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. 2023 Aug 24;9(9):e19390. doi: 10.1016/j.heliyon.2023.e19390

Literature review of digital twin in healthcare

Tatiana Mallet Machado 1,∗, Fernando Tobal Berssaneti 1
PMCID: PMC10558347  PMID: 37809792

Abstract

This article aims to make a bibliometric literature review using systematic scientific mapping and content analysis of digital twins in healthcare to know the evolution, domain, keywords, content type, and kind and purpose of digital twin's implementation in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified.

The increase in publications of digital twins in healthcare is quite recent and it is still concentrated in the domain of technology sources. The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side. Digital twins in healthcare are probably stepping out of the infancy phase. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector. There is an absence of some publication's types in general, device and care subgroup and no whole body or hospital digital twin was reported. Based on the presented arguments, guidelines for future research were presented: advance in the creation of general frameworks, in subgroups not as much explored, and in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved.

Keywords: Digital twin, Simulation, Healthcare, Bibliometric, Review

1. Introduction

“Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity” [1]. Healthcare is the provision of medical care services to achieve this definition and is a resource that helps people lead with their everyday lives. It is a complex system and new improvement's methods besides old ones, like lean, are vital to increase its efficiency, productivity, and quality [2,3]. Therefore, Digital Twin (DT), that is the pinnacle of the fourth industrial revolution or the so-called industry 4.0, is being used to optimize this complex system. DT is a digital representation of a physical entity that combine different technologies like Internet of Things (IoT), big data, Cyber-Physical System (CPS), Artificial Intelligence (AI), and Machine Learning (ML), to provide automation, quality, efficiency, productivity etc. [4,5]. It became so significant, that the consulting firm Gartner named it as one of the top ten strategic technology trends that organizations need to explore for three consecutive years (2017–2019) [[6], [7], [8]]. Since it combines many technologies, several companies, such as Microsoft [9], Siemens [10], and GE [11] recently launched solutions to facilitate the development of digital twins for devices, processes, systems, and persons.

As there is a certain literary and practical relevance of digital twins in healthcare, the idea was to assess how the phrase “digital twin” is being used in the healthcare sector in databases publications. Thus, the constructs of this research are digital twins and healthcare.

Concerning previous discoveries in the literature, the main authors of the healthcare area thought about literature reviews of DT to give an overview in precision medicine [5], in hematology [12], and in facilities management [13]; to analyze characteristics and tools [4,14], issues [[15], [16], [17], [18]], risks [19], possible applications [[20], [21], [22], [23], [24], [25]], gaps in modeling supply chain for vaccines [26], and state of the art in general [27], in medical imaging [28] and in Intensive Care Unit (ICU) medicine [29]; and to investigate goals [30], open research problems [31], and the progress made for precision medicine [[32], [33], [34]].

However, there is the limitation of a literature review to know the evolution, domain, keywords, literature content type, and kind and purpose of DT in healthcare, so a consolidation and future improvement of existing knowledge can be made and gaps for new studies can be identified as the theoretical contribution of this project. Therefore, the main objective of this article is aligned with this limitation in the literature and the research question related to this objective is: How is the evolution, domain and keywords of the subject? How is the literature content type and concentration themes of digital twins in healthcare?

This article adopts the method of a bibliometric literature review from a miner's perspective using systematic scientific mapping and content analysis. This method was chosen in accordance with this article's goal of mapping existing knowledge and of establishing, criticizing, or connecting previously existing sets of knowledge to identify researchers and influential research groups in a field, its evolution curve, and trends to improve understanding of the topic and feed future studies.

The structure of this article comprises the sections: materials and methods (section 2), results (section 3), discussion (section 4), and conclusion (section 5).

2. Materials and methods

In this article, the method of bibliometric literature review with a miner perspective using systematic scientific mapping and content analysis adapted from Ref. [35] was chosen in accordance with this article's goal. In addition, this method was chosen to increase the rigor and quality of the research, with aims to “strict precision, accuracy” [36,37].

The bibliometric literature review approach augments the traditional literature review by analyzing the whole research activity to discover patterns and provide fresh insights [38]. This method has an intrinsically mining perspective, as it identifies gaps, organizes, and categorizes the literature, problematizes it, and identifies and exposes contradictions [39]. Scientific mapping has the potential of introducing a transparent and replicable process, and it is increasingly being used to map, represent, and visualize the structure and development of scientific fields and disciplines [40]. Content analysis is a technique for reading and interpreting the content of publications. It explores knowledge of aspects and phenomena that is otherwise inaccessible [41], and it is carried out by reading abstracts of publications.

2.1. Search strategy

To set a golden list of publications to the search string formation, one previous search was conducted at Google Scholar, ordered by relevance with this article's constructs ((“digital twin*") OR (“process twin*") OR (“data twin*")) AND ((“healthcare”) OR (“health care")). From these research's results, the first 20 publications specifically to healthcare were added to the golden list (e.g., not specifically: “Digital twin technologies and smart cities” and “Industry 4.0: digital twin and its industrial applications”).

Then search strings were tested on their respective databases until the whole golden list was covered. Finally, the research for this article was done with the validated search string in the IEEE, Scopus, and Web of Science databases made in titles, abstracts, and keywords of publications up to the date 6th July 2022, and the number of results is listed in Table 1. These databases were chosen because IEEE is the leading academic database in the field of engineering and computer science, and Scopus and Web of Science are two of the biggest commercial, bibliographic databases covering almost any discipline [42,43].

Table 1.

Search string in databases and results.

Search string Database Number of Results
((“digital twin*") OR (“process twin*") OR (“data twin*")) AND ((“healthcare”) OR (“health care")) IEEE 61
Scopus 153
Web of Science 195
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2.2. Selection criteria

From the publications found, duplicates were removed, and then exclusion/eligibility criteria with due justification were used (Table 2) to select only publications that fit the subject and purpose of this article. Then the remaining publications abstracts were read and publications not specific to the healthcare sector and not related to digital twins were eliminated. No quality assessment was made after the selection criteria since this is not required for mapping studies [35].

Table 2.

Exclusion/eligibility criteria and justification.

Criteria Justification
Publication type: not article, paper, book, and book chapter. Elimination of ineligible publication type.
Keyword: no “digital twin”, “process twin”, “data twin”, “healthcare” and/or “health care” words found in abstract, keywords, or title. Search revalidation and eligibility.
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The database search identified in the literature search for digital twins in healthcare totaled 409 publications. After duplicates’ removal, a total of 293 publications remained. Afterward the exclusion criterias (according to Table 2 and abstract read) were used and a total of 121 publications were included in the review for the bibliometric and content analysis (Fig. 1).

Fig. 1.

Fig. 1

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Flowchart of the literature review.

2.3. Bibliometric analysis strategy

At first analysis of the selected publications, Excel and VOS viewer (visualization of similarities) software were used to graphically analyze the results [44] from the following methods, which are immediately available after publication and do not take time to accumulate like citations [40]: (a) Publication year, to assess the importance of the subject for the academic field over time; (b) Publication sources with more than one publication, to identify the domain in which the publications are being published; (c) Co-occurrence of all keywords with more than five occurrences, to identify the current keywords on the subject's research and to use as criteria in the content analysis strategy of publications' objective theme and subtheme (Chapter 2.4). Afterward, a content analysis was used since it is a systematic analysis of textual elements.

