What Is Really Real? A Commentary on Digital Patient and Digital Twin Technologies

Digital patient technologies promise personalized healthcare but remain limited by inconsistent terminology and uncertain clinical readiness. Drawing on three companion preprints using the same 61-review evidence map, we argue that operational characteristics determine the computational evidence and clinical accountability required for a stated use. Patient specificity appeared in 55/61 reviews, bidirectionality in 26/61 and patient specificity, dynamic orientation and bidirectionality together in 25/61. These are review-level reporting patterns, not verified capabilities or patient benefit. Clear capabilities, system-level validation, semantic interoperability, governance and prospective clinical evidence remain priorities.

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Publication Details

Journal
Digital Health and Innovation
Published
2026-11-11
DOI
https://doi.org/10.3390/dhi1020009
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

What Is Really Real? A Commentary on Digital Patient and Digital Twin Technologies

Arash Ghorbannia, Charles Donald Combs, Taryn T. Cuper
Digital Health and Innovation
Artificial Intelligence in Healthcare and Education
article

What Is Really Real? A Commentary on Digital Patient and Digital Twin Technologies

Arash Ghorbannia, Charles Donald Combs, Taryn T. Cuper
article en

Abstract

Digital patient technologies promise personalized healthcare but remain limited by inconsistent terminology and uncertain clinical readiness. Drawing on three companion preprints using the same 61-review evidence map, we argue that operational characteristics determine the computational evidence and clinical accountability required for a stated use. Patient specificity appeared in 55/61 reviews, bidirectionality in 26/61 and patient specificity, dynamic orientation and bidirectionality together in 25/61. These are review-level reporting patterns, not verified capabilities or patient benefit. Clear capabilities, system-level validation, semantic interoperability, governance and prospective clinical evidence remain priorities.

Digital Health and InnovationVol. 1(2)
Old Dominion University (US)
Artificial Intelligence in Healthcare and Education
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