Int J Med Inform. 2026 Jun 3;218:106525. doi: 10.1016/j.ijmedinf.2026.106525. Online ahead of print.
ABSTRACT
PURPOSE: Artificial intelligence (AI) can leverage patient-generated health data (PGHD) to support pre-care processes such as triage, symptom assessment, and history-taking. Existing systematic reviews have examined AI clinical decision support, PGHD use, and AI for specific data modalities as separate domains, but none addresses their intersection for pre-care. We aimed to map AI methods and PGHD modalities, synthesize outcomes across technical, clinical, operational, user experience, and equity domains, and identify barriers to deployment and gaps in reporting.
METHODS: This systematic review was conducted in accordance with the PRISMA 2020 statement and prospectively registered with PROSPERO (CRD420251134235). We searched PubMed, MEDLINE, and Web of Science (January 2020-June 2025) for studies evaluating AI applications using PGHD to support pre-care processes in elective care. Risk of bias was assessed using validated tools appropriate to each study design. Narrative synthesis addressed heterogeneity across outcome domains.
RESULTS: Twenty-one studies analyzed PGHD from free text (38%), questionnaires (33%), voice recordings (14%), wearables (10%), and images (5%). Most used classical machine learning (67%), with deep learning present in 43% of studies and large language models emerging recently (14%). Model performance appeared promising, with area under the curve values ranging from 0.64 to 0.98 (median 0.78). However, this evidence has serious limitations: risk of bias was high in 95% of studies, external validation occurred in only 6% of evaluations, and clinical outcomes were measured in just one study. Equity was assessed in only 14% of studies. No study demonstrated patient benefit or described routine clinical deployment.
CONCLUSION: Current evidence establishes proof-of-concept but not proof-of-benefit. The field requires a methodological shift from algorithm development toward prospective validation, clinical outcome measurement, and equity assessment before deployment can be justified.
PMID:42250455 | DOI:10.1016/j.ijmedinf.2026.106525