ArticleNPJ digital medicine2025
Accuracy of online symptom assessment applications, large language models, and laypeople for self-triage decisions.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed.
- Evaluating digital triage symptom checker with historical triage-related adverse events.Scandinavian journal of primary health care · 2026Article
- Article
- Agreement and Classification Performance of a Mobile Self-Triage Application Based on the Manchester Triage System in Hemodynamically Stable Adult Walk-In Emergency Department Patients: A Prospective Observational Study.Journal of clinical medicine · 2026Article
- Patient-Facing AI System for Symptom Guidance Using Simulated Encounters and Physician Review: Analytical Validation Study.JMIR formative research · 2026Article
- Generative Artificial Intelligence (AI)-Assisted Self-Diagnosis and the Patient-Physician Relationship: Mixed Methods Study of Calibration, Participation, and Trust.Journal of medical Internet research · 2026Article
- Digital Dental Triage and Access to Care: A Qualitative Study of Experiences in a Teaching Hospital.Dentistry journal · 2026Article
- Health equity and public acceptance of large language models in healthcare in China: A national population-based survey.PLOS digital health · 2026Article
- Personal Health Large Language Models and the Negotiation of Medical Authority in Clinical Care: Opportunities, Risks, and Governance.Journal of medical Internet research · 2026Article
- Patient Cognitive Bias in Large Language Model-Supported Health Consultations: Simulation-Based Comparative Study.Journal of medical Internet research · 2026Article
- A light-weight symptom checker and its methodological validation.Veterinary research communications · 2026Article
- Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.JMIR biomedical engineering · 2026Article
- Metacognition and diagnostic decision-making: short "blips" of knowledge and the consequences of overconfidence.Cognitive research: principles and implications · 2026Article
- Article
- Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice.Communications medicine · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Independent and collaborative performance of large language models and healthcare professionals in diagnosis and triage.NPJ digital medicine · 2026Article
- How to Evaluate the Accuracy of Symptom Checkers and Diagnostic Decision Support Systems: Symptom Checker Accuracy Reporting Framework (SCARF).JMIR human factors · 2026Article
- Editorial: The promise of sleep technology.Frontiers in neuroscience · 2026Article
- Two-stage prompting framework with predefined verification steps for evaluating diagnostic reasoning tasks on two datasets.NPJ digital medicine · 2025Article
- Ten years on: how far have we come in patient engagement in diagnosis?Diagnosis (Berlin, Germany) · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Symptom-Assessment Application (SAAs, e.g., NHS 111 online) that assist laypeople in deciding if and where to seek care (self-triage) are gaining popularity and Large Language Models (LLMs) are increasingly used too. However, there is no evidence synthesis on the accuracy of LLMs, and no review has contextualized the accuracy of SAAs and LLMs. This systematic review evaluates the self-triage accuracy of both SAAs and LLMs and compares them to the accuracy of laypeople. A total of 1549 studies were screened and 19 included. The self-triage accuracy of SAAs was moderate but highly variable (11.5-90.0%), while the accuracy of LLMs (57.8-76.0%) and laypeople (47.3-62.4%) was moderate with low variability. Based on the available evidence, the use of SAAs or LLMs should neither be universally recommended nor discouraged; rather, we suggest that their utility should be assessed based on the specific use case and user group under consideration.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.