2.4. Content analysis strategy

The content analysis of the selected publications was carried out mainly through the reading of abstracts. There it was possible to group the publications in families according to Table 3 of: (a) Literature content type; (b) Publications' objective theme (one publication can be classified in more than one group), that refers to the kind of digital twin used; (c) Publications' objective subtheme, that is the digital twin's main purpose in the publication. Then the grouping results of theme and subtheme divided by literature content type were printed as bar chart.

Table 3.

Group properties and criteria.

Property Group criteria
Literature content type [45,46]

  • •Application: detailed descriptions of the application in specific fields, with technical details and detailed case studies.

  • •Framework: general descriptions of the application in respective fields, with in-depth analysis, implementation framework, and a rather detailed case study.

  • •Discussion: research summary or discussion on selected literature concerning a particular topic.

  • •Review: structured research summary on existing literature concerning a particular topic.
Publications' objective theme group

  • •Concern: related to digital twins in general, not focused on places and/or humans.

  • •Hospital: digital twins with the purpose of places.

  • •Patient: digital twin with the purpose of humans.
Publications' objective subtheme group

  • •Aspects, issues and/or policies: regard to aspects, issues, problems, risks, requirements and/or policies of digital twin, like security, privacy, or knowledge management.

  • •Care: digital twin of services or supplies related to the health of a patient, like crisis/emergency alarms, notifications, and management.

  • •Device: about a device or an equipment digital twin, like dual energy x-ray absorptiometry or electrocardiogram.

  • •Facilities management: digital twin that integrate multiple disciplines to ensure the organization has the necessary resources to function on a day-to-day operation [47].

  • •Precision medicine: digital twin customized individualized patient's care and treatment based on genetic, environmental and lifestyle factors [48].

  • •General: related to digital twins in general, not focused on a specific subtheme.
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3. Results

3.1. Bibliometric analysis results

With application of bibliometric methods to publication year (Fig. 2), it is possible to observe that the interest in the topic is growing exponentially, with an increase of more than 100% from 2020 to 2021 and that the years 2021 and 2022 concentrates 65% of all publications considered.

Fig. 2.

Fig. 2

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Chart of publication years.

Regarding the sources in the final selection of publications with more than one publication (Fig. 3), it is possible to note that the subject is being published majorly at technological sources with 62%. The rest of the sources are from the medical and biological domain.

Fig. 3.

Fig. 3

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Publications' sources with more than one publication.

In the co-occurrence of all keywords with more than five occurrences (Fig. 4), the three different colors are the clusters of related items, the size of circles and label are relative to the number of publications, and the distance and line thickness reflect the strength of the relation between the items. The red cluster is related to publications of hospital's, device's, and data's digital twin with its issues; the green one is of human's digital twins for precision medicine; and the blue cluster is related to knowledge management. Here it is also possible to identify that after digital twin (81 occurrences and 2071 link strength) or digital twins (19 occurrences and 360 link strength) and health care (52 occurrences and 1387 link strength) or healthcare (22 occurrences and 484 link strength), the most important keywords were related to patient (human – 27 occurrences and 853 link strength; humans – 21 occurrences and 745 link strength) and to the main technologies of the digital twin (artificial intelligence – 27 occurrences and 799 link strength; internet of things – 21 occurrences and 666 link strength; machine learning – 18 occurrences and 682 link strength) and medical services (20 occurrences and 608 link strength).

Fig. 4.

Fig. 4

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Co-occurrence of all keywords with more than five occurrences.

3.2. Content analysis results

Regarding the analysis of literature content type (Fig. 5), it is found that framework highlights with 31% of all publications (37 of 121 publications) and other types have almost the same number of publications. In addition, the number of applications, that was the lowest until 2021, in 2022 trespass other types.

Fig. 5.

Fig. 5

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Literature content type.

The analysis of objective theme groups (Fig. 6) shows that the subject is majorly focused on publications related to patient's digital twins (63 of 136 publications or 52%) and that the two top theme groups (patient and hospital) have 89% of all publications (108 of 136 publications). Additionally, discussions, frameworks and reviews are majorly concentrated in patient group (respectively 13 of 32 or 41%, 22 of 40 or 55% and 17 of 36 or 47%) and application are in hospital group (14 of 28 publications or 50%).

Fig. 6.

Fig. 6

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Publications' objective theme group.

As for the analysis of objective theme subgroups (Fig. 7), it is possible to identify that precision medicine concentrates 29% of all publications (35 of 121 publications) and aspects, issues and/or policies comes in second place with 25% (30 of 121 publications). Moreover, applications are concentrated in device and facilities management subgroup (15 of 28 publications or 54%), discussions in precision medicine subgroup (9 of 37 publications or 34%), frameworks in aspects, issues and/or policies subgroup (14 of 37 publications or 38%) and review in general and precision medicine subgroups (20 of 29 or 69%); Another point is the absence of applications and frameworks in general subgroup, of discussion and review in device subgroup and of review in care subgroup.

Fig. 7.

Fig. 7

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Publications' objective theme subgroup chart.

The complete table of publications with literature content type, theme groups, and theme subgroups are in Table A1, Appendix A.

4. Discussion

The main objective of this review was to synthesize evidence on the evolution, domain, keywords, content type, kind and purpose of digital twin applied to healthcare, highlighting potential areas for future studies, and using a bibliometric literature review from a miner's perspective through systematic scientific mapping and content analysis.

The bibliometric analysis results shows that the increase in publications of digital twins in healthcare is quite recent (Fig. 2), i.e., 2019, three years later than the general number of publications reported by Ref. [49] and that it is still concentrated in the domain of technology sources (Fig. 3).

The keywords importance results of Fig. 4 (co-occurrence of all keywords with more than five occurrences) agreed with the objective theme group (Fig. 6), where the patient group is the most important and the most challenging demand in the future of healthcare [21,22,50,51]. However, this result did not agree with the objective theme subgroup analysis (Fig. 7), since precision medicine and aspects, issues and/or policies are not so crucial in the keywords results, but precision medicine is one of the main subject for the effective future of healthcare [52] that will only work if aspects, issues and/or policies are resolved [4,53].

The development of digital twins in healthcare indicates that if the number of applications in 2022 remains greater than other publications types (Fig. 5), according to Ref. [45], it could suggests that digital twin in healthcare is stepping out of its infancy phase, where it was not mature enough to have only a few frameworks to be followed, that the number of publications regarding the subgroup of aspects, issues and/or policies is still the second highest and there is an absence of frameworks in general subgroup (Fig. 7). Another point of attention regarding theme groups (Fig. 6) and subgroups, is that the hospital group and the device and facilities management subgroups have more applications and are more mature than patient group and other subgroups, since many ideas could be learned and utilized from the digital twin of the manufacturing sector (i.e., [29]) that is in a rapid development stage, where researchers explore more real practices.

As for gaps in literature, as already mentioned, the absence of applications and frameworks in general subgroup is due to the maturity level of the healthcare sector, however there are some interest frameworks in other subgroups that could be explored, like [[54], [55], [56], [57], [58]]. As for the device and care subgroups, reviews and discussions could already be made, since there are relevant and significant publications, like different standardized and generalized frameworks for care [54,55] or devices digital twin applications for cardiovascular system [59,60]. Another point is that it was not reported any whole body or hospital digital twin, something that many publications envision and it could be explored.

