Evidence map›Paper›PMID 32012052›Full record

ArticleJournal of medical Internet research2020

Patient Perspectives on the Usefulness of an Artificial Intelligence-Assisted Symptom Checker: Cross-Sectional Survey Study.

Ashley N D Meyer, Traber D Giardina, Christiane Spitzmueller, Umber Shahid, Taylor M T Scott, Hardeep Singh

Registry-linked trialAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
76citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

NCT07733752 recruitingnot on this mapstarted 2026, after this paper: background citation

When AI Is the First Clinician: Impact of Pre-Visit AI Use on Presentation, Diagnostic Expectations, and Shared Decision-Making in Spine Physical Therapy

Typeobservational_patient_registrySponsorAssiut UniversityRan2026 to 2027Enrolled200ConditionsLow Back Pain, Neck Pain, RadiculopathyArmsre-visit AI symptom-checker use
3 · Its place in the literature

Who cites it

76 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  15. 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 · 2026
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  18. Artificial intelligence in acne vulgaris patients.Revista da Associacao Medica Brasileira (1992) · 2026
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16 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ashley N D MeyerCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States.ORCID 0000-0001-7993-8584
Traber D GiardinaCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States.ORCID 0000-0002-9184-6524
Christiane SpitzmuellerDepartment of Psychology, University of Houston, Houston, TX, United States.ORCID 0000-0003-0549-0259
Umber ShahidCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States.ORCID 0000-0002-9417-986X
Taylor M T ScottCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States.ORCID 0000-0002-1131-4270
Hardeep SinghCenter for Innovations in Quality, Effectiveness and Safety, Michael E DeBakey Veterans Affairs Medical Center and Baylor College of Medicine, Houston, TX, United States.ORCID 0000-0002-4419-8974

Funding

Application of a Machine Learning to Enhance e-Triggers to Detect and Learn from Diagnostic Safety EventsR01HS027363 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI SINGH, HARDEEP · 2019 to 2021
$1.5M
Engaging Patients in Diagnostic Error ReportingK01HS025474 · AHRQ · BAYLOR COLLEGE OF MEDICINE · PI GIARDINA, TRABER L · 2018 to 2022
$764k
AHRQ HHS K01 HS025474AHRQ HHS R01 HS027363HSRD VA IK2 HX002586
6 · The paper itself

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.

Indexed as

Artificial IntelligenceCross-Sectional StudiesFemaleHumansMaleMiddle AgedPatient PreferenceSurveys and Questionnairesclinical decision support systemscomputer-assisted diagnosisdiagnosispatient safetysymptom checkertechnology

Identifiers

PMID32012052
PMCPMC7055765

What OpenQuestion holds

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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.