ABSTRACT
The advancement of high-throughput technologies and the expansion of Internet web-services have a significant impact on accessing the biological datasets for the public, especially for scientists/research communities. As a result, ways to process, analyze, and infer knowledge have drastically changed in the recent past related to healthcare data records, and in this context, data-science terminologies like machine learning, artificial intelligence, IoT, and blockchain have become a core part of our daily routine and revolutionized the way the translation activities are designed/executed to lead the discovery across the globe for the betterment of e-health scenarios. Currently, the advanced technologies like artificial intelligence, machine learning, and blockchain have invaded medical research and the pharmaceutical industry too. Although the massive advancement in conventional research is quickly occurring, translation to the routine medical practices has been slower. Recently, the National Institute of Health has explored the present state of the knowledge, infrastructure challenges, and barriers to the comprehensive implementation/execution of translation studies. In the recent past, the clinical trials have been growing at a rapid rate, and the design of such problems must suggest answers for patients. Furthermore, these trials need to focus on enhancing patient treatment, and in order for this to work, they need to experimentally test important effects that truly represent real-world settings/concerns.