5. Conclusions

The finding of this review has implications for the theoretical and practical side of digital twins in healthcare. It achieved the theoretical contribution of understanding the evolution, domain, keywords, content type, nature and purpose on the subject, identifying gaps for future studies; since that, it gave new fresh insights and perspective to the actual view: (1) The increase in publications of digital twins in healthcare is quite recent, three years later than the general number of publications and it is still concentrated in the domain of technology sources; (2) The subject is majorly concentrated in patient's digital twin group and in precision medicine and aspects, issues and/or policies subgroups, although the publications keywords mirror it only at the group side; (3) Digital twins in healthcare is probably stepping out of the infancy phase, with more applications in 2022 than other publications types. On the other hand, digital twins in hospital group and the device and facilities management subgroups are more mature with all knowledge gathered from the manufacturing sector; (4) There is an absence of applications and frameworks in general subgroup, of discussions and reviews in device subgroup and of reviews in care subgroup; and no whole body or hospital digital twin was reported.

The main limitation to that achievement is that the publications do not represent the universality of the subject but only those that were captured by the adopted keywords search and the adopted databases. Based on the aforementioned foundations, three directions are proposed for future research: (1) Advance in the creation of general frameworks that can serve as basis for researches to resolve any gap group presented, for example, through questions, critiques, reviews, and discussions of analogies about frameworks and real-life applications already published; (2) Advance of the subject in subgroups not as much explored, such as digital twin in the improvement of services or supplies related to the health of a patient in remote areas in Brazil or digital twin of a magnetic resonance imaging (MRI) to a future facility management improvement; (3) Advance of the subject in groups and subgroups already explored, but that need more advancement to achieve the main goals of a whole human or hospital digital twin with the main issues resolved. In addition to this proposal, it is suggested that this publication should be repeated in the future to understand how the subject of digital twins in healthcare progressed.

Author contribution statement

Tatiana Mallet Machado: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Wrote the paper.

Fernando Tobal Berssaneti: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data.

Data availability statement

Data included in article/supp. material/referenced in article.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors would like to acknowledge that this research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.

Appendix A. Publications' list by group, subgroup, and content type

The complete table of publications by group, subgroup, and content type is in Table A1.

Table A.1.

Publications' list by group, subgroup, and content type.

Group Subgroup Content type
Application Discussion Framework Review
Concern Aspects, issues and/or policies (19) [[61], [62], [63]] [[64], [65], [66], [67], [68]] [56,[69], [70], [71], [72], [73]] [[15], [16], [17], [18], [19]]
Hospital Aspects, issues and/or policies (4) [74] [75] [76,77]
Patient Aspects, issues and/or policies (10) [78] [75] [76,77,[79], [80], [81], [82], [83], [84]]
Concern Care (1) [85]
Hospital Care (4) [86] [87] [88,89]
Patient Care (6) [90] [87] [54,55,89,91]
Concern Device (0)
Hospital Device (6) [59,60,[92], [93], [94]] [95]
Patient Device (3) [[96], [97], [98]]
Concern Facilities management (0)
Hospital Facilities management (21) [[99], [100], [101], [102], [103], [104], [105]] [[106], [107], [108]] [57,[109], [110], [111], [112], [113], [114]] [13,25,26,29]
Patient Facilities management (0)
Concern General (8) [50,[115], [116], [117], [118]] [4,22,28]
Hospital General (8) [119,120] [12,20,21,23,24,27]
Patient General (9) [119,120] [12,14,20,21,23,24,27]
Concern Precision medicine (0)
Hospital Precision medicine (2) [121] [30]
Patient Precision medicine (35) [[122], [123], [124], [125], [126], [127]] [121,[128], [129], [130], [131], [132], [133], [134], [135]] [58,[136], [137], [138], [139], [140], [141], [142], [143], [144]] [5,[30], [31], [32], [33], [34],[145], [146], [147], [148]]
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References

