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

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.