Rural healthcare faces persistent challenges, including inadequate infrastructure, workforce shortages, and socioeconomic disparities that hinder access to essential services. This study investigates the transformative potential of artificial intelligence (AI) in addressing these issues in underserved rural areas. We systematically reviewed 109 studies published between 2019 and 2024 from PubMed, Embase, Web of Science, IEEE Xplore, and Scopus. Articles were screened using PRISMA guidelines and Covidence software. A thematic analysis was conducted to identify key patterns and insights regarding AI implementation in rural healthcare delivery. The findings reveal significant promise for AI applications, such as predictive analytics, telemedicine platforms, and automated diagnostic tools, in improving healthcare accessibility, quality, and efficiency. Among these, advanced AI systems, including Multimodal Foundation Models (MFMs) and Large Language Models (LLMs), offer particularly transformative potential. MFMs integrate diverse data sources, such as imaging, clinical records, and bio signals, to support comprehensive decision-making, while LLMs facilitate clinical documentation, patient triage, translation, and virtual assistance. Together, these technologies can revolutionize rural healthcare by augmenting human capacity, reducing diagnostic delays, and democratizing access to expertise. However, barriers remain, including infrastructural limitations, data quality concerns, and ethical considerations. Addressing these challenges requires interdisciplinary collaboration, investment in digital infrastructure, and the development of regulatory frameworks. This review offers actionable recommendations and highlights areas for future research to ensure equitable and sustainable integration of AI in rural healthcare systems.

Security vulnerabilities present in a code that has been written in diverse programming languages are among the most critical yet complicated aspects of source code to detect. Static analysis tools based on rule-based patterns usually do not work well at detecting the context-dependent bugs and lead to high false positive rates. Recent developments in artificial intelligence, specifically the use of transformer-based models like CodeBERT and CodeLlama, provide light to this problem, as they show potential in finding such flaws better. This paper presents the implementations of these models on various datasets of code vulnerability, showing how off-the-shelf models can successfully produce predictive capacity in models through dynamic fine-tuning of the models on vulnerable and safe code fragments. The methodology comprises the gathering of the dataset, normalization of the language, fine-tuning of the model, and incorporation of ensemble learning and explainable AI. Experiments show that a well-trained CodeBERT can be as good as or even better than some existing static analyzers in terms of accuracy greater than 97%. Further study has indicated that although language models can achieve close-to-perfect recall, the precision can decrease. A solution to this is given by hybrid models and validation procedures, which will reduce false positives. According to the results, the AI-based solutions generalize to different programming languages and classes of vulnerability. Nevertheless, robustness, interpretability, and deployment readiness are still being developed. The results illustrate the probabilities that AI will enhance the trustworthiness in the usability and scalability of machine-learning-based detectors of vulnerabilities.

Objective: This study proposes and preliminarily validates a novel "Functional-Energetic Topology Model" to uncover neurodynamic mechanisms of Non-Suicidal Self-Injury (NSSI), using Graph Neural Networks (GNNs) to decode brain network patterns from single-channel EEG in real-world settings.Methods: EEG data were collected over ~1 month from three adolescents with NSSI using a smartphone app and a portable Fp1 EEG headband during impulsive and non-impulsive states. A theory-driven GNN with seven functional nodes was built. Performance was evaluated via intra-subject (80/20 split) and leave-one-subject-out cross-validation (LOSOCV). GNNExplainer was used for interpretability.Results: The model achieved high intra-subject accuracy (>85%) and significantly above-chance cross-subject performance (approximately73.7%). Explainability analysis revealed a key finding: during NSSI states, a critical feedback loop regulating somatic sensation exhibits dysfunction and directional reversal. Specifically, the brain loses its ability to self-correct via negative bodily feedback, and the regulatory mechanism enters an "ineffective idling" state.Conclusion: This work demonstrates the feasibility of applying theory-guided GNNs to sparse, single-channel EEG for decoding complex mental states. The identified "feedback loop reversal" offers a novel, dynamic, and computable model of NSSI mechanisms, paving the way for objective biomarkers and next-generation Digital Therapeutics (DTx).

As large language models (LLMs) grow more capable, concerns about their safe deployment have also grown. Although alignment mechanisms have been introduced to deter misuse, they remain vulnerable to carefully designed adversarial prompts. In this work, we present a scalable attack strategy: intent-hiding adversarial prompting, which conceals malicious intent through the composition of skills. We develop a game-theoretic framework to model the interaction between such attacks and defense systems that apply both prompt and response filtering. Our analysis identifies equilibrium points and reveals structural advantages for the attacker. To counter these threats, we propose and analyze a defense mechanism tailored to intent-hiding attacks. Empirically, we validate the attack's effectiveness on multiple real-world LLMs across a range of malicious behaviors, demonstrating clear advantages over existing adversarial prompting techniques.

In this proof, we use the notion of weighted exchangeability as defined in Section 3.2 of [27]. A.2 Proof of Proposition 4.2 The following proof is an adaptation of [14, Proposition 1] to our setting. To get from (32) to (33), we use Assumption 2 and Markov's inequality. B.1 Further comments on the differences between [14] and COPP In this subsection, we elaborate on the differences between our work and [14]. As mentioned in in the main text, given that we are integrating out the action in Eq. 7, we are essentially able to use the full dataset when constructing the CP intervals.

We believe this work brings fundamental insights into the related fields.

Recently, Sur and Candès [2019] showed that these issues can be corrected by applying a new approximation of the MLE's sampling distribution in this high-dimensional regime.

Our proposed algorithm can also be used to minimize the sum of ranked range loss, which also lacks efficient solvers.

Our solution is application agnostic and relies only on the data collected for diagnosis. For the evaluation, we compare the proposed solution with a modified version of the PC algorithm and the state-of-the-art for root cause analysis.

AUPR-Out is similar to AUPR-In. All experiments were repeated five times with the random seed setting from 0 to 4. For SimCLR, We Other parameters are the same as the default settings. The learning rate and weight decay are set to 0.0003 and A.3 Stability of Training We track the relationship between loss and performance on the validation set for U The results are shown in Fig.( 1). Therefore, UOOD's training is not stable Relatively, their detection performance on unknown OOD samples is improved. Table 1: Performance of different methods on Known / Unknown OOD data set evaluated by AUROC.