  • 1.World Health Organization . International Health Conference. New York; 1946. Constitution of the world health organization; pp. 1–18.https://apps.who.int/gb/bd/PDF/bd47/EN/constitution-en.pdf?ua=1 [Google Scholar]
  • 2.Braithwaite J. Changing how we think about healthcare improvement. BMJ. 2018 doi: 10.1136/bmj.k2014. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 3.Mahajan A., Islam S.D., Schwartz M.J., Cannesson M. A hospital is not just a factory, but a complex adaptive system—implications for perioperative care. Anesth. Analg. 2017;125:333–341. doi: 10.1213/ANE.0000000000002144. [DOI] [PubMed] [Google Scholar]
  • 4.Khan S., Arslan T., Ratnarajah T. Digital twin perspective of fourth industrial and healthcare revolution. IEEE Access. 2022;10:25732–25754. doi: 10.1109/ACCESS.2022.3156062. [DOI] [Google Scholar]
  • 5.Sharma C., Gupta G. Mobile Health: Advances in Research and Applications. 2021. Innovation insight for healthcare provider digital twins: a review. [Google Scholar]
  • 6.Gartner Inc. Newsroom; 2016. Gartner Identifies the Top 10 Strategic Technology Trends for 2017.https://www.gartner.com/en/newsroom/press-releases/2016-10-18-gartner-identifies-the-top-10-strategic-technology-trends-for-2017 [Google Scholar]
  • 7.Gartner Inc. Newsroom; 2017. Gartner Identifies the Top 10 Strategic Technology Trends for 2018.https://www.gartner.com/en/newsroom/press-releases/2017-10-04-gartner-identifies-the-top-10-strategic-technology-trends-for-2018 [Google Scholar]
  • 8.Gartner Inc. Newsroom; 2018. Gartner Identifies the Top 10 Strategic Technology Trends for 2019.https://www.gartner.com/en/newsroom/press-releases/2018-10-15-gartner-identifies-the-top-10-strategic-technology-trends-for-2019 [Google Scholar]
  • 9.Microsoft O que é o. Microsoft Documentação; 2021. Azure Digital Twins?https://docs.microsoft.com/pt-pt/azure/digital-twins/overview [Google Scholar]
  • 10.Siemens AG, PLM Automation, Digital Twin Glossary, (n.d.). https://www.plm.automation.siemens.com/global/pt/our-story/glossary/digital-twin/24465 (accessed April 16, 2021).
  • 11.GE Digital, Digital Twin, GE Digital (n.d.). https://www.ge.com/digital/applications/digital-twin (accessed April 16, 2021)..
  • 12.Walter W., Haferlach C., Nadarajah N., Schmidts I., Kühn C., Kern W., Haferlach T. How artificial intelligence might disrupt diagnostics in hematology in the near future. Oncogene. 2021;40:4271–4280. doi: 10.1038/s41388-021-01861-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Madubuike O.C., Anumba C.J. Computing in Civil Engineering 2021. American Society of Civil Engineers; Reston, VA: 2022. Digital twin application in healthcare facilities management; pp. 366–373. [DOI] [Google Scholar]
  • 14.Sengan S., Kumar K., Subramaniyaswamy V., Ravi L. Cost-effective and efficient 3D human model creation and re-identification application for human digital twins. Multimed. Tool. Appl. 2022;81:26839–26856. doi: 10.1007/s11042-021-10842-y. [DOI] [Google Scholar]
  • 15.Ali M., Naeem F., Tariq M., Kaddoum G. Federated learning for privacy preservation in smart healthcare systems: a comprehensive survey. IEEE J Biomed Health Inform. 2022:1–14. doi: 10.1109/JBHI.2022.3181823. [DOI] [PubMed] [Google Scholar]
  • 16.Huang P., Kim K., Schermer M. Ethical issues of digital twins for personalized health care service: preliminary mapping study. J. Med. Internet Res. 2022;24 doi: 10.2196/33081. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Leo C.G., Tumolo M.R., Sabina S., Colella R., Recchia V., Ponzini G., Fotiadis D.I., Bodini A., Mincarone P. Health technology assessment for in silico medicine: social, ethical and legal aspects. Int. J. Environ. Res. Publ. Health. 2022;19:1510. doi: 10.3390/ijerph19031510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.Volkov I., Radchenko G., Tchernykh A. Digital twins, internet of things and mobile medicine: a review of current platforms to support smart healthcare. Program. Comput. Software. 2021;47:578–590. doi: 10.1134/S0361768821080284. [DOI] [Google Scholar]
  • 19.Popa E.O., van Hilten M., Oosterkamp E., Bogaardt M.-J. The use of digital twins in healthcare: socio-ethical benefits and socio-ethical risks. Life Sci Soc Policy. 2021;17:6. doi: 10.1186/s40504-021-00113-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Bhattad P.B., Jain V. Artificial intelligence in modern medicine – the evolving necessity of the present and role in transforming the future of medical care. Cureus. 2020 doi: 10.7759/cureus.8041. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.de Maeyer C., Markopoulos P. 2021. Future Outlook on the Materialisation, Expectations and Implementation of Digital Twins in Healthcare. [DOI] [Google Scholar]
  • 22.Erol T., Mendi A.F., Dogan D. 2020 4th International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) IEEE; 2020. The digital twin revolution in healthcare; pp. 1–7. [DOI] [Google Scholar]
  • 23.Kobyakova O.S., Starodubov V.I., Kurakova N.G., Tsvetkova L.A. Digital twins in healthcare: an assessment of technological and practical prospects. Annal. Russ. Acad. Med. Sci. 2021;76:476–487. doi: 10.15690/vramn1717. [DOI] [Google Scholar]
  • 24.Kurakova N.G., Tsvetkova L.A., Polyakova YuV. Zhurnal Im. N.I. Pirogova; 2022. Digital Twins in Surgery: Achievements and Limitations, Khirurgiya. 97. [DOI] [PubMed] [Google Scholar]
  • 25.Cahill R.A., mac Aonghusa P., Mortensen N. The age of surgical operative video big data - my bicycle or our park? Surgeon. 2022;20:e7–e12. doi: 10.1016/j.surge.2021.03.006. [DOI] [PubMed] [Google Scholar]
  • 26.Golan M.S., Trump B.D., Cegan J.C., Linkov I. Supply chain resilience for vaccines: review of modeling approaches in the context of the COVID-19 pandemic. Ind. Manag. Data Syst. 2021;121:1723–1748. doi: 10.1108/IMDS-01-2021-0022. [DOI] [Google Scholar]
  • 27.Patrone C., Galli G., Revetria R. Frontiers in Artificial Intelligence and Applications. 2019. A state of the art of digital twin and simulation supported by data mining in the healthcare sector. [Google Scholar]
  • 28.Panayides A.S., Amini A., Filipovic N.D., Sharma A., Tsaftaris S.A., Young A., Foran D., Do N., Golemati S., Kurc T., Huang K., Nikita K.S., Veasey B.P., Zervakis M., Saltz J.H., Pattichis C.S. AI in medical imaging informatics: current challenges and future directions. IEEE J Biomed Health Inform. 2020;24:1837–1857. doi: 10.1109/JBHI.2020.2991043. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Chase J.G., Zhou C., Knopp J.L., Shaw G.M., Näswall K., Wong J.H.K., Malinen S., Moeller K., Benyo B., Chiew Y.S., Desaive T. Digital twins in critical care: what, when, how, where, why? IFAC-PapersOnLine. 2021;54:310–315. doi: 10.1016/j.ifacol.2021.10.274. [DOI] [Google Scholar]
  • 30.Ahmadi-Assalemi G., Al-Khateeb H., Maple C., Epiphaniou G., Alhaboby Z.A., Alkaabi S., Alhaboby D. 2020. Digital Twins for Precision Healthcare; pp. 133–158. [DOI] [Google Scholar]
