The lymphatic system is essential for maintaining tissue-fluid homeostasis. In conditions of elevated venous pressures (e.g., congestive heart failure or congenital single-ventricle heart disease after Fontan palliation), increased capillary filtration and impaired lymphatic drainage disrupt this balance, predisposing patients to lymphatic overload and edema. The lack of human-scale computational models capturing lymphatic pumping and coupled tissue-fluid exchange limits the development of effective interventions. We developed a human-scale lumped-parameter model that combines multi-lymphangion thoracic duct pumping with a Starling-based tissue-fluid balance model, enabling integrated simulation of central lymphatic function and tissue-fluid homeostasis. The framework was used to evaluate surgical and device-based lymphatic interventions. Model predictions were combined with virtual fitting using MR-derived anatomy from three Fontan patients to demonstrate translational applicability and design an extravascular soft robotic lymphatic assist sleeve, whose performance was characterized in hydraulic benchtop experiments. The model reproduced physiologic thoracic duct flow ( 3.1L/day) and predicted interstitial fluid accumulation under venous hypertension (up to 18 mL/min net accumulation). Surgical (lymphatic decompression) and device-based (extravascular sleeve) interventions increased thoracic duct flow and reduced simulated tissue-fluid accumulation (47
Accurate three-dimensional temperature prediction is important for planning microwave ablation because incomplete heating may leave residual tumor, whereas excessive heating may damage adjacent structures. This study develops a fast and physically interpretable surrogate for predicting the 180-s endpoint temperature field in nonperfused liver tissue during single-antenna microwave ablation under a fixed protocol. GAPPTANet introduces an analytical specific absorption rate (SAR) field as an explicit heat-source prior, applies a Geometry-Aware Adapter to correct applicator-induced near-field approximation errors, and uses a Gated Dual-Branch Decoder to reconstruct global heat diffusion and local high-temperature boundaries. The model was trained and evaluated using 27,050 high-fidelity finite-difference frequency-domain electromagnetic–thermal simulations. Performance was assessed using full-field errors, 50 and 60 ^∘ C isotherm morphology, computational efficiency, ablation studies, and point-wise measurements from a limited ex vivo porcine liver experiment. GAPPTANet achieved a full-field root-mean-square error of 0.6001 ^∘ C, a mean relative error of 0.5904 2.83± 1.82 ^∘C . Explicit heat-source priors combined with geometry-aware correction enable accurate, interpretable, and millisecond-scale 3D temperature prediction, supporting further development of AI-assisted microwave ablation planning.
The convergence of artificial intelligence (AI) and smart hydrogel-based additive manufacturing offers new capabilities for fabricating adaptive structures, though the field remains in early stages of translation. Smart hydrogels, with their stimuli-responsive properties and biocompatibility, present opportunities in tissue engineering, drug delivery, and soft robotics. However, the complexity of their material behavior and the challenges of high-resolution four-dimensional (4D) printing demand advanced computational approaches to optimize design, processing, and performance. This review systematically examines the role of AI, encompassing machine learning (ML), deep learning (DL), and generative models, in addressing these challenges. We critically assess AI-driven strategies for hydrogel formulation, real-time process control, structural optimization, and biological outcome prediction, distinguishing between retrospectively fitted models and prospectively validated implementations. A key contribution of this review is the clear differentiation between AI-assisted 3D printing and genuine 4D printing, where constructs exhibit time-dependent shape change, actuation, or degradation in response to stimuli. We introduce AI applications in specific hydrogel printing domains, provide a comparative framework for distinguishing 3D from 4D systems, and critically evaluate quantitative claims with attention to dataset size, validation approaches, and reproducibility. Case studies highlight where AI enhances printability, resolution, and functional outcomes, while we also identify persistent limitations including data scarcity, model interpretability, and regulatory hurdles. A comparative analysis of AI-assisted versus traditional design-of-experiment strategies reveals that while AI offers advantages in high-dimensional spaces, traditional methods remain more accessible, interpretable, and reproducible for simpler systems. By synthesizing recent advancements with transparent methodology and practical guidance, this study provides a roadmap for future research at the intersection of AI and smart hydrogel additive manufacturing, advocating for interdisciplinary collaboration and standardized reporting to enable next-generation innovations.
Many foundation models that have entered medicine are language models, although foundation models for imaging and other modalities have also emerged. A second class is now emerging from robotics and autonomous driving, trained not to produce text but to predict how an environment evolves under action, and marketed under the designation world foundation model. We argue that medicine should approach this class with a specific form of caution. Medicine already possesses world models in the mechanistic simulators and digital twins built from physiology and physics, and the constraint on those has never been the volume of available data. The constraint is identifiability. A simulator learned from observational clinical records inherits the treatment policies that generated them, and rolling such a model forward under an alternative treatment recovers the practice pattern rather than the underlying physiology. We distinguish the settings where learned world models are likely to succeed from those where they will reproduce confounding at scale, and set out what validation would require.
Traumatic brain injury (TBI) often results from falls, sports impacts, violence, and traffic or industrial-related events. It is principally attributed to inertial loading that produces intracranial deformations, rotations, tissue strains, and subsequent neuropathology. Computational approaches such as finite element models (FEMs) are used to predict brain biomechanics and injury patterns, but their validity depends on experimental, biofidelic datasets of in situ human brain–skull kinematics and regional deformation, which to date, are limited. This study’s objective was to measure the dynamic response of the brain when subjected to variable head impact severities, direction, and impactor compliance. This study used high-speed X-ray cinematography to quantify the dynamic response of discrete anatomical regions in post-mortem human subject (PMHS) heads across systematically varied impact conditions. We derived brain–skull relative displacement and strain time histories suitable for FEM validation and inter-model comparison. Frontal impacts produced consistently larger peak displacements and strains than occipital impacts regardless of impact compliance or gross kinematics. Regional analysis revealed spatially non-uniform deformation, with the greatest displacements and strains observed in superior regions, possibly related to differences in local skull and meningeal geometry in the region relative to the skull base. These results demonstrate the sensitivity of intracranial mechanics to impact direction, site, and local boundary geometry. The datasets generated here provide experimentally grounded targets for validating and improving FEMs. The results also inform our understanding of the evolution of neuropathology in TBI and the development of protective strategies that account for directional and regional vulnerability in patients who sustain TBI.
