Artificial intelligence applications using patient-generated health data for pre-care processes in elective healthcare: a systematic review

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.