  • 31.Verma R., Patel S., Minj S.V., Bhat A. 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) IEEE; 2022. Healthcare 5.0: a study on improving personalized care; pp. 1815–1818. [DOI] [Google Scholar]
  • 32.Boată A., Angelescu R., Dobrescu R. Using digital twins in health care. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science. 2021 [Google Scholar]
  • 33.Schwartz S.M., Wildenhaus K., Bucher A., Byrd B. Digital twins and the emerging science of self: implications for digital health experience design and “small” data. Front. Comput. Sci. 2020;2 doi: 10.3389/fcomp.2020.00031. [DOI] [Google Scholar]
  • 34.Voigt I., Inojosa H., Dillenseger A., Haase R., Akgün K., Ziemssen T. Digital twins for multiple sclerosis. Front. Immunol. 2021;12 doi: 10.3389/fimmu.2021.669811. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Kitchenham B.A., Budgen D., Brereton P. CRC Press; Boca Raton: 2016. Evidence-Based Software Engineering and Systematic Reviews. [Google Scholar]
  • 36.Gulati R. Tent Poles, tribalism, and boundary spanning: the rigor-relevance debate in management research. Acad. Manag. J. 2007;50:775–782. [Google Scholar]
  • 37.Narasimhan R. The fallacy of impact without relevance – reclaiming relevance and rigor. Eur. Bus. Rev. 2018;30:157–168. doi: 10.1108/EBR-01-2017-0005. [DOI] [Google Scholar]
  • 38.Porter A.L., Kongthon A. Kluwer Academic Publishers; 2002. Research Profiling: Improving the Literature Review.http://www.derwent.com/worldpatentsindex [Google Scholar]
  • 39.Breslin D., Gatrell C. Theorizing through literature reviews: the miner-prospector continuum. Organ. Res. Methods. 2020:1–29. doi: 10.1177/1094428120943288. [DOI] [Google Scholar]
  • 40.Zupic I., Čater T. Bibliometric methods in management and organization. Organ. Res. Methods. 2015;18:429–472. doi: 10.1177/1094428114562629. [DOI] [Google Scholar]
  • 41.Bardin L. first ed. Edições 70, São Paulo; 2015. Análise de Conteúdo. [Google Scholar]
  • 42.Paperpile L.L.C. 2021. The Top List of Academic Research Databases, Research and Writing Guides.https://paperpile.com/g/academic-research-databases/ [Google Scholar]
  • 43.Pranckutė R. Web of science (Wos) and scopus: the titans of bibliographic information in today's academic world. Publications. 2021;9 doi: 10.3390/publications9010012. [DOI] [Google Scholar]
  • 44.van Eck N.J., Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–538. doi: 10.1007/s11192-009-0146-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 45.Liu M., Fang S., Dong H., Xu C. Review of digital twin about concepts, technologies, and industrial applications. J. Manuf. Syst. 2021;58:346–361. doi: 10.1016/j.jmsy.2020.06.017. [DOI] [Google Scholar]
  • 46.Springer, Types of journal articles, Springer.(n.d.). https://www.springer.com/gp/authors-editors/authorandreviewertutorials/writing-a-journal-manuscript/types-of-journal-articles/10285504 (accessed April 16, 2021)..
  • 47.ISO, ISO 41001:2018(en) Facility management — Management systems — Requirements with guidance for use (n.d.). https://www.iso.org/obp/ui/#iso:std:iso:41001:ed-1:v1:en (accessed June 30, 2022)..
  • 48.National Library of Medicine, What is precision medicine?,(n.d.). https://medlineplus.gov/genetics/understanding/precisionmedicine/definition/(accessed June 30, 2022)..
  • 49.Singh M., Fuenmayor E., Hinchy E., Qiao Y., Murray N., Devine D. Digital twin: origin to future. Applied System Innovation. 2021;4:36. doi: 10.3390/asi4020036. [DOI] [Google Scholar]
  • 50.Rojas-Arce J.L., Ortega-Maldonado E.C. The advent of the digital twin. A Prospective in Healthcare in the Next Decade. 2021:249–255. doi: 10.1007/978-3-030-85910-7_26. [DOI] [Google Scholar]
  • 51.Gartner Inc Trends Drive the Gartner Hype Cycle for Emerging Technologies, 2020. 2021;5 https://www.gartner.com/smarterwithgartner/5-trends-drive-the-gartner-hype-cycle-for-emerging-technologies-2020 [Google Scholar]
  • 52.World Economic Forum . 2019. How Precision Medicine Will Change the Future of Healthcare, Technological Transformation.https://www.weforum.org/agenda/2019/01/why-precision-medicine-is-the-future-of-healthcare/#:∼:text=on%20data%20insights.-,Precision%20medicine%20allows%20doctors%20to%20select%20treatments%20based%20on%20a,every%20patient%20across%20various%20stages [Google Scholar]
  • 53.Armeni P., Polat I., de Rossi L.M., Diaferia L., Meregalli S., Gatti A. Digital twins in healthcare: is it the beginning of a new era of evidence-based medicine? A critical review. J Pers Med. 2022;12:1255. doi: 10.3390/jpm12081255. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 54.Laamarti F., Badawi H.F., Ding Y., Arafsha F., Hafidh B., el Saddik A. An ISO/IEEE 11073 standardized digital twin framework for health and well-being in smart cities. IEEE Access. 2020;8 doi: 10.1109/ACCESS.2020.2999871. –105961. [DOI] [Google Scholar]
  • 55.Liu Y., Zhang L., Yang Y., Zhou L., Ren L., Wang F., Liu R., Pang Z., Deen M.J. A novel cloud-based framework for the elderly healthcare services using digital twin. IEEE Access. 2019;7:49088–49101. doi: 10.1109/ACCESS.2019.2909828. [DOI] [Google Scholar]
  • 56.Stark K., Eder J., Zatloukal K. 2006. Priority-Based K-Anonymity Accomplished by Weighted Generalisation Structures; pp. 394–404. [DOI] [Google Scholar]
  • 57.Karakra A., Fontanili F., Lamine E., Lamothe J., Taweel A. Pervasive computing integrated discrete event simulation for a hospital digital twin. IEEE/ACS 15th International Conference on Computer Systems and Applications (AICCSA), IEEE. 2018:1–6. doi: 10.1109/AICCSA.2018.8612796. 2018. [DOI] [Google Scholar]
  • 58.Chakshu N.K., Sazonov I., Nithiarasu P. Towards enabling a cardiovascular digital twin for human systemic circulation using inverse analysis. Biomech. Model. Mechanobiol. 2021;20:449–465. doi: 10.1007/s10237-020-01393-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 59.Elayan H., Aloqaily M., Guizani M. Digital twin for intelligent context-aware IoT healthcare systems. IEEE Internet Things J. 2021;8:16749–16757. doi: 10.1109/JIOT.2021.3051158. [DOI] [Google Scholar]
  • 60.Mazumder O., Roy D., Bhattacharya S., Sinha A., Pal A. 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society. EMBC), IEEE; 2019. Synthetic PPG generation from haemodynamic model with baroreflex autoregulation: a Digital twin of cardiovascular system; pp. 5024–5029. 2019. [DOI] [PubMed] [Google Scholar]
  • 61.Angulo C., Antonio Ortega J., Gonzalez-Abril L. Frontiers in Artificial Intelligence and Applications. 2019. Towards a healthcare digital twin. [Google Scholar]
  • 62.Lutze R. IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC) IEEE; 2019. Digital twins in eHealth – : prospects and challenges focussing on information management; pp. 1–9. 2019. [DOI] [Google Scholar]
  • 63.Zhang J., Li L., Lin G., Fang D., Tai Y., Huang J. Cyber resilience in healthcare digital twin on lung cancer. IEEE Access. 2020;8:201900–201913. doi: 10.1109/ACCESS.2020.3034324. [DOI] [Google Scholar]
