当前，药物临床试验面临着两大难题：数据真实性及相关人员操作规范性。现阶段国内外在药物临床试验方面的监管主要以事后监查为主，在数据质量管理以及操作规划标准的监查方面存在一定的时延性。而区块链通过非对称加密、哈希算法及智能合约等技术，可以在保证受试者隐私信息的前提下，提高政府相关监督机构的监管效率，提升药物临床试验数据管理的透明度；同时，与物联网的紧密结合可以实现对标准操作规范的进一步核查，与人工智能的结合有望实现受试者的自动招募。

Clinical drug trials are confronted with two major issues: first, data authenticity, for instance, if any data falsification is conducted during the whole trial; second, whether the standard of procedure is accordingly conducted throughout the whole trial or not. Currently, both domestic and overseas clinical drug trials are not supervised without delay (ex-post inspection). Blockchain technology can improve the efficiency of Food and Drug Administration and the transparency of trials while the rights and safety of human research subjects are guaranteed by the integrated technology such as chained structure, asymmetry key algorithm, hash algorithm, and smart contract. Furthermore, with the assistance of internet of things (IoT) and artificial intelligence (AI), the actual supervision over the whole trial and automatic recruitment of human research subjects are expected to achieve.

Age stands as a primary risk factor for diseases and disabilities among the elderly. To effectively assess the underlying aging processes, accurate measures of biological age and rates of aging across multiple levels of aging features are essential. Biological age derives from physiological assessments of systems and organs. It has emerged as a superior predictor of age-related diseases and mortality compared to chronological age. Recent advancements in machine learning have catalyzed the development of sophisticated models capable of quantitatively characterizing biological aging with different types of data. This review explores the machine learning models in advancing our understanding of biological aging, highlighting the potential of these innovative approaches to facilitate aging research and personalized healthcare strategies.

Age stands as a primary risk factor for diseases and disabilities among the elderly. To effectively assess the underlying aging processes, accurate measures of biological age and rates of aging across multiple levels of aging features are essential. Biological age derives from physiological assessments of systems and organs. It has emerged as a superior predictor of age-related diseases and mortality compared to chronological age. Recent advancements in machine learning have catalyzed the development of sophisticated models capable of quantitatively characterizing biological aging with different types of data. This review explores the machine learning models in advancing our understanding of biological aging, highlighting the potential of these innovative approaches to facilitate aging research and personalized healthcare strategies.