ArticleJournal of medical Internet research2020
Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07733752 (When AI Is the First Clinician), which is not on this map. Cited by 76 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
When AI Is the First Clinician: Impact of Pre-Visit AI Use on Presentation, Diagnostic Expectations, and Shared Decision-Making in Spine Physical Therapy
Who cites it
76 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Barriers and Facilitators to Patient Acceptance of Artificial Intelligence in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial Intelligence in Head and Neck Cancer: A Systematic Review of Systematic Reviews.Advances in therapy · 2023Pooled it
- Triage and Diagnostic Accuracy of Online Symptom Checkers: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Artificial Intelligence Techniques That May Be Applied to Primary Care Data to Facilitate Earlier Diagnosis of Cancer: Systematic Review.Journal of medical Internet research · 2021Pooled it
- Assessing the impact of information on patient attitudes toward artificial intelligence-based clinical decision support (AI/CDS): a pilot web-based SMART vignette study.Journal of medical ethics · 2025Trial
- Impact of a Symptom Checker App on Patient-Physician Interaction Among Self-Referred Walk-In Patients in the Emergency Department: Multicenter, Parallel-Group, Randomized, Controlled Trial.Journal of medical Internet research · 2025Trial
- Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial.International journal of environmental research and public health · 2023Trial
- Diagnostic accuracy of an artificial intelligence online engine in migraine: A multi-center study.Headache · 2022Trial
- Patient's Perception of Digital Symptom Assessment Technologies in Rheumatology: Results From a Multicentre Study.Frontiers in public health · 2022Trial
- Evaluating digital triage symptom checker with historical triage-related adverse events.Scandinavian journal of primary health care · 2026Article
- Factors affecting nurses' acceptance of a digital triage platform in primary health care in Sweden: an extended UTAUT analysis.Scandinavian journal of primary health care · 2026Article
- Comparative Evaluation of Large Language Models in Responding to Common Misconceptions Regarding Periodontal Disease and Oral Hygiene.Diagnostics (Basel, Switzerland) · 2026Article
- Authentication status and AI triage concordance among care seekers in a US health system.npj health systems · 2026Article
- The trust in AI-generated health advice (TAIGHA) scale and short version (TAIGHA-S): Development and validation study.PLOS digital health · 2026Article
- Patient-Provider Discussions About Online Health Information in the Health Information National Trends Survey.Health education & behavior : the official publication of the Society for Public Health Education · 2026Article
- Evaluating the accuracy of ChatGPT model versions for giving care-seeking advice.Communications medicine · 2026Article
- Health sciences students' attitudes toward artificial intelligence: predictors of ethical awareness, clinical decision-making, and public health perceptions-a cross-sectional study.BMC medical education · 2026Article
- Artificial intelligence in acne vulgaris patients.Revista da Associacao Medica Brasileira (1992) · 2026Article
- From fragmented neurotechnologies to closed-loop brain health systems: integrating artificial intelligence, digital health, digital twins, and advanced materials for brain disorders.Frontiers in bioengineering and biotechnology · 2026Article
- Public attitudes and practices toward using AI chatbots for healthcare assistance: a multinational cross-sectional study.BMC health services research · 2025Article
16 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundPatients are increasingly seeking Web-based symptom checkers to obtain diagnoses. However, little is known about the characteristics of the patients who use these resources, their rationale for use, and whether they find them accurate and useful.
objectiveThe study aimed to examine patients' experiences using an artificial intelligence (AI)-assisted online symptom checker.
methodsAn online survey was administered between March 2, 2018, through March 15, 2018, to US users of the Isabel Symptom Checker within 6 months of their use. User characteristics, experiences of symptom checker use, experiences discussing results with physicians, and prior personal history of experiencing a diagnostic error were collected.
resultsA total of 329 usable responses was obtained. The mean respondent age was 48.0 (SD 16.7) years; most were women (230/304, 75.7%) and white (271/304, 89.1%). Patients most commonly used the symptom checker to better understand the causes of their symptoms (232/304, 76.3%), followed by for deciding whether to seek care (101/304, 33.2%) or where (eg, primary or urgent care: 63/304, 20.7%), obtaining medical advice without going to a doctor (48/304, 15.8%), and understanding their diagnoses better (39/304, 12.8%). Most patients reported receiving useful information for their health problems (274/304, 90.1%), with half reporting positive health effects (154/302, 51.0%). Most patients perceived it to be useful as a diagnostic tool (253/301, 84.1%), as a tool providing insights leading them closer to correct diagnoses (231/303, 76.2%), and reported they would use it again (278/304, 91.4%). Patients who discussed findings with their physicians (103/213, 48.4%) more often felt physicians were interested (42/103, 40.8%) than not interested in learning about the tool's results (24/103, 23.3%) and more often felt physicians were open (62/103, 60.2%) than not open (21/103, 20.4%) to discussing the results. Compared with patients who had not previously experienced diagnostic errors (missed or delayed diagnoses: 123/304, 40.5%), patients who had previously experienced diagnostic errors (181/304, 59.5%) were more likely to use the symptom checker to determine where they should seek care (15/123, 12.2% vs 48/181, 26.5%; P=.002), but they less often felt that physicians were interested in discussing the tool's results (20/34, 59% vs 22/69, 32%; P=.04).
conclusionsDespite ongoing concerns about symptom checker accuracy, a large patient-user group perceived an AI-assisted symptom checker as useful for diagnosis. Formal validation studies evaluating symptom checker accuracy and effectiveness in real-world practice could provide additional useful information about their benefit.
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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.