  • 64.Ali A., Dindoust D., Grant J., Clarke D. Delivering epilepsy care in low-resource settings: the role of technology. Expert Rev Med Devices. 2021;18:13–23. doi: 10.1080/17434440.2021.2013198. [DOI] [PubMed] [Google Scholar]
  • 65.Kendzierskyj S., Jahankhani H., Jamal A., Jimenez J.I. The transparency of big data. Data Harvesting and Digital Twins. 2019:139–148. doi: 10.1007/978-3-030-11289-9_6. [DOI] [Google Scholar]
  • 66.Lopatina K., Dokuchaev V.A., Maklachkova V.v. 2021 International Conference on Engineering Management of Communication and Technology (EMCTECH) IEEE; 2021. Data risks identification in healthcare sensor networks; pp. 1–7. [DOI] [Google Scholar]
  • 67.Mashaly M. Connecting the twins: a review on digital twin technology & its networking requirements. Procedia Comput. Sci. 2021;184:299–305. doi: 10.1016/j.procs.2021.03.039. [DOI] [Google Scholar]
  • 68.Norton B., Normann M.J. CEUR Workshop Proc. 2020. Digital twins and semantic data fusion for security in a healthcare environment. [Google Scholar]
  • 69.el Azzaoui A., Kim T.W., Loia V., Park J.H. 2021. Blockchain-Based Secure Digital Twin Framework for Smart Healthy City; pp. 107–113. [DOI] [Google Scholar]
  • 70.el Saddik A. 2020 Seventh International Conference on Social Networks Analysis, Management and Security (SNAMS) IEEE; 2020. Multimedia convergence for citizens' well-being; p. 1. 1. [DOI] [Google Scholar]
  • 71.Hatzivasilis G., Ioannidis S., Smyrlis M., Spanoudakis G., Frati F., Braghin C., Damiani E., Koshutanski H., Tsakirakis G., Hildebrandt T., Goeke L., Pape S., Blinder O., Vinov M., Leftheriotis G., Kunc M., Oikonomou F., Magilo G., Petrarolo V., Chieti A., Bordianu R. 2021 IEEE International Conference on Cyber Security and Resilience (CSR) IEEE; 2021. The THREAT-ARREST cyber range platform; pp. 422–427. [DOI] [Google Scholar]
  • 72.Zhang J., Tai Y. Secure medical digital twin via human-centric interaction and cyber vulnerability resilience. Connect. Sci. 2022;34:895–910. doi: 10.1080/09540091.2021.2013443. [DOI] [Google Scholar]
  • 73.Zheng Y., Lu R., Guan Y., Zhang S., Shao J. 2021 IEEE/ACM 29th International Symposium on Quality of Service (IWQOS) IEEE; 2021. Towards private similarity query based healthcare monitoring over digital twin cloud platform; pp. 1–10. [DOI] [Google Scholar]
  • 74.Legaspi J., Bhada S.V. Recent Trends and Advances in Model Based Systems Engineering. Springer International Publishing; Cham: 2022. Introducing digital doppelgängers for healthcare policy analysis; pp. 25–33. [DOI] [Google Scholar]
  • 75.McKendrick M., Yang S., McLeod G.A. The use of artificial intelligence and robotics in regional anaesthesia. Anaesthesia. 2021;76:171–181. doi: 10.1111/anae.15274. [DOI] [PubMed] [Google Scholar]
  • 76.Talukder A.K. Bridging the Inferential Gaps in Healthcare. 2021:31–43. doi: 10.1007/978-3-030-93620-4_3. [DOI] [Google Scholar]
  • 77.Yun S., Kim W. A healthcare digital twin platform architecture based on OneM2M standards. Basic Clin. Pharmacol. Toxicol. 2019;124:316. doi: 10.1111/bcpt.13217. 316. [DOI] [Google Scholar]
  • 78.Akash S.S., Ferdous M.S. A blockchain based system for healthcare digital twin. IEEE Access. 2022;10:50523–50547. doi: 10.1109/ACCESS.2022.3173617. [DOI] [Google Scholar]
  • 79.Garg H., Sharma B., Shekhar S., Agarwal R. Spoofing detection system for e-health digital twin using EfficientNet Convolution Neural Network. Multimed. Tool. Appl. 2022;81:26873–26888. doi: 10.1007/s11042-021-11578-5. [DOI] [Google Scholar]
  • 80.Gonzalez-Abril L., Angulo C., Ortega J.-A., Lopez-Guerra J.-L. Generative adversarial networks for anonymized healthcare of lung cancer patients. Electronics (Basel) 2021;10:2220. doi: 10.3390/electronics10182220. [DOI] [Google Scholar]
  • 81.Gupta D., Kayode O., Bhatt S., Gupta M., Tosun A.S. IEEE 7th International Conference on Collaboration and Internet Computing (CIC) IEEE; 2021. Hierarchical federated learning based anomaly detection using digital twins for smart healthcare; pp. 16–25. 2021. [DOI] [Google Scholar]
  • 82.Lutze R. Digital twin based software design in eHealth - a new development approach for health/medical software products. IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC), IEEE. 2020:1–9. doi: 10.1109/ICE/ITMC49519.2020.9198546. 2020. [DOI] [Google Scholar]
  • 83.Piacentino E., Angulo C. 2020. Generating Fake Data Using GANs for Anonymizing Healthcare Data; pp. 406–417. [DOI] [Google Scholar]
  • 84.Yu Z., Wang K., Wan Z., Xie S., Lv Z. IEEE Consumer Electronics Magazine; 2022. FMCPNN in Digital Twins Smart Healthcare. 1–8. [DOI] [Google Scholar]
  • 85.Firouzi F., Farahani B., Daneshmand M., Grise K., Song J., Saracco R., Wang L.L., Lo K., Angelov P., Soares E., Loh P.-S., Talebpour Z., Moradi R., Goodarzi M., Ashraf H., Talebpour M., Talebpour A., Romeo L., Das R., Heidari H., Pasquale D., Moody J., Woods C., Huang E.S., Barnaghi P., Sarrafzadeh M., Li R., Beck K.L., Isayev O., Sung N., Luo A. Harnessing the power of smart and connected health to tackle COVID-19: IoT, AI, robotics, and blockchain for a better world. IEEE Internet Things J. 2021;8:12826–12846. doi: 10.1109/JIOT.2021.3073904. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 86.Alves S.F.R., Uribe-Quevedo A., Chen D., Morris J., Radmard S. Developing a VR simulator for robotics navigation and human robot interactions employing digital twins. IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE. 2022:121–125. doi: 10.1109/VRW55335.2022.00036. 2022. [DOI] [Google Scholar]
  • 87.Ghita M., khadija M., Siham B., Mariam B., Hicham M. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability. WorldS4), IEEE; 2020. Digitalization against the new outbreak; pp. 707–714. [DOI] [Google Scholar]
  • 88.Droghini D., Squartini S., Principi E., Gabrielli L., Piazza F. Audio metric learning by using siamese autoencoders for one-shot human fall detection. IEEE Trans Emerg Top Comput Intell. 2021;5:108–118. doi: 10.1109/TETCI.2019.2948151. [DOI] [Google Scholar]
  • 89.Sahal R., Alsamhi S.H., Brown K.N., O'Shea D., Alouffi B. Blockchain-Based digital twins collaboration for smart pandemic alerting: decentralized COVID-19 pandemic alerting use case. Comput. Intell. Neurosci. 2022;2022:1–14. doi: 10.1155/2022/7786441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 90.Jovanović R., Davidović M., Lazović I., Jovanović M., Jovašević-Stojanović M. Modelling voluntary general population vaccination strategies during COVID-19 outbreak: influence of disease prevalence. Int. J. Environ. Res. Publ. Health. 2021;18:6217. doi: 10.3390/ijerph18126217. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 91.Trobinger M., Costinescu A., Xing H., Elsner J., Hu T., Naceri A., Figueredo L., Jensen E., Burschka D., Haddadin S. 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) IEEE; 2021. A dual doctor-patient twin paradigm for transparent remote examination, diagnosis, and rehabilitation; pp. 2933–2940. [DOI] [Google Scholar]
