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Digital Twins for Clinical and Operational Decision-Making: Scoping Review.

Vahid Riahi1, Ibrahima Diouf1, Sankalp Khanna2

  • 1Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation, Melbourne, Australia.

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Summary
This summary is machine-generated.

Digital twins (DTs) in healthcare show promise for improving clinical and operational decisions. While many applications focus on decision-making, current systems do not yet fully utilize DT capabilities.

Keywords:
CDMODMclinical decision-makingdigital twinhealth careoperational decision-makingscoping review

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Area of Science:

  • Digital Health
  • Health Informatics
  • Applied Artificial Intelligence

Background:

  • The healthcare industry is increasingly adopting digital technologies to address evolving challenges.
  • Digital twins (DTs), virtual representations interacting in real-time with physical counterparts, are gaining attention for their transformative potential in healthcare.
  • Despite diverse applications like personalized care and treatment optimization, a clear understanding of DTs' definition and implementation in health systems is lacking.

Purpose of the Study:

  • To review the applications of Digital Twins (DTs) in healthcare, specifically for clinical decision-making (CDM) and operational decision-making (ODM).
  • To establish a definition and framework for DTs by examining their core elements and characteristics.
  • To assess the current advancements and scope of DT applications supporting CDM and ODM based on the defined DT characteristics.

Main Methods:

  • A scoping review was conducted following the PRISMA-ScR protocol.
  • Searches were performed across multiple databases (PubMed, MEDLINE, Scopus) for original research on DTs in health system CDM and ODM.
  • Studies proposing only ideas or frameworks, or lacking experimental data, were excluded. Data on DT characteristics, testing environments, and methods were collated and analyzed using descriptive statistics.

Main Results:

  • Of 5537 papers, 86 (1.55%) met inclusion criteria, all published post-2017.
  • The majority of studies focused on clinical decision-making (CDM) (75/86). Mathematical modeling and simulation were the most common methods.
  • Key application areas included circulatory system diseases, health status, and endocrine/nutritional/metabolic diseases. Only 19% of studies were tested in real environments; most used simulations. Developed systems have not yet realized the full potential of DTs.

Conclusions:

  • This review offers a comprehensive overview of Digital Twin (DT) applications in healthcare for clinical and operational decision-making (CDM/ODM).
  • A novel framework defining DT elements and characteristics in healthcare is presented.
  • DT applications show promise for future roles in disease diagnosis, prevention, clinical trial design, and personalized, optimized treatments.