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Diagnostic Inaccuracy of Smartphone Applications for Melanoma Detection
Open accessAbstract
OBJECTIVE: To measure the performance of smartphone applications that evaluate photographs of skin lesions and provide the user with feedback about the likelihood of malignancy. DESIGN: Case-control diagnostic accuracy study. SETTING: Academic dermatology department. PARTICIPANTS AND MATERIALS: Digital clinical images of pigmented cutaneous lesions (60 melanoma and 128 benign control lesions) with a histologic diagnosis rendered by a board-certified dermatopathologist, obtained before biopsy from patients undergoing lesion removal as a part of routine care. MAIN OUTCOME MEASURES: Sensitivity, specificity, and positive and negative predictive values of 4 smartphone applications designed to aid nonclinician users in determining whether their skin lesion is benign or malignant. RESULTS: Sensitivity of the 4 tested applications ranged from 6.8% to 98.1%; specificity, 30.4% to 93.7%; positive predictive value, 33.3% to 42.1%; and negative predictive value, 65.4% to 97.0%. The highest sensitivity for melanoma diagnosis was observed for an application that sends the image directly to a board-certified dermatologist for analysis; the lowest, for applications that use automated algorithms to analyze images. CONCLUSIONS: The performance of smartphone applications in assessing melanoma risk is highly variable, and 3 of 4 smartphone applications incorrectly classified 30% or more of melanomas as unconcerning. Reliance on these applications, which are not subject to regulatory oversight, in lieu of medical consultation can delay the diagnosis of melanoma and harm users.
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In the citing paper
“Regarding automated smartphone apps, the major concerns are the lack of assessment in clinical practice conditions, the lack of assessment in primary care populations, and their low sensitivity, ranging from 7% to 87% (median 69%).”
From Use of Smartphones for Early Detection of Melanoma: Systematic Review (2018) · cites this paper as [49]
Experimental — accuracy not yet measuredIn the citing paper
“Across the body of evidence presented, different apps recommended conflicting management advice for the same lesions.23 34”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [34]
In the citing paper
“We found studies were based on images of suspicious skin lesions that had undergone excision or biopsy, and were further selected to only include conditions identified by the apps; for example, they excluded lesions with clinical and histological features similar to melanoma,34 or restricted inclusion to melanocytic lesions which are more likely to be recognised by apps.23 28 29 31 32”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [34]
In the citing paper
“Table 2, table 3, and figure 2 report the numbers excluded for each analysis because of test failure, which ranged from 3/188 (1.6%)34 to 90/199 (45.2%).30”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [34]
In the citing paper
“However, a recent comparison of the accuracy of four smartphone apps in assessing melanoma risk demonstrated wide variation in performance and utility.7”
From Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review (2015) · cites this paper as [7]
In the citing paper
“a recent report highlighted the diagnostic inaccuracies of four smartphone apps for melanoma detection, with wide variations in sensitivity and specificity to detect malignant skin lesions.7”
From Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review (2015) · cites this paper as [7]
In the citing paper
“Reviews of smartphone apps for dermatology in general, 7,11 and for colorectal cancer, 8 have reported similar findings.”
From Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review (2015) · cites this paper as [7]
In the citing paper
“many experts remain cautious about the utility of the thousands of apps currently available, either free or at a small charge, for the prevention, detection and management of cancer.6,7”
From Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review (2015) · cites this paper as [7]
In the citing paper
“Cautionary commentary has also appeared suggesting that results obtained with publicly available smartphone dermatology applications may be highly variable and unreliable [51].”
From Recent directions in telemedicine: Review of trends in research and practice (2015) · cites this paper as [51]
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Metadata
- Authors
- Wolf, J. A., Moreau, J. F., Akilov, O., Patton, T., English, J. C., Ho, J., & Ferris, L. K.
- Affiliations
- University of Pittsburgh (US)
- Venue
- JAMA Dermatology
- Publisher
- American Medical Association (AMA)
- Published
- 2013-04-01
- Volume
- 149
- Issue
- 4
- Pages
- 422
- ISSN
- 2168-6068 (ISSN-L)
- PMC
- PMC4019431
- OpenAlex
- W2101764630
- Cited by
- 393
- References
- 9
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 9 · 2023: 12 · 2022: 21 · 2021: 19 · 2020: 38 · 2019: 41 · 2018: 45 · 2017: 32 · 2016: 43 · 2015: 53 · 2014: 43 · 2013: 27
- FWCI
- 29.61
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- JAMA Dermatology (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (UL1TR000005)
- National Center for Research Resources (UL1RR024153)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- melanoma, mobile application, melanoma detection, diagnostic accuracy, pigmented skin lesions, automated image analysis, telemedicine
- MeSH terms
- Algorithms, Diagnosis, Differential, Diagnostic Errors, Humans, Image Interpretation, Computer-Assisted, Melanoma, ROC Curve, Skin Neoplasms, Software, User-Computer Interface, Video Recording, Telemedicine, Computers, Handheld, Electronic Mail, Cell Phone, Dermoscopy
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