@revista_internacional{1029,
  keywords = {Multi-criteria group decision-making, Consensus reaching process, Linguistic 2-tuples representation model, CRITIC-DNMA method, Medical digital twins},
  author = {Qun Wu and Weiqi Tan and Ligang Zhou and Muhammet Deveci and Weiping Ding and Luis Martínez and Dragan Pamucar},
  title = {An integrated consensus-oriented decision-making framework for exploring the barriers and applications of medical digital twins},
  abstract = {With improvements in medical data collection technology and the refinement of medical big data, digital twin (DT) technology has increasingly been applied in the medical field. A DT is a digital representation of physical objects, processes, and systems. The rapid iteration of technologies such as mathematical modeling, artificial intelligence, cloud computing, blockchain, and the Internet of Things has made the development of medical digital twins (MDTs) possible, showing immense potential for growth in the medical industry. Although MDT application has evolved considerably, it still faces numerous barriers, and specific MDT application fields remain unclear. This research presents a novel integrated consensus-oriented MCGDM framework under uncertain linguistic environments for exploring MDT barriers and applications. First, the linguistic 2-tuple representation model is used to quantify the uncertain linguistic semantics in the decision-making procedure. Second, a two-stage 2-tuple linguistic consensus optimization model with minimum adjustment rules is presented to aggregate individual opinions to derive more scientific and efficient group opinions. Third, the objective and reasonable Criteria Importance Through Inter-criteria Correlation (CRITIC) method combined with subjective weights and the flexible and reliable double normalization-based multiple aggregation (DNMA) method are incorporated into the 2-tuple linguistic environment to prioritize the MDT barriers and application fields, respectively. Finally, a case study with a comprehensive evaluation system to prioritize MDT barriers and application fields is utilized to test the effectiveness and practicality of the presented decision support framework. The results show that technology and talent shortage (0.2153) has the highest levels among all the barriers, and prognosis prediction and chronic disease management (0.5474) is the most pervasive and easy-to-implement MDT application field. The findings of this article may enrich the theories of uncertain MCGDM methods and offer references for the practical operation and management of MDTs.},
  year = {2025},
  journal = {Expert Systems with Applications},
  volume = {284},
  pages = {127683},
  issn = {0957-4174},
  url = {https://www.sciencedirect.com/science/article/pii/S0957417425013053},
  doi = {https://doi.org/10.1016/j.eswa.2025.127683},
}