) and value-for-money analyses (0%) were rare. Transparency varied: conflicts of interest were disclosed in 88.7%, pre-registration occurred in 39.9%, public protocols in 10.1%, and raw-data sharing in 1.2%. Transparency improved over time, correlated with clinical-utility scores (r=0.32, p<0.001) and was greater in hospital-funded, self-funded, and higher-impact journals.Limitations Usefulness scoring involves subjective judgment; non-English trials were excluded; the absence of formal risk of bias analysis limits interpretability of trial quality.Discussions/conclusions Although transparency is improving, most trials remain insufficiently pragmatic, lack economic evaluation, and rarely share data, limiting real-world applicability. The results of this study suggest that aligning funder, journal, and collaborative expectations around open science and pragmatic design could accelerate the generation of high-utility evidence for perioperative block selection.

As malicious generative AI tools surge, businesses must bolster defences with strategic security measures to counter evolving AI-enabled cyber threats.

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As AI-driven energy demand collides with nation-state cyber probing and fragmented IT/OT defenses, utilities must move beyond patchwork fixes and adopt a secure-by-design strategy to prevent small intrusions from triggering large-scale outages.

LaGuardia Airport slammed the brakes on incoming flights early Monday after a regional jet struck a ground vehicle during its landing rollout, drawing

Adam Khan—Vice President of Global Security Operations and Office of the CTO for Barracuda—explains what the missing link between AI pilots and production success lies. Organizations are racing to …

New award charts mean higher costs for popular Asia and Europe redemptions