Prospective study to assess general practitioners' dermatological diagnostic skills in a referral setting
Abstract
SUMMARY: A prospective study was conducted to assess general practitioners' diagnostic skills in a referral setting. The primary objective was to identify general practitioners' strengths and weaknesses in diagnosing a broad spectrum of skin conditions. The diagnoses of 315 skin conditions made by 165 general practitioners were compared with a reference standard. The reference standard was made up of 73 histopathological diagnoses, 119 dermatologists' clinical diagnoses and 123 dermatologists' diagnoses plus follow up. The diagnoses assigned by referring general practitioners were consistent with dermatologists' clinical diagnoses and histology (where available) in 57% of cases. General practitioners made the correct diagnosis in 44% of cases when compared with histopathology. General practitioners were generally good at diagnosing conditions such as acne, warts, rosacea, molluscum contagiosum, vitiligo and skin tags. The proportion of correct diagnoses for premalignant and malignant skin tumours was 47%, and that of skin rashes requiring a diagnosis was 44%. Further education of general practitioners would help to improve their diagnostic skills in certain skin conditions.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The diagnostic accuracy of nonspecialists is reportedly only 24% to 70%.
“The diagnostic accuracy of nonspecialists is reportedly only 24% to 70%,4,8,9,10”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cited by
Sorted by citations
Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4
Zhou, J., He, X., Sun, L., Xu, J., Chen, X., Chu, Y., Zhou, L., Liao, X., Zhang, B., Afvari, S., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50043-3 · Cited by 189 · see citing works
Jain, A., Way, D., Gupta, V., Gao, Y., de Oliveira Marinho, G., Hartford, J., Sayres, R., Kanada, K., Eng, C., Nagpal, K., et al. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.7249 · Cited by 164 · see citing works
Deep learning-aided decision support for diagnosis of skin disease across skin tones
Groh, M., Badri, O., Daneshjou, R., Koochek, A., Harris, C., Soenksen, L. R., Doraiswamy, P. M., & Picard, R. (2024) · Nature Medicine · doi:10.1038/s41591-023-02728-3 · Cited by 160 · see citing works
Zhong, F., He, K., Ji, M., Chen, J., Gao, T., Li, S., Zhang, J., & Li, C. (2024) · Scientific Reports · doi:10.1038/s41598-024-59436-2 · Cited by 37 · see citing works
- Authors
- Moreno, G., Tran, H., Chia, A. L., Lim, A., & Shumack, S.
- Corresponding author
- Shumack, Stephen
- Venue
- Australasian Journal of Dermatology
- Publisher
- Wiley
- Published
- 2007-05
- Volume
- 48
- Issue
- 2
- Pages
- 77-82
- ISSN
- 0004-8380 (ISSN-L)
- 1440-0960
- OpenAlex
- W2012945985
- Cited by
- 52
- References
- 0
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 8 · 2023: 10 · 2022: 4 · 2021: 5 · 2020: 3 · 2019: 1 · 2017: 1 · 2016: 2 · 2014: 3 · 2012: 1
- FWCI
- 4.62
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Australasian Journal of Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Health Professions › General Health Professions › Primary Care and Health Outcomes
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Healthcare Systems and Technology
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- general practitioners, diagnostic accuracy, histopathology, dermatologist comparison, skin tumours, premalignant skin lesions, rash, acne, warts, rosacea, molluscum contagiosum, vitiligo, skin tags, continuing medical education
- MeSH terms
- Adult, Clinical Competence, Family Practice, Female, Humans, Health Knowledge, Attitudes, Practice, Male, Middle Aged, New South Wales, Practice Patterns, Physicians', Prospective Studies, Referral and Consultation, Skin Diseases, Health Care Surveys
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review