Digital twins in pediatric metabolic dysfunction-associated steatotic liver disease: Developmentally referenced multimodal risk stratification

Table 1 Traditional pediatric metabolic dysfunction-associated steatotic liver disease monitoring compared with an author-proposed investigational digital-twin approach

Dimension	Traditional monitoring	Author-proposed investigational digital-twin approach
Data domains	Periodic anthropometrics, liver tests, and selected imaging	Minimum viable core of time-stamped developmental, clinical, biochemical, treatment, and clinically obtained imaging data; optional advanced imaging, body composition, genetics/omics, microbiome, and digital context modules
Developmental reference	Age or sex considered at individual visits	Age-, sex-, growth-, and puberty-referenced trajectories when appropriate
Update frequency	Clinic visits at fixed intervals; tests repeated when indicated	Prospective synchronization when validated new data arrive; cadence depends on data type and intended use
Primary output	Thresholds, population risk groups, and clinician synthesis	Candidate association-based trajectory or calibrated risk range with uncertainty and main drivers; not for care until prospectively validated
Therapeutic use	Retrospective assessment of response	Hypothesis-generating scenario comparison only; no individualized treatment-effect claim without a separately validated causal model
Requirements and risks	Established workflows but fragmented longitudinal context	Interoperable records, label-quality controls, explicit missing-not-at-random handling, external validation, explainability, governance, equity monitoring, and safeguards against false alerts, false reassurance, and alert fatigue

The digital-twin column describes an author-proposed research framework, not validated clinical guidance.

Table 2 Evidence hierarchy for claims in this review

Evidence tier	Evidence source	What it supports in this review	What it does not establish	Ref.
Tier 1: Direct	Pediatric MASLD	Candidate clinical variables, observed associations, epidemiology, ALT/histology limitations, imaging, body composition, genetics, cardiometabolic outcomes, microbiome, and social context	A validated pediatric MASLD digital twin; calibrated individualized twin-based risk; adaptive follow-up benefit; causal treatment-effect simulation	[2-10,33-44,57-61]
Tier 2: Extrapolated	Adult MASLD or metabolic disease	Biological plausibility, adult feasibility of digital-twin-enabled metabolic management, and selected adult prognostic or genetic concepts	Pediatric calibration, safety, developmental validity, or pediatric treatment effects	[21,30,31,46,56]
Tier 3: Pediatric extrapolation	Other pediatric diseases/child-health digital twins	Developmental referencing, pediatric digital-twin feasibility, family perspectives, ethics, and child-centered implementation principles	MASLD-specific predictive performance or clinical utility	[11,22-27]
Tier 4: Methodological	Generic digital twins, AI, prediction-model methods, regulation, and interoperability	Definitions, architecture, explainability, federated learning, validation/reporting methods, governance, interoperability, and regulatory principles	Clinical validity or effectiveness in pediatric MASLD	[12-20,28,29,32,62-82]

Evidence tiers reflect directness to pediatric metabolic dysfunction-associated steatotic liver disease (MASLD) digital-twin clinical use, not an assessment of study quality. Evidence from Tiers 2-4 is extrapolative and should not be interpreted as validation of pediatric MASLD digital-twin outputs. AI: Artificial intelligence; ALT: Alanine aminotransferase; MASLD: Metabolic dysfunction-associated steatotic liver disease.

Table 3 Author-proposed evidence pathway for evaluating pediatric metabolic dysfunction-associated steatotic liver disease digital twins before clinical use

Phase	Purpose	Evidence focus	Readiness signal
1a	Model specification and retrospective development	Define intended use, target population, outcome, minimum viable core, label provenance, biopsy-selection bias, missing-data assumptions, and subgroup analysis before modeling	Model and outputs are fully specified; internal validation and subgroup calibration justify prospective observation, not clinical use
2b	Silent prospective evaluation	Run a locked model in real time without influencing care; measure calculability, missingness, calibration, false alerts, false reassurance, equity, and workflow burden	Prospective error profile and workflow burden are characterized, with no care decisions driven by the model
3c	External and temporal validation	Test independent sites and later time periods; assess recalibration, subgroup performance, and incremental value of optional modules	Performance remains calibrated and clinically interpretable across settings and time
4d	Prospective decision-support evaluation	Test clinician-supervised outputs; assess net benefit, safety, downstream testing, alert fatigue, clinician/family understanding, consent, and equity	The tool improves or safely supports decisions beyond usual care without unacceptable harms or disparities
5e	Controlled updating and scaling	Use version control and governed updates; monitor drift, bias, cybersecurity, safety, performance, and model cards; federated scaling may be used when appropriate	Performance remains stable during monitored updates under an appropriate regulatory and governance process

Phase 1a: TRIPOD + AI[70], PROBAST + AI[75], MI-CLAIM[77], and MINIMAR[78] inform model specification, transparent reporting, and risk-of-bias assessment. Phase 2b: DECIDE-AI[81] and FUTURE-AI[76] inform early prospective workflow evaluation; FHIR[74] supports interoperable data exchange where applicable. Phase 3c: External-validation principles are informed by Riley et al[68], TRIPOD + AI[70], and PROBAST + AI[75]. Phase 4d: SPIRIT-AI[79], CONSORT-AI[80], DECIDE-AI[81], and STARD-AI[82] apply according to study design and intended use. Phase 5e: FDA predetermined change control plan guidance[71], IMDRF good machine learning practice[72], FUTURE-AI[76], and the European Union Artificial Intelligence Act[73] inform controlled updating and oversight as applicable to jurisdiction and product classification. This phased pathway is an author-proposed investigational framework synthesized from existing methodological, reporting, regulatory, and interoperability guidance. It is not a validated staging system or a regulatory pathway.