Introduction: Lumbar radiculopathy, frequently triggered by disc herniation, affects around one in 50 young adults (30-55 ages), leading to significant impacts on quality of life. When conservative treatments fail to alleviate symptoms, surgical intervention such as discectomy is often pursued. Technological advancements have led to various minimally invasive techniques, promising improved outcomes. Our aim was to compare clinical outcomes of different techniques. Methods: We conducted a systematic search in PubMed using "lumbar disc hernia" and the meta-analysis filter. Inclusion criteria comprised meta-analyses focusing on lumbar disc herniation treatments, published in English. A rigorous selection process adhering to PRISMA guidelines ensured high-quality evidence extraction. Results: From 28,171 initial records, 31 meta-analyses involving 87,852 patients were included. Heterogeneity across the literature presented challenges, notably in study populations, surgical techniques, and research designs. Among the techniques evaluated, full-endoscopic lumbar discectomy (FELD) emerged as superior in various perioperative parameters, including Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI), operation time, and complications. Conversely, automated percutaneous discectomy (AUTD) consistently exhibited poorer outcomes, suggesting limited effectiveness compared to other techniques. Conclusion: Despite advancements, the heterogeneity observed in the literature underscores the need for standardized approaches in minimally invasive spinal surgery research. FELD stands out as a preferred technique, offering favorable outcomes across multiple parameters. Conversely, techniques like AUTD raise concerns regarding efficacy. Future research should aim for uniformity in methodologies to facilitate accurate comparisons and guide clinical decision-making.

This article presents a radical reassessment of scientific validation processes, arguing that traditional peer review has become an outdated, inefficient, and ultimately flawed mechanism for ensuring research quality. Modern artificial intelligence systems demonstrate superior capabilities in analyzing methodological rigor, statistical validity, and literature comprehensiveness, while being free from human cognitive biases, professional rivalries, and institutional politics. Through examination of empirical evidence, we demonstrate how AI systems consistently outperform human reviewers in speed, accuracy, and comprehensiveness of research evaluation. The current peer review system, characterized by months-long delays, substantial costs, and demonstrable biases, actively impedes scientific progress. We propose a fully automated AI-driven validation framework that can evaluate research in real-time, identify methodological flaws, verify statistical analyses, and assess significance within the broader scientific context. This transformation would democratize research validation, eliminate publication bottlenecks, and accelerate scientific progress while maintaining higher standards of methodological rigor than currently possible under human review.