Arman Process Journal (APJ)

Abstract The rapid growth of big data, coupled with advancements in data science and artificial intelligence, has significantly accelerated the potential for developing digital twins. A digital twin is a continuously updated virtual copy that enables the analysis, simulation, and prediction of a real-world object or process. Recently, applications of digital twins have seen substantial expansion across both academic communities and diverse governmental and military industries, and the healthcare sector is no exception. The concept of the digital twin for health promises a transformation in medical systems, encompassing service management and delivery, disease treatment and prevention, health maintenance, and ultimately, the enhancement of human life. By harnessing the ability to aggregate and analyze vast datasets from multiple sources, digital twins can facilitate personalized treatment pathways tailored to individual patient characteristics, medical history, and physiological data. This enables predictive analytics, preventative interventions, and the early identification of health risks and diseases through machine learning algorithms. Furthermore, digital twins can optimize clinical operations by analyzing treatment processes and resource allocation, leading to simplified and expedited treatment protocols. This review outlines the current applications of digital twins within the healthcare sector, delineates their core components in medicine, and examines the present landscape of open research opportunities. We demonstrate how the integration of diverse enabling technologies and tools—such as artificial intelligence, large language models, and mechanistic modeling—paves the way for overcoming limitations and fostering broader clinical adoption and implementation of digital twins. This review also aims to assist data scientists, clinicians, and policymakers in developing future medical digital twins and bridging the gap between this emerging paradigm's theoretical promise and practical realization.

Abstract Statistics deals with the collection, analysis, and use of data to solve problems. All people, both in specialized fields and in everyday life, come across information in the form of numbers or data through contact with the press, radio, television and other mass media. So some understanding of statistics will be useful for everyone. Because engineers, scientists, and administrators are constantly involved in data collection and analysis, statistical knowledge is essential for these disciplines. Experimental and observational studies The general purpose of a statistical research project is to investigate random events, and in particular to draw conclusions about the effect of changes in the value of indicators or independent variables on a response or dependent variable. There are two main methods of random statistical studies: experimental studies and observational studies. In both types of studies, the effect of changes in a non-dependent variable (or variables) on the behavior of dependent variables is observed. The difference between the two methods is in how the study is actually conducted.
In human subject research, a survey is a list of questions that aims to extract specific data from a specific group of people. Surveys may be conducted by telephone, post, Internet, as well as on street corners or shopping malls. Statistics are used to gather or gain knowledge in areas such as social research and demography. Once the data have been collected, either by a specific sampling method or by recording responses to behaviors in an experimental set (experimental design) or by repeatedly observing a process over time (time series), graphical or numerical summaries can be made. Use of descriptive statistics achieved.