Background: Flatfoot deformity is associated with altered lower extremity biomechanics and functional impairment during gait. However, evidence describing spatio-temporal gait alterations remains heterogeneous and has not been consistently synthesized across studies. Methods: A systematic review was conducted in accordance with PRISMA guidelines. MEDLINE (via PubMed) and Scopus were searched through 24 March 2025 for studies evaluating gait characteristics in individuals with flatfoot or progressive collapsing foot deformity. Studies reporting spatio-temporal parameters in both flatfoot and healthy control cohorts were included in quantitative synthesis. Random-effects meta-analyses were performed to evaluate gait velocity, stance duration, stride length, and cadence. Results: Fifteen studies met inclusion criteria, of which five provided sufficient data for meta-analysis. Compared with healthy controls, individuals with flatfoot demonstrated longer stance duration and shorter stride length. No differences were observed in gait velocity or cadence. Substantial heterogeneity was present across all pooled outcomes (I² > 80%), reflecting variability in study populations, disease characteristics, and gait analysis methodologies. Conclusions: Flatfoot is associated with consistent spatio-temporal gait adaptations characterized by longer stance duration and reduced stride length. Despite heterogeneity among included studies, these findings suggest consistent spatio-temporal gait adaptations that may serve as clinically relevant markers of altered gait mechanics and functional impairment. Further studies with standardized protocols are needed to refine the role of gait analysis in the assessment and management of flatfoot.

Background: Metallic orthopedic implants face persistent clinical challenges that have proved resistant to incremental conventional development. Machine learning and artificial intelligence offer a complementary paradigm for navigating the high-dimensional design spaces governing implant performance, yet the literature remains fragmented across disciplinary silos with no comprehensive synthesis spanning the full development pipeline. Methods: A structured database search of PubMed/MEDLINE, Embase, and Cochrane (executed May 2026), supplemented by hand-searching of reference lists, identified 33 primary studies organized across five sequential domains: alloy composition discovery, additive manufacturing process–property optimization, lattice and porous structure design, surface engineering and coatings, and corrosion and wear prediction. Results: Across all five domains, machine learning approaches, including random forests, convolutional neural networks, Bayesian optimization, generative adversarial networks, physics-informed neural networks, and autonomous multi-agent platforms, have accelerated property prediction and design space exploration beyond experimental or simulation-based methods. Shared barriers to translation include small, heterogeneous datasets, reliance on internal rather than external validation, limited interpretability, and the absence of regulatory frameworks for AI-assisted device design. Representative performance included modulus predictions within ~4 GPa of first-principles values, ML-designed alloys reaching ~42.7 GPa (versus 103–120 GPa for Ti-6Al-4V), property prediction R² often above 0.90 (up to 0.96–0.9991), 98.3% corrosion severity classification accuracy, and acceleration from a roughly fivefold reduction in finite element simulations to surrogates compressing days into minutes. Conclusions: Addressing these limitations will require open standardized databases linking materials parameters to registry-level clinical outcomes, prospective clinical validation studies, and coordinated engagement between researchers, industry, and regulatory agencies.

Diabetes mellitus (DM) is highly prevalent among patients undergoing foot and ankle surgery and is associated with substantially increased perioperative and postoperative risk. This narrative review synthesizes the current literature on optimization of DM patients undergoing foot and ankle surgery. Complications of chronic hyperglycemia, including neuropathy and peripheral vascular disease, make the foot and ankle particularly vulnerable to ulceration, infection, and deformity, contributing to high rates of both operations and postoperative complications such as surgical site infection and readmission. Glycemic control and obesity are modifiable predictors of surgical outcomes and represent key targets for preoperative optimization. Lifestyle modification and pharmacologic therapy play central roles in DM optimization. Traditional agents such as metformin, sulfonylureas, thiazolidinediones, and dipeptidyl peptidase-4 inhibitors remain foundational therapies, while newer therapies such as sodium–glucose cotransporter-2 inhibitors (SGLT2is) and glucagon-like peptide-1 (GLP-1) agonists offer meaningful improvements to glycemic control and weight loss. Pharmacologic regimens must be individualized, and many agents require careful perioperative management. Despite advances in medical therapy, high-quality evidence specific to foot and ankle surgery remains limited. Future research should focus on developing procedure- and agent-specific guidelines to reduce the substantial clinical and economic burden of DM in foot and ankle surgical patients.