# Digital Twins Generated by Artificial Intelligence in Personalized Healthcare
## 3 Results
### Sources of Data for AI-Based DTs  
The creation and maintenance of AI-based DTs in healthcare is facilitated by the availability of large historical datasets from previous clinical trials and other real-world data sources (e.g., patient biometrics collected from mobile devices). AI-based DTs in healthcare rely on a diverse set of data sources to accurately model a patient's health status. Electronic health records (EHRs) provide a structured medical history, including diagnoses, treatments, and laboratory test results. Mobile devices and remote monitoring systems provide real-time physiological data such as heart rate, blood pressure, and activity levels. Medical imaging (e.g., MRI, CT scans) offers detailed anatomical and functional information that is crucial for creating accurate models of organs and systems. Genomic data provide personalized insights into a patient's genetic predispositions and potential responses to treatment. Patient-reported outcomes and data from mobile health apps capture subjective experiences, symptoms, and lifestyle factors that impact health. Environmental and social determinants of health provide context about external factors, such as air quality and socioeconomic status, that affect a patient's well-being. Pharmacy records provide information on medication adherence and potential interactions. Finally, clinical notes and unstructured data from healthcare professionals add valuable narrative information, often processed using natural language processing (NLP) techniques to help others understand a patient's condition. There is also the challenge of combining disparate datasets and omics to quickly interpret large datasets to generate health and disease indicators to improve sampling and longitudinal analysis [17; 18; 19; 20]. Multiomics in AI-based DTs involves integrating data from multiple \"omics\" disciplines--genomics, proteomics, transcriptomics, metabolomics, and epigenomics--to create a comprehensive and personalized model of a patient's biological processes. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt}} \hline \hline
**Parameter/Feature** & **Value** \\ \hline Leading types of publication & Article (35.9\%), conference paper (25.1\%), book chapter (15.2\%), review (13.9\%) \\ \hline Leading areas of science & Computer science (31.0\%), Engineering (20.4\%), Medicine (10.6\%) \\ \hline Leading topics & Industrial: design and manufacturing, telecommunications, security systems, human–computer interactions \\ \cline{2-3}  & Medical: cardiac arrhythmia, neuromuscular disorders, nursing, tuberculosis and leprosy, wounds and ulcers, ECK, multiple sclerosis, high-level disinfection \\ \hline Leading countries & India, USA, China, UK, Italy, Canada, Germany \\ \hline Leading scientists & [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] \\ \hline Leading affiliations & Swinburne University of Technology, Galogtias University, Mohamed Bun Zayed University of Artificial Intelligence \\ \hline Leading funders (where information available) & National Natural Science Foundation of China, Horizon 2020 Framework Programme/European Commission, Ministry of Science and ICT of South Korea, Ministry of Science and Technology of China \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of results of bibliographic analysis (WoS, Scopus, PubMed). Genomics provides information about a patient's DNA sequence and genetic variants, helping to predict disease risk and response to treatment. Proteomics analyses the proteins expressed by genes, offering insight into cellular function and disease mechanisms. Transcriptomics examines RNA transcripts to understand patterns of gene expression that can indicate how genes are activated or repressed under specific conditions. Metabolomics focuses on metabolites, small molecules produced during metabolic processes, revealing real-time physiological states and treatment responses.