Mobile Medical Applications for Melanoma Risk Assessment: False Assurance or Valuable Tool?
Open accessAbstract
With the smartphone revolution, consumer-focused mobile medical applications (apps) have flooded the market without restriction. We searched the market for commercially available apps on all mobile platforms that could provide automated risk analysis of the most serious skin cancer, melanoma. We tested 5 relevant apps against 15 images of previously excised skin lesions and compared the apps' risk grades to the known histopathologic diagnosis of the lesions. Two of the apps did not identify any of the melanomas. The remaining 3 apps obtained 80% sensitivity for melanoma risk identification; specificities for the 5 apps ranged from 20%-100%. Each app provided its own grading and recommendation scale and included a disclaimer recommending regular dermatologist evaluation regardless of the analysis outcome. The results indicate that autonomous lesion analysis is not yet ready for use as a triage tool. More concerning is the lack of restrictions and regulations for these applications.
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The SkinVision predecessor app SkinScan was evaluated in a single study of only 15 lesions (five melanomas).
“The SkinVision predecessor app SkinScan was evaluated in a single study of only 15 lesions (five melanomas).23”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
For the SkinScan app, sensitivity was low regardless of whether moderate risk was combined with the low or high risk category (0% or 20% respectively), with corresponding specificities of 100% and 60%.
“Sensitivity was low regardless of whether moderate risk was combined with the low or high risk category (0% or 20% respectively), with corresponding specificities of 100% and 60% (fig 2).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
App recommendations commonly disagreed with histopathological results or clinical assessment, with some apps unable to identify any cases of melanoma.
“Additionally app recommendations commonly disagreed with histopathological results or clinical assessment, with some apps unable to identify any cases of melanoma.23”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
Results from the five studies that reported data for apps with uncertain availability or withdrawn apps are if anything more variable.
“Results from the five studies that reported data for apps with uncertain availability23 24 or withdrawn apps23 32 are if anything more variable.”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
For apps with uncertain availability or withdrawn apps, MelApp had low sensitivities (25-50%) and Mole Detective, Dr Mole, and SpotMole had low specificities (20-60%).
“with either low sensitivities (25-50% for MelApp) or specificities (20-60% for Mole Detective, Dr Mole, and SpotMole).”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
Across the body of evidence, different apps recommended conflicting management advice for the same lesions.
“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 [23]
When apps failed to return a risk assessment, images were either excluded a priori from the studies, excluded from analysis, or were not reported.
“When apps failed to return a risk assessment, images were either excluded a priori from the studies,23 excluded from analysis,24 28 30 31 32 34 or were not reported.29”
From Algorithm based smartphone apps to assess risk of skin cancer in adults: systematic review of diagnostic accuracy studies (2020) · cites this paper as [23]
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, or restricted inclusion to melanocytic lesions which are more likely to be recognised by apps.
“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 [23]
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References 21 · Cited by 17
Research works printed but not linked: 1
- C. Massone, E.M. Wurm, and H.P. Soyer, "Teledermatology," Giornale italiano di dermatologia e venereologia : organo ufficiale, Societa italiana di dermatologia e sifilografia, 2008, 143, 213-218.
- Authors
- Chadwick, X., Loescher, L. J., Janda, M., & Soyer, H. P.
- Affiliations
- The University of Queensland (AU)
- University of Arizona (US)
- Queensland University of Technology (AU)
- Venue
- 2014 47th Hawaii International Conference on System Sciences
- Publisher
- IEEE
- Published
- 2014-01
- Pages
- 2675-2684
- ISSN
- 1530-1605 (ISSN-L)
- OpenAlex
- W2036975116
- Cited by
- 17
- References
- 21
- Citations by year
- 2025: 1 · 2023: 1 · 2020: 2 · 2019: 3 · 2018: 1 · 2017: 2 · 2016: 4 · 2015: 2 · 2014: 1
- FWCI
- 21.24
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion)
- Funders
- Medical Research Council
- National Health and Medical Research Council (1020148)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- melanoma, mobile health apps, sensitivity and specificity, triage tool, consumer health technology
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