Cervical cancer is a major malignancy that threatens women’s health, and early screening is a core strategy for reducing its incidence and mortality. Multimodal fusion artificial intelligence (AI) pathological diagnosis models integrate multidimensional data—including cytological images, colposcopic images, whole-slide histopathological images, clinical data, and molecular testing results—and may enhance the detection sensitivity, grading accuracy, and screening efficiency for early cervical cancer and precancerous lesions. However, traditional cervical cancer screening methods face limitations such as high subjectivity, reliance on single-source information, relatively low efficiency, and insufficient primary care resources. Furthermore, existing reviews mostly focus on single-modal AI models or specific technical aspects, lacking a comprehensive analysis of the full technical framework and clinical translation pathways of multimodal fusion models. This review aims to comprehensively present the development and application of multimodal fusion AI models in pathological diagnosis for early cervical cancer screening. Specifically, it comprehensively details the technical architecture, data modalities, and fusion strategies—including deep learning, attention mechanisms, and cross-modal alignment techniques—that enable the complementary representation of morphological, clinical, and molecular information. Additionally, the review integrates recent advances in clinical applications and evaluates current translational challenges, providing insights into clinical validation pathways to bridge technological innovation and practical healthcare delivery. In conclusion, with further technological refinement and clinical validation, multimodal fusion AI may become a useful tool for improving the precision and efficiency of cervical cancer screening and prevention, and may inform the standardized application and translational research of AI technology in this field.

Cerebrospinal fluid leakage and postoperative tissue adhesion are serious complications following dural injury. Current dural substitutes often lack the functional asymmetry of the native dura mater. This study aimed to develop a hydrophilic/hydrophobic Janus polyvinyl alcohol (PVA) hydrogel membrane with a directional structure and dual functionality for effective dural defect repair.

A PVA hydrogel with an aligned porous architecture was fabricated via directional freezing combined with salt leaching, and thermal annealing was applied to enhance mechanical strength and structural stability. The hydrogel was asymmetrically modified to obtain a Janus membrane. Morphology, mechanical properties, degradation, swelling, wettability, in vitro biocompatibility, and cell migration were evaluated by the NIH-3T3 mouse fibroblast cell line. In vivo biocompatibility was assessed using a rat subcutaneous implantation model, including blank control, Durepair®, frozen-salted PVA, and Janus-PVA groups, with 5 rats in each group. Dural repair efficacy was evaluated in a rat cranial dural defect model, including untreated defect control, frozen-salted-annealed PVA, and Janus-PVA groups, with 15 rats in each group.

The Janus membrane exhibited high tensile strength (8.93 ± 1.46 MPa), slow degradation (1.42% mass loss at 28 days), and low swelling (58.13% water content at 28 days). It displayed distinct bilateral wettability, and effectively blocked fibroblast migration on both sides, acting as a physical barrier against fibroblast-driven adhesion. In the rat dural defect model, the Janus membrane reduced cerebrospinal fluid leakage and brain–dura adhesion compared with the untreated defect and frozen-salted-annealed PVA control groups.

The engineered hydrophilic/hydrophobic Janus PVA hydrogel membrane mimics the functional asymmetry of the native dura mater and may serve as a promising candidate for further evaluation as a dural repair material.

The modern intensive care unit (ICU) inundates clinicians with large volumes of data, leading to cognitive overload and a gap between data availability and actionable insight. While artificial intelligence (AI) promises a solution, its clinical adoption is limited by systemic barriers, including algorithmic bias, a lack of trust, and validation failures. This paper argues that a design philosophy that envisions AI as an autonomous decision-maker, rather than an integrated collaborative tool, has hindered its clinical adoption. We propose an alternative: a collaborative framework designed to augment the intensivist’s expertise by offloading specific cognitive burdens. This framework redefines AI’s purpose as managing data-intensive tasks, illustrated through four collaborative example roles: a synthesizer to create coherent clinical narratives, a sentinel for proactive deterioration surveillance, a simulator to forecast patient responses to interventions, and a stratifier to identify meaningful subphenotypes within complex syndromes. By delegating these computational tasks, this collaborative model frees clinicians to focus on complex synthesis, nuanced judgment, and compassionate communication. Realizing this vision requires a deliberate translational pathway focused on robust data infrastructure, human-centered design, and rigorous validation through prospective clinical trials. Ultimately, the successful integration of AI in critical care depends not on replacing clinicians but on empowering them, creating a more functional ICU in which technology supports the delivery of safer, more precise, and more humane care.

Atrial fibrillation and chronic kidney disease (CKD) frequently coexist, increasing thromboembolic and bleeding risks. This is a narrative review of pathophysiology and clinical evidence for anticoagulation strategies in CKD patients. Direct oral anticoagulants are preferred in CKD stages 1–4. Recent data suggest that the efficacy of apixaban and rivaroxaban is comparable to that of warfarin in end-stage renal disease. In advanced CKD, anticoagulation should be tailored with close monitoring.