With the development of the systems of artificial intelligence as non-communicative and non-cognitive assistive tools, to autonomous agents independent of human intention rendering decisions and carrying out tasks, society experiences the challenges that it never experienced before in the issues of responsibility of their creation and implementation. The swift decentralization of agentic AI systems into essential sectors such as healthcare, financial systems, transportation, and defense has lagged behind the formulation of sufficient governance strategies, among other aspects of excessively high risks and safety controls and ethical accountability. These systems are autonomous and have the ability to exhibit new emergent behavior and multi-step thinking, which have prompted new safety issues not properly handled by the traditional AI governance frameworks. This literature review is designed in accordance with the PRISMA approach to analyze the existing literature, theories, and new tendencies in the responsible agentic AI regulation in a systematic manner. The discussion has identified that there are severe issues to define effective oversight systems of autonomous AI agents such as checking goal congruency, tracking on emergent capabilities, engineering against negative instrumental actions, and accountability in multi-agent systems. The prevailing systems of governance are characterized by disintegration in the various regulatory systems and there is little coordination between the technical safety actions and the policy actions. The findings indicate that there is a drastic response to adaptive governance constructs able to adjust to the quickly changing AI functions, combination of technical safety protocols with ethics, and formation of international coordination systems.

Early diagnosis of mental vulnerability is still a problem since most clinical evaluations are based on the subjective aspects of evaluation like self-report and not on the objective physiological indications. The idea of psychological resilience which is a fundamental protective factor against stress related disorders has been connected to the autonomic regulation and daily behavioral patterns, but the objective method of calculating on such a scale is yet to be established. This paper presents a multimodal graph neural network, which combines the wearable derived physiological signals to forecast trait level resilience and stress as well as state level. The ongoing heart activity, electrodermal activity, and motion data of adult participants were gathered and matched with resilient score proven validated scores. Participant specific graphs were created whereby physiological modalities were the nodes and the inter signal dependencies were the edges. Statistical analysis showed that there were significant physiological disparities that were related to resilience. High resilience group members had much better values of root mean square of successive differences and physical activity per day (mean difference on 1000 extra steps per day, p = 0.003). The proposed graph neural network (GNN) performed well in classification tasks in terms of area under the receiver operating characteristic curve (0.80 +- 0.02) to differentiate high and low resilience, which was significantly better than that of logistic regression (0.60), random forest (0.65), and long short-term memory models (0.72), with the difference being supported by DeLong test (p < 0.01). Graph based learning also offers added benefits of discrimination and physiologically explainable output and thus can facilitate its future as a scalable and objective digital mental health monitoring and early risk stratification tool.

This book is both atlas and meditation, manual and elegy. It aims to teach and to unsettle, to illuminate and to provoke. In the end, pediatric hematopathology is not about diseases alone. It is about the children who bear them, the families who stay beside them, and the clinicians who walk the narrow path between science and sorrow. In every child’s marrow there is not just hematopoiesis, but hope. Knowledge demands responsibility. This book is meant as a synthesis and an offering, a vigil kept over the fragile grammar of blood.

Health care systems have grown to produce ever growing amounts of garbage, which further strain the environment, costs of running the system, and regulatory demands especially in the post pandemic period. The traditional methods of managing linear waste are also inefficient and do not well comply with a circular economy. The paper introduces an artificial intelligence-based, scientifically supported digital twin system that aims at maximizing the effectiveness of healthcare waste management through the application of real time sensing and analytics based on predictive algorithms and through the use of multi objective optimization that is integrated into the circle economy paradigm. The framework virtualizes the streams of waste materials in the hospital through Internet of Things based monitoring and implements the machine learning models to forecast, assess, and analyze waste and provide an optimization engine that balances the goals of both economy and environmental objectives within the regulatory constraints. Statistical results showed that there were significant and significant increases in all the key indicators. The average recycling rate rose by 20.0 to 50.7 percent which is the 30.7 percentage point, and the sample t testing group has verified that the difference is significant at p < 0.001, with a statistical power of 0.99. Statistically significant findings prove that the value of artificial intelligence facilitated digital twins can be used to provide the measurable, reproducible, and scalable benefits in terms of circularity, cost-effectiveness, and regulatory compliance in the management of healthcare waste and are justified in their integration into a data driven model of a sustainable approach to operating the hospital.

Unfavorable experiences also present sudden changes in the distribution of clinical data streams, which tend to cause significant deterioration in the performance of traditional clinical decision support algorithms. The models of artificial intelligence used to date are primarily missing the ability to be generalized across the acute perturbations of physiology or system because most of them are not algorithmically resilient. This paper presents a Causal Foundation Model, which combines causal representation learning with large pre trained multimodal foundation models and digital twin-based simulation to become better robust to adverse clinical events. The framework limits latent representations by matching them with underlying causal factors by structural causal models and interventional training and a digital twin environment is used to simulate controlled adverse events like septic shock, pulmonary embolism and equipment failure. The evaluation of model performance was done on intensive care unit outcome prediction tasks given conditions of a normal and unfavorable condition to determine that the results were all in a form of mean values with standard deviations and ninety five percent confidence intervals. The proposed model was found to have the lowest mean penalty error of organ failure score prediction of 0.214 +- 0.003 and Brier penalty mortality prediction on the first attempt of 0.078 +- 0.002 significant at a p < 0.01 compared to recurrent and transformer-based baselines. The reduction in the performance loss was found to be very significant p = 0.001 very significant paired statistical testing confirmed that the major clinical events. These findings indicate that within a context of causal constraints, foundation models, and training on digital twins, statistically significant and clinically significant increases in resilience, accuracy, and capability in early warning are achieved, which can be used to further make clinical-based artificial intelligence systems more reliable and trustworthy.