  • 92.Defraeye T., Bahrami F., Rossi R.M. Inverse mechanistic modeling of transdermal drug delivery for fast identification of optimal model parameters. Front. Pharmacol. 2021;12 doi: 10.3389/fphar.2021.641111. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 93.Haddadi K., Muller S., Bloch I. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI) IEEE; 2021. Validation of A Bi-energetic spectrum approximation in bone mineral density measurement with A dxa digital twin; pp. 380–384. [DOI] [Google Scholar]
  • 94.Pedersen A. Altering perceptions, visualizing sub-ground metal objects. Emerging Science Journal. 2020;4:205–213. doi: 10.28991/esj-2020-01224. [DOI] [Google Scholar]
  • 95.Xing X., del Ser J., Wu Y., Li Y., Xia J., Lei X., Firmin D., Gatehouse P., Yang G. HDL: hybrid deep learning for the synthesis of myocardial velocity maps in digital twins for cardiac analysis. IEEE J Biomed Health Inform. 2022:1. doi: 10.1109/JBHI.2022.3158897. –1. [DOI] [PubMed] [Google Scholar]
  • 96.Alcaraz J.C., Moghaddamnia S., Fuhrwerk M., Peissig J. Efficiency of the memory polynomial model in realizing digital twins for gait assessment. 2019 27th European Signal Processing Conference (EUSIPCO), IEEE. 2019:1–5. doi: 10.23919/EUSIPCO.2019.8903143. [DOI] [Google Scholar]
  • 97.Badawi H., Laamarti F., Arafsha F., el Saddik A. 2019. Standardizing a Shoe Insole Based on ISO/IEEE 11073 Personal Health Device (X73-PHD) Standards; pp. 764–778. [DOI] [Google Scholar]
  • 98.Conte Alcaraz J., Moghaddamnia S., Peissig J. Efficiency of deep neural networks for joint angle modeling in digital gait assessment. EURASIP J Adv Signal Process. 2021;2021:10. doi: 10.1186/s13634-020-00715-1. [DOI] [Google Scholar]
  • 99.Basaglia A., Spacone E., van de Lindt J.W., Kirsch T.D. A discrete-event simulation model of hospital patient flow following major earthquakes. Int. J. Disaster Risk Reduc. 2022;71 doi: 10.1016/j.ijdrr.2022.102825. [DOI] [Google Scholar]
  • 100.Bianchini A., Carcasci C., Manfrida G., Zini M. Reconstruction and analysis of the energy demand of a healthcare facility in Italy. E3S Web of Conferences. 2020;197 doi: 10.1051/e3sconf/202019702009. [DOI] [Google Scholar]
  • 101.Cheng W., Lian W., Tian J. Building the hospital intelligent twins for all-scenario intelligence health care. Digit Health. 2022;8 doi: 10.1177/20552076221107894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 102.Lu Q., Ye Z., Fang Z., Meng J., Pitt M., Lin J., Xie X., Chen L. 2021 Winter Simulation Conference (WSC) IEEE; 2021. Creating an inter-hospital resilient network for pandemic response based on blockchain and dynamic digital twins; pp. 1–12. [DOI] [Google Scholar]
  • 103.Nonnemann L., Haescher M., Aehnelt M., Bieber G., Diener H., Urban B. Health@Hand A visual interface for eHealth monitoring. 2019 IEEE Symposium on Computers and Communications (ISCC), IEEE. 2019:1093–1096. doi: 10.1109/ISCC47284.2019.8969647. [DOI] [Google Scholar]
  • 104.Xu L., de Vrieze P., Lu X., Wang W. 2022. Digital Twins Approach for Sustainable Industry; pp. 126–134. [DOI] [Google Scholar]
  • 105.Zhong X., Babaie Sarijaloo F., Prakash A., Park J., Huang C., Barwise A., Herasevich V., Gajic O., Pickering B., Dong Y. A multidisciplinary approach to the development of digital twin models of critical care delivery in intensive care units. Int. J. Prod. Res. 2022;60:4197–4213. doi: 10.1080/00207543.2021.2022235. [DOI] [Google Scholar]
  • 106.Karakra A., Lamine E., Fontanili F., Lamothe J. Proceedings of the 9th International Workshop on Innovative Simulation for Healthcare (IWISH 2020) CAL-TEK srl; 2020. HospiT'Win: a digital twin framework for patients' pathways real-time monitoring and hospital organizational resilience capacity enhancement; pp. 62–71. [DOI] [Google Scholar]
  • 107.Ricci A., Croatti A., Montagna S. IEEE Internet Comput; 2022. Pervasive and Connected Digital Twins – A Vision for Digital Health; p. 1. 1. [DOI] [Google Scholar]
  • 108.Toumi A., el Khoury R., Harb E., Nasrallah N. Modelling COVID-19 effect on the performance of MENA Health-care sector. J. Model. Manag. 2022 doi: 10.1108/JM2-01-2022-0004. [DOI] [Google Scholar]
  • 109.Augusto V., Murgier M., Viallon A. A modelling and simulation framework for intelligent control of emergency units in the case of major crisis. Winter Simulation Conference (WSC), IEEE. 2018:2495–2506. doi: 10.1109/WSC.2018.8632438. 2018. [DOI] [Google Scholar]
  • 110.Galli G., Patrone C., Bellam A.C., Annapareddy N.R., Revetria R. Lecture Notes in Engineering and Computer Science. 2019. Improving process using digital twin: a methodology for the automatic creation of models.https://www.scopus.com/inward/record.uri?eid=2-s2.0-85083371674&partnerID=40&md5=a4244de994fe8a4fb50485700b848957 [Google Scholar]
  • 111.Karakra A., Fontanili F., Lamine E., Lamothe J., HospiT'Win A predictive simulation-based digital twin for patients pathways in hospital. IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), IEEE. 2019:1–4. doi: 10.1109/BHI.2019.8834534. 2019. [DOI] [Google Scholar]
  • 112.Shaked A. Modeling for rapid systems prototyping: hospital situational awareness system design. Systems. 2021;9:12. doi: 10.3390/systems9010012. [DOI] [Google Scholar]
  • 113.Song Y., Li Y. Construction Research Congress 2022. American Society of Civil Engineers; Reston, VA: 2022. Digital twin aided healthcare facility management: a case study of Shanghai tongji hospital; pp. 1145–1155. [DOI] [Google Scholar]
  • 114.Wang Y., Wang F.-Y., Wang X., Guan Z., Ouyang L., Wang J., Yan L., Zheng W., Zhang W. 2021 IEEE 1st International Conference on Digital Twins and Parallel Intelligence (DTPI) IEEE; 2021. Parallel hospital: ACP-based hospital smart operating system; pp. 474–477. [DOI] [Google Scholar]
  • 115.Heininger R., Stary C. Proceedings of the 2021 ACM Workshop on Secure and Trustworthy Cyber-Physical Systems. ACM; New York, NY, USA: 2021. Capturing autonomy in its multiple facets: a digital twin approach; pp. 3–12. [DOI] [Google Scholar]
  • 116.Jimenez J.I., Jahankhani H., Kendzierskyj S. 2020. Health Care in the Cyberspace: Medical Cyber-Physical System and Digital Twin Challenges; pp. 79–92. [DOI] [Google Scholar]
  • 117.Lupton D. Language matters: the ‘digital twin’ metaphor in health and medicine. J. Med. Ethics. 2021;47:409. doi: 10.1136/medethics-2021-107517. 409. [DOI] [PubMed] [Google Scholar]
  • 118.Mozumder M.A.I., Sheeraz M.M., Athar A., Aich S., Kim H.-C. 2022 24th International Conference on Advanced Communication Technology (ICACT) IEEE; 2022. Overview: technology roadmap of the future trend of metaverse based on IoT, blockchain, AI technique, and medical domain metaverse activity; pp. 256–261. [DOI] [Google Scholar]
  • 119.Comaniciu D. Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM; New York, NY, USA: 2020. Artificial intelligence for healthcare; p. 3603. 3603. [DOI] [Google Scholar]
