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How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs
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Abstract
Objectives: To compare breadth of condition coverage, accuracy of suggested conditions and appropriateness of urgency advice of eight popular symptom assessment apps. Design: Vignettes study. Setting: 200 primary care vignettes. Intervention/comparator: For eight apps and seven general practitioners (GPs): breadth of coverage and condition-suggestion and urgency advice accuracy measured against the vignettes’ gold-standard. Primary outcome measures: (1) Proportion of conditions ‘covered’ by an app, that is, not excluded because the user was too young/old or pregnant, or not modelled; (2) proportion of vignettes with the correct primary diagnosis among the top 3 conditions suggested; (3) proportion of ‘safe’ urgency advice (ie, at gold standard level, more conservative, or no more than one level less conservative). Results: Condition-suggestion coverage was highly variable, with some apps not offering a suggestion for many users: in alphabetical order, Ada: 99.0%; Babylon: 51.5%; Buoy: 88.5%; K Health: 74.5%; Mediktor: 80.5%; Symptomate: 61.5%; Your.MD: 64.5%; WebMD: 93.0%. Top-3 suggestion accuracy was GPs (average): 82.1%±5.2%; Ada: 70.5%; Babylon: 32.0%; Buoy: 43.0%; K Health: 36.0%; Mediktor: 36.0%; Symptomate: 27.5%; WebMD: 35.5%; Your.MD: 23.5%. Some apps excluded certain user demographics or conditions and their performance was generally greater with the exclusion of corresponding vignettes. For safe urgency advice, tested GPs had an average of 97.0%±2.5%. For the vignettes with advice provided, only three apps had safety performance within 1 SD of the GPs—Ada: 97.0%; Babylon: 95.1%; Symptomate: 97.8%. One app had a safety performance within 2 SDs of GPs—Your.MD: 92.6%. Three apps had a safety performance outside 2 SDs of GPs—Buoy: 80.0% (p<0.001); K Health: 81.3% (p<0.001); Mediktor: 87.3% (p=1.3×10 -3 ). Conclusions: The utility of digital symptom assessment apps relies on coverage, accuracy and safety. While no digital tool outperformed GPs, some came close, and the nature of iterative improvements to software offers scalable improvements to care.
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“Furthermore, the accuracy observed in our study is close to the performance rate of ADA (48.5%) across a broad spectrum of medical conditions in the study by Gilbert et al [34] but lower than in another recent comparative study (72%) [35].”
From Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients (2022) · cites this paper as [35]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In two-thirds (34/49, 67%) of cases, 1 in 5 condition suggestions aligned with any of the interview-based diagnoses, which is somewhat below the range of performance rates of ADA in previous studies using case vignettes (77%-84%) [34,35] or patients seeking emergency care (91.3%) [58].”
From Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients (2022) · cites this paper as [35]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“On this matter, evidence indicates that the use of algorithms over other methods, the inclusion of demographic information [57], or more rigorous questioning [35] could explain the differences between symptom checkers’ diagnostic performances.”
From Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients (2022) · cites this paper as [35]
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“As previous studies have shown considerable differences between symptom checkers’ diagnostic accuracy [33,35], future studies could compare various symptom checkers for the formal diagnosis of mental disorders.”
From Diagnostic Performance of an App-Based Symptom Checker in Mental Disorders: Comparative Study in Psychotherapy Outpatients (2022) · cites this paper as [35]
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Exploratory Study of the Determinants of Medical AI Adoption
Chae, J.-B., & Kim, K.-J. (2026) · The Korean Journal of Health Service Management · doi:10.12811/kshsm.2026.20.2.031
Zhao, Z., & Arnesano, C. (2026) · IEEE International Conference on Pattern Recognition and Machine Learning (PRML) · doi:10.1109/prml71097.2026.11686893
“Semigran et al. [2] audited 23 symptom checkers, setting the tone for the evaluation of such tools; Gilbert et al. [3] compared apps with general practitioners, and Ceney et al. [4] conducted a cross-platform accuracy comparison.”
Usability and Acceptability of a Symptom Checker App by Patients Seeking Urgent Care
Fraser, H., Bacher, I., Khatri, I. S., Raman, S., Nagle, D., Crossland, D., Madsen, T., & Hilliard, R. (2026) · Applied Clinical Informatics · doi:10.1055/a-2962-9261
“10 , 11 While such studies can offer useful insights into the comparative performance of different diagnostic algorithms, many have fewer than 50 cases and are not necessarily representative of real patient presentations.”
Self-citation
Metadata
- Authors
- Gilbert, S., Mehl, A., Baluch, A., Cawley, C., Challiner, J., Fraser, H., Millen, E., Montazeri, M., Multmeier, J., Pick, F., Richter, C., Türk, E., Upadhyay, S., Virani, V., Vona, N., Wicks, P., & Novorol, C.
- Affiliations
- Brown University (US)
- Corresponding author
- Gilbert, Stephen
- ORCID
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2020-12
- Volume
- 10
- Issue
- 12
- Pages
- e040269
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC7745523
- OpenAlex
- W3112185662
- Cited by
- 183
- References
- 30
- Citations by year
- 2026: 20 · 2025: 25 · 2024: 31 · 2023: 37 · 2022: 40 · 2021: 27 · 2020: 3
- FWCI
- 27.92
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- HighWire Press Open Archive (submittedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- symptom checkers, clinical vignettes, general practitioners, Ada, Babylon, buoys, WebMD, primary care, diagnostic accuracy, patient safety, user demographics, mobile health apps, telemedicine, primary diagnosis
- MeSH terms
- Humans, Primary Health Care, General Practitioners, Symptom Assessment, Mobile Applications
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