Despite curative-intent treatment, recurrence risk in resectable non-small cell lung cancer (NSCLC) remains difficult to define, and postoperative management still depends on imperfect clinicopathologic assessment. Circulating tumor DNA (ctDNA)-based minimal residual disease (MRD) has emerged as a promising adjunct for postoperative risk stratification, early relapse detection, and perioperative response assessment. Its clinical translation, however, is still limited by low sensitivity in low-shedding diseases, imperfect specificity from biological background signals, interpretive uncertainty, and a lack of assay standardization. In this context, artificial intelligence (AI) is relevant not as a generic add-on, but as a potential means to improve weak-signal detection, refine variant-origin assignment, and translate serial ctDNA measurements into clinically interpretable risk estimates. This review considers the current clinical role of ctDNA-MRD in perioperative NSCLC, the barriers to routine implementation, and the extent to which AI may help make molecular monitoring more reliable and clinically useful.

Adolescent idiopathic scoliosis (AIS) is the most common three-dimensional spinal deformity among adolescents, characterized by a coronal curvature of > 10°, often accompanied by vertebral rotation and abnormal sagittal alignment, which can lead to severe cardiopulmonary and neurological dysfunction. For moderate-to-severe or progressive AIS (e.g., Cobb angle ≥ 45°), surgical intervention is the primary treatment modality. However, traditional surgical planning relies heavily on surgeons’ clinical experience and is subject to substantial interobserver variability, making it difficult to fully meet the needs of personalized precision medicine. In recent years, advances in artificial intelligence (AI), particularly in machine learning and deep learning, have provided new methodological tools for precise surgical planning in AIS. This article reviews the main applications of AI in AIS surgical planning, including AI-assisted assessment and automated measurement of preoperative spinal deformities; personalized decision-making for surgical approaches and fusion levels; personalized biomechanical and finite element analysis (FEA)-based optimization of internal fixation devices; prediction of postoperative outcomes; and recent advances in 3D surgical simulation and navigation. Furthermore, this review examines the current limitations of translating AI models into clinical practice, such as data heterogeneity, the “black-box” nature of algorithms, and ethical and regulatory issues. It also discusses future directions, including multicenter federated learning and human-in-the-loop collaborative decision-making, and aims to provide a systematic overview to support intelligent and precise surgical treatment of AIS.

Degenerative, traumatic, deformity-related, and neoplastic spinal disorders place a growing burden on patients and health systems. Artificial intelligence (AI) has shown value in selected spine-care tasks, including imaging analysis, surgical-planning support, navigation assistance, risk prediction, rehabilitation monitoring, and early translational research. Most applications, however, remain task-specific tools rather than integrated clinical systems. This review therefore follows the routine spine-care pathway and focuses on clinician-supervised multimodal AI orchestration systems. In such systems, a DeepSeek-style large language model would be only one component. The broader clinical orchestration system would also require data governance, validated specialist modules, retrieval, uncertainty estimation, safety filters, audit trails, and clinician oversight. We review evidence from admission and imaging assessment through preoperative planning, intraoperative support, postoperative monitoring, and rehabilitation follow-up. We distinguish direct spine-specific evidence from indirect technical analogies and future hypotheses. Multi-omics and drug-development studies are considered only as an outer-loop translational layer for mechanism generation, endotype discovery, biomarker development, and trial enrichment. Current evidence supports selected diagnostic, prognostic, rehabilitation-monitoring, and workflow-assistance tasks, but not a safe, end-to-end autonomous platform for spinal surgery. Near-term translation should prioritize external validation, prospective silent testing, calibration, evidence traceability, post-deployment surveillance, and explicit clinician control.

With increasing detection and longer survival in localized prostate cancer (PCa), treatment goals increasingly emphasize preserving urinary continence and sexual function without compromising oncological control. This review synthesizes evidence on function-preserving strategies, focusing on minimally invasive radical prostatectomy, focal ablation, and artificial intelligence (AI)-assisted surgery. Laparoscopic and robot-assisted radical prostatectomy remain the principal radical approaches; multiport robot-assisted surgery has the most mature evidence for balancing cancer control and functional recovery. Relevant refinements include nerve-sparing, fluorescence imaging, three-dimensional reconstruction, and AI-assisted risk stratification, planning, navigation, and outcome prediction. In carefully selected patients with low- or intermediate-risk disease, focal therapies may reduce treatment burden and preserve function. Focal therapy is not yet a standard option for localized prostate cancer, but it may be discussed during shared decision-making with carefully selected, fully informed patients. Some focal modalities are already used in selected clinical settings, including outside formal trials where locally permitted, although implementation varies across health-care systems. Counseling should address limitations in long-term evidence, the need for structured surveillance, and possible repeat or salvage treatment. Heterogeneous selection, endpoints, and follow-up, together with a lack of high-quality head-to-head comparisons, preclude reliable ranking of focal modalities; sexual recovery and long-term oncological durability also vary. Direct evidence that AI improves nerve preservation, continence, erectile function, or margin control remains insufficient for routine adoption. Optimal function preservation requires risk-adapted selection, technical expertise, multidisciplinary decision-making, and rigorous follow-up. Future studies should prospectively validate AI tools and standardize imaging, outcomes, and clinically meaningful endpoints.

Background: Healthcare simulation is evolving rapidly, yet its role in surgical education remains uneven. During the 9th NASCE (Network of Accredited Clinical Skills Centers of Europe) conference at Orsi Academy (Ghent, Belgium), SESAM (Society in Europe for Simulation Applied to Medicine), DSSH (Dutch Society for Simulation in Healthcare), and NASCE convened a multidisciplinary roundtable, “Healthcare Simulation 2050: Building a Better Future Together”, to explore future directions, challenges, and opportunities in surgical simulation.

Methods: Conference faculty submitted reflections in advance to inform the discussion. The session was audio-recorded (with consent), transcribed verbatim, and analyzed by two reviewers using rapid qualitative thematic analysis with inductive coding. Themes were derived from both pre-meeting inputs and live discussions, aiming to synthesize perspectives rather than reach formal consensus.

Results: Twenty-four multidisciplinary participants contributed, including surgeons, trainees, engineers, and industry and society representatives. Many participants expressed the view that simulation should precede clinical exposure, ideally through proficiency-based progression, to enhance patient safety and standardize training. Participants identified inequities in access, limited curricular integration, and insufficient validation linking simulation to clinical outcomes. Artificial intelligence (AI)-driven coaching, adaptive learning, and global collaboration were viewed as promising but requiring stronger evidence. Human factors, mentorship, empathy, and equity, were emphasized as essential for sustainable, patient-centered implementation.

Conclusions: The discussion highlights a shared vision for surgical simulation: balancing innovation with inclusivity, and technology with human mentorship. While AI and personalized learning are expected to transform training within the next decade, simulation must remain grounded in patient safety, validation, and equitable access. These perspectives reflect emerging expert opinions from a structured multidisciplinary discussion, and do not constitute a formally validated guideline or consensus statement.