  • 120.Croatti A., Gabellini M., Montagna S., Ricci A. On the integration of agents and digital twins in healthcare. J. Med. Syst. 2020;44:161. doi: 10.1007/s10916-020-01623-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 121.Coorey G., Figtree G.A., Fletcher D.F., Redfern J. The health digital twin: advancing precision cardiovascular medicine. Nat. Rev. Cardiol. 2021;18:803–804. doi: 10.1038/s41569-021-00630-4. [DOI] [PubMed] [Google Scholar]
  • 122.Angulo C., Gonzalez-Abril L., Raya C., Ortega J.A. 2020. A Proposal to Evolving towards Digital Twins in Healthcare; pp. 418–426. [DOI] [Google Scholar]
  • 123.Kobayashi T., Fukae K., Imai T., Arai K. Digital twin agent for super-aged society. 2022 IEEE International Conference on Consumer Electronics (ICCE), IEEE. 2022:1–6. doi: 10.1109/ICCE53296.2022.9730230. [DOI] [Google Scholar]
  • 124.Laubenbacher R., Niarakis A., Helikar T., An G., Shapiro B., Malik-Sheriff R.S., Sego T.J., Knapp A., Macklin P., Glazier J.A. Building digital twins of the human immune system: toward a roadmap. NPJ Digit Med. 2022;5:64. doi: 10.1038/s41746-022-00610-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 125.Meraghni S., Benaggoune K., al Masry Z., Terrissa L.S., Devalland C., Zerhouni N. Towards Digital Twins Driven Breast Cancer Detection. 2021:87–99. doi: 10.1007/978-3-030-80129-8_7. [DOI] [Google Scholar]
  • 126.Shamanna P., Dharmalingam M., Sahay R., Mohammed J., Mohamed M., Poon T., Kleinman N., Thajudeen M. Retrospective study of glycemic variability, BMI, and blood pressure in diabetes patients in the Digital Twin Precision Treatment Program. Sci. Rep. 2021;11 doi: 10.1038/s41598-021-94339-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 127.Xie S., Zhu S., Dai J. 2021 International Conference on Computer Information Science and Artificial Intelligence. CISAI), IEEE; 2021. Feasibility study of intelligent healthcare based on digital twin and data mining; pp. 906–911. [DOI] [Google Scholar]
  • 128.Bruynseels K., de Sio F.S., van den Hoven J. Digital Twins in health care: ethical implications of an emerging engineering paradigm. Front. Genet. 2018 doi: 10.3389/fgene.2018.00031. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 129.Fagherazzi G. Deep digital phenotyping and digital twins for precision health: time to dig deeper. J. Med. Internet Res. 2020;22 doi: 10.2196/16770. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 130.Hose D.R., Lawford P.v., Huberts W., Hellevik L.R., Omholt S.W., van de Vosse F.N. Cardiovascular models for personalised medicine: where now and where next? Med. Eng. Phys. 2019;72:38–48. doi: 10.1016/j.medengphy.2019.08.007. [DOI] [PubMed] [Google Scholar]
  • 131.James L. Digital twins will revolutionise healthcare. Eng. Technol. 2021;16:50–53. doi: 10.1049/et.2021.0210. [DOI] [Google Scholar]
  • 132.Lareyre F., Adam C., Carrier M., Raffort J. Using digital twins for precision medicine in vascular surgery. Ann. Vasc. Surg. 2020;67 doi: 10.1016/j.avsg.2020.04.042. e577–e578. [DOI] [PubMed] [Google Scholar]
  • 133.Peirlinck M., Costabal F.S., Yao J., Guccione J.M., Tripathy S., Wang Y., Ozturk D., Segars P., Morrison T.M., Levine S., Kuhl E. Precision medicine in human heart modeling. Biomech. Model. Mechanobiol. 2021;20:803–831. doi: 10.1007/s10237-021-01421-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 134.Rivera L.F., Villegas N.M., Jiménez M., Tamura G., Angara P., Müller H.A. CASCON - Conference of the Centre for Advanced Studies on Collaborative Research - Annual International Conference on Computer Science and Software Engineering - Proceedings. 2020. Towards continuous monitoring in personalized healthcare through digital twins. [Google Scholar]
  • 135.Thiong’o G.M., Rutka J.T. Digital twin technology: the future of predicting neurological complications of pediatric cancers and their treatment. Front. Oncol. 2022;11 doi: 10.3389/fonc.2021.781499. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 136.Alrashed S., Min-Allah N., Ali I., Mehmood R. COVID-19 outbreak and the role of digital twin. Multimed. Tool. Appl. 2022;81:26857–26871. doi: 10.1007/s11042-021-11664-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 137.Barbiero P., Viñas Torné R., Lió P. Graph representation forecasting of patient's medical conditions: toward a digital twin. Front. Genet. 2021;12 doi: 10.3389/fgene.2021.652907. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 138.Comito C., Forestiero A., Papuzzo G. IEEE/WIC/ACM International Conference on Web Intelligence - Companion Volume. ACM; New York, NY, USA: 2019. Exploiting social media to enhance clinical decision support; pp. 244–249. [DOI] [Google Scholar]
  • 139.Fu W., Sharma S., Abadi E., Iliopoulos A.-S., Wang Q., Lo J.Y., Sun X., Segars W.P., Samei E. Phantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry. IEEE J Biomed Health Inform. 2021;25:3061–3072. doi: 10.1109/JBHI.2021.3063080. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 140.Lombardo A., Ricci C. Digital twins federation for remote medical care of de-hospitalized patients. IEEE International Conference on Pervasive Computing and Communications Workshops and Other Affiliated Events (PerCom Workshops), IEEE. 2022:718–723. doi: 10.1109/PerComWorkshops53856.2022.9767417. 2022. [DOI] [Google Scholar]
  • 141.Mohapatra S., Bose S. An appraisal of literature for design and implementation of developing a framework for digital twin and validation through case studies. Health Technol. 2020;10:1229–1237. doi: 10.1007/s12553-020-00443-4. [DOI] [Google Scholar]
  • 142.Ossai C.I., Wickramasinghe N. Annual Americas Conference on Information Systems. AMCIS) - Proceedings; 2021. A Bayesian network model to establish a digital twin architecture for superior falls risk prediction. [Google Scholar]
  • 143.Petrova-Antonova D., Spasov I., Krasteva I., Manova I., Ilieva S. 2020. A Digital Twin Platform for Diagnostics and Rehabilitation of Multiple Sclerosis; pp. 503–518. [DOI] [Google Scholar]
  • 144.Wickramasinghe N., Jayaraman P.P., Zelcer J., Forkan A.R.M., Ulapane N., Kaul R., Vaughan S. IEEE Internet Comput; 2021. A Vision for Leveraging the Concept of Digital Twins to Support the Provision of Personalised Cancer Care; p. 1. 1. [DOI] [Google Scholar]
  • 145.Dillenseger A., Weidemann M.L., Trentzsch K., Inojosa H., Haase R., Schriefer D., Voigt I., Scholz M., Akgün K., Ziemssen T. Digital biomarkers in multiple sclerosis. Brain Sci. 2021;11:1519. doi: 10.3390/brainsci11111519. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 146.Khan A., Milne-Ives M., Meinert E., Iyawa G.E., Jones R.B., Josephraj A.N. A scoping review of digital twins in the context of the covid-19 pandemic. Biomed. Eng. Comput. Biol. 2022;13 doi: 10.1177/11795972221102115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 147.Powell D., Stuart S., Godfrey A. Sports related concussion: an emerging era in digital sports technology. NPJ Digit Med. 2021;4:164. doi: 10.1038/s41746-021-00538-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 148.Yang J., Joong-Sook L. Utilization exercise rehabilitation using metaverse. Korean Journal of Sport Biomechanics. 2021 [Google Scholar]

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