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Table 3

From: Towards the future of personalized medicine: digital twin technology

Table 3. Disease-specific digital-twin applications and acceptance considerations.

Condition	Digital-twin configuration	Control mode	Patient acceptance factors
Diabetes (chronic metabolic disorder)	Real-time glucose–insulin modeling with continuous sensor input	Human-led decisions with automated alerts and suggested actions	High acceptance when autonomy is preserved; usefulness tied to reducing burden and uncertainty
Cardiac rehabilitation (post-acute recovery)	Predictive modeling of exercise tolerance, cardiovascular load, and risk events	Shared control between clinicians, patients, and twins	Acceptance depends on explainability, safety monitoring, and clinician endorsement
Oncology (complex high-stakes treatment)	Multimodal simulation for treatment effects and toxicity prediction	Clinician-governed control with a twin providing decision support	Patients prefer physician authority; emotional reassurance and transparency are essential

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Declarations

Author contributions

KM: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. SB: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. Both authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

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