Table 4.
Components of the review of papers.
| Publication type | Author(s) | Category | Year of publication | Publisher | Digital Twin model | Intended user/service |
|---|---|---|---|---|---|---|
| Journal | Mohapatra and Bose 23 | Review | 2020 | Springer Link Health Technol | A framework for digital twin implementation in the healthcare industry | Service improvement aimed at general public |
| Journal | Corral-Acero et al 24 | Review | 2020 | European Heart Journal | Application of DT to accelerate cardiovascular research and enable the vision of precision medicine | Clinician and patient |
| Chapter | Bagaria et al 25 | Review | 2020 | Springer Nature | DT for personal health and well-being | General public |
| Journal | Bruynseels et al 26 | Review | 2018 | Frontiers in Genetics | Digital twins privacy in healthcare technologies and ethics of biomedical data | General public and clinician |
| Journal | Liu et al 28 | Framework/Model | 2019 | IEEE Access | Cloud-Based Framework for the Elderly Healthcare Services Using Digital Twin | Elderly population |
| Journal | Rodríguez-Aguilar and Marmolejo-Saucedo 15 | Framework/Model | 2020 | EAI Endorsed Transactions on Pervasive Health and Technology | DT of the public emergency system | General public |
| Journal | Laamartet al 29 | Framework/ Model | 2020 | IEEE Access | QoE-based DT framework for health and well-being in smart cities | General public |
| Conference | Rivera et al 27 | Framework/Model | 2019 | ACM Digital Library | A DT reference model in healthcare for decision-making processes when applying medical treatments to patients by healthcare professionals. | General public |
| Conference | Lutze 30 | Framework/Model | 2019 | IEEE | A DT ehealth system focussing on information management | General public |
| PhD thesis | Albraikan 31 | Framework/Model | 2019 | University of Ottawa, Canada | A DT model for emotional well-being | General public/patient with non-clinical mental health issues |
| Journal | Björnsson et al 9 | Framework/Model | 2019 | Genome Med | Personalised medicine framework | General public |
| Journal | Croatti et al 12 | Framework/Model | 2020 | Med Syst. | Agent-based DTs to manage severe traumas | Trauma patient/Clinician |
| Journal | Chakshu et al 32 | Disease improvement | 2019 | Int J Numer Method Biomed Eng. | DT to detect the severity of carotid stenosis from head vibration | Clinician/patient |
| Preprint article | Rao and Mane 33 | Disease improvement | 2019 | Persistent Systems Ltd. | Customised per-patient DT model in liver disease diagnosis using Domain Knowledge and Machine Learning | Clinician/patient |
| Journal | Grosman et al 34 | Disease management | 2020 | Diabetes | DT programme to personalise MiniMed™ 670G settings in Type 1 diabetes patients | Clinician/patient |
| Journal | Walsh et al 14 | Disease improvement | 2020 | bioRxiv | DT for multiple sclerosis using probabilistic neural networks | Clinician/patient |
| Conference | Martinez-Velazquez et al 36 | Disease improvement | 2019 | IEEE | DT for cardiac patients | Clinician/patient |
| Conference | Mazumder et al 35 | Disease improvement | 2019 | IEEE | DT for Cardio-vascular patients | Clinician/patient |