The convergence of artificial intelligence (AI) and blockchain is transforming the medical sector on its head unprecedented possibilities to establish sustainable, efficient, and patient-centered health systems. This chapter discusses how the characteristics of the decentralized, immutable design of blockchain conform to the ability of AI to perform forecasting analysis and planning. Through this, strengths of integrating a high-priority medical model coupled with a resource distribution model become real in solving systems issues in healthcare. The first purpose is met by AI technology that can accomplish the task of augmented diagnosis, the individual treatment plan, and operational efficiencies, whereas blockchain can guarantee a secure and transparent interoperation of health data between large groups of fragmented clients. All these technologies combined will instill confidence in every stakeholder and enable citizens to manage their personal health records. This combination too is highly promising for programs of public good—indeed it renders real-time feedback of diseased regions a reality. By this means, decentralized clinical trials will be able to travel to the field and the hard-to-reach communities will have equal access to healthcare. The chapter also ends with presenting the practical success and policy implications of the twin tech system model. Of special interest is the issue of how and whether AI technologies supported by blockchain can be used to support the UN Sustainable Development Goals (SDGs), in particular, SDG 3: Good Health and Well-being. Making the chapter relevant to the ecological concepts, the authors suggest the healthcare reform process in which all actors are included in order to establish healthcare ecological system. Also based on thorough analysis and case studies the chapter further establishes that the integration of blockchain and AI has tremendous potential to develop a resilient and comprehensive healthcare ecological network.
GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...
Xiaotian Zhang, Chun-yan Li, Yi Zong et al.· arXiv.org· 216 citations· ⚡17
An algorithm is proposed which leverages disentangled semantic factors to generate adversarial perturbation by altering controlled semantic attributes to fool the learner towards various "adversarial" targets.
Haonan Qiu, Chaowei Xiao, Lei Yang et al.· arXiv.org· 208 citations· ⚡17
This work proposes a HALO that directly maximizes the utility of generations instead of maximizing the log-likelihood of preferences, and suggests that the best loss depends on the inductive biases most appropriate for a given setting, an oft-overlooked consideration.
This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.
This work shows that orders of magnitude enhancement in performance could be obtained by a combination of hardware improvements and tight quantum-HPC integration and introduces high-performance architectures for quantum-probabilistic computing with custom-designed accelerators to tackle today's industry-scale classical...
Masoud Mohseni, Artur Scherer, K. Johnson et al.· arXiv.org· 121 citations· ⚡9
This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.