J Med Internet Res. 2026 Feb 2. doi: 10.2196/73612. Online ahead of print.
ABSTRACT
BACKGROUND: Rare diseases (RDs) affect over 300 million people globally, and only about 5% have approved therapies. Lysosomal storage disorders (LSDs) exemplify the diagnostic and long-term care complexity typical of RDs, and digital health technologies (DHTs), especially artificial intelligence (AI) and connected care (CC), are increasingly reported as tools to support LSD management.
OBJECTIVE: This scoping review maps and synthesizes peer-reviewed and grey literature from the past decade on DHTs relevant for LSD care, with a primary analytic focus on AI-enabled and CC solutions, and a contextual mapping of other enabling DHTs. Evidence distribution is charted by population, care-journey phase, and outcome domains to identify gaps, methodological limitations, and timely priorities relevant for research, clinical practice implementation, and policies.
METHODS: We conducted a scoping review guided by a Population-Concept-Context (PCC) framework and operationalised through a PICO-informed data-charting structure to map study characteristics and reported outcomes, without causal or effectiveness assumptions and without risk-of-bias assessment. We searched PubMed/MEDLINE, Google Scholar, and ClinicalTrials.gov for studies published between October 2015 and September 2024, complemented by AI-assisted discovery tools (Consensus, SciSpace, Connected Papers) for citation extension. Reproducibility logs (search strings, run dates, filters, and stepwise counts) were maintained. Of 1,751 records retrieved, 245 were included. Evidence was charted by LSD population, intervention class (artificial intelligence, connected care, and other enabling digital health technologies), outcome domains (patient, healthcare, societal), and phase of the care journey.
RESULTS: Amongst 245 included records, 92.2% were peer-reviewed and 7.8% were grey literature; no completed and published randomized controlled trials or LSD-specific systematic reviews were identified, with evidence dominated by small, single-center observational studies. 40 peer-reviewed records reported AI-driven DHTs, 89 reported CC DHTs, and 144 reported other enabling DHTs (some multi-labeled). Evidence was concentrated mostly in Gaucher and Fabry diseases. Nearly half of the mapped literature focused on screening/diagnosis, with fewer records addressing treatment intensification, rehabilitation, and end-of-life care. Outcomes were predominantly healthcare-delivery performance measures, with fewer patient and societal outcomes. AI applications mainly supported diagnostic decision support, phenotyping, monitoring/progression tracking, and risk stratification; CC commonly involved telemedicine, remote monitoring, and patient-engagement platforms; enabling DHTs included interoperable data systems, registries, and digital infrastructures.
CONCLUSIONS: The evidence base is appreciable for a niche field and reflects growing interest in AI and CC for LSD care, but heterogeneity and methodological limitations preclude inferences on effectiveness or routine implementation. This evidence map highlights relatively stronger areas and clear gaps, providing a structured foundation to inform subsequent expert consensus-building and research prioritization. Key priorities include interoperable data infrastructures and data availability, prospective multicenter evaluations, transparent reporting of algorithms and workflows, and implementation-relevant outcomes to support safe, equitable, and scalable adoption aligned with evolving EU and global rare-disease priorities.
PMID:41661224 | DOI:10.2196/73612