Smartphone applications for melanoma detection by community, patient and generalist clinician users: a review
Open accessAbstract
Smartphone health applications ('apps') are widely available but experts remain cautious about their utility and safety. We reviewed currently available apps for the detection of melanoma (July 2014), aimed at general community, patient and generalist clinician users. A proforma was used to extract and assess each app that met the inclusion criteria, and we undertook content analysis to evaluate their content and the evidence applied in their development. Thirty-nine apps were identified with the majority available only for Apple users. Over half (n = 22) provided information or education about melanoma, ultraviolet radiation exposure prevention advice, and skin self-examination strategies, mainly using the ABCDE (A, Asymmetry; B, Border; C, Colour; D, Diameter; E, Evolving) method. Half (n = 19) helped users take and store images of their skin lesions either for review by a dermatologist or for self-monitoring to identify change, an important predictor of melanoma; a similar number (n = 18) used reminders to help users monitor their skin lesions. A few (n = 9) offered expert review of images. Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign, and one app calculated users' future risk of melanoma. None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods. Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change. Despite their potential usefulness, and while clinicians may choose to use apps that provide information to educate their patients, apps for melanoma detection require further validation of their utility and safety.
AI snapshot
Written from the paper’s full text.
TL;DR
This review identified 39 smartphone apps available in July 2014 for melanoma detection by community, patient, and generalist clinician users, most of which provided information, image capture/storage, or lesion monitoring. None appeared validated for diagnostic accuracy or utility, and only four apps provided lesion risk assessment.
Key findings
- Of 39 included apps, 26 (66.7%) were Apple-only, 8 (20.5%) Android-only, and 5 (12.8%) available on both; 28 were free, with paid apps ranging from £0.69 to £4.99.
- The commonest function was general information about melanoma/skin cancer (n = 22); 19 apps catalogued/classified lesions by capturing images, 18 used image analysis, 18 aimed to monitor skin lesions, and 9 offered expert image review.
- Only 4 apps provided risk assessment for whether a lesion was malignant or benign, and one app calculated future melanoma risk using eight risk factors to estimate 5-year absolute risk.
- None of the apps appeared validated for diagnostic accuracy or utility using established research methods; only one app acknowledged research/development processes, and clinician involvement was clearly stated in only four apps.
Methods
- In July 2014, the authors searched Apple and Android online stores using terms including “skin cancer,” “mole,” and “melanoma,” including apps stating an aim to help detect melanoma and excluding entertainment, cosmetics-only, general medical information-only, specialist-only/dermoscopy, and non-English apps.
- A proforma was used by one researcher to extract data from online app descriptions only; no apps were downloaded. Data covered operating system, research/validation status, latest update, and functions including information/education, risk factor assessment, image analysis/cataloguing, and monitoring changes.
- Content analysis and critical appraisal were performed independently by two researchers, with categories refined and differences resolved by consensus among the wider research team.
Limitations
- The paper states the review was restricted to online store descriptions, did not purchase or use apps, included only English-language apps, and covered only Apple and Android platforms.
- The paper notes that more information may have been available after purchasing or using apps, but this was not done to ensure equity across the review.
- Evident from the text: the review could not assess actual diagnostic performance, safety, or clinical utility because no apps were validated or tested against a reference standard in the included material.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
0 of 32 cited sources checked · 0 supported · 0 flagged
- Findingown data
Thirty-nine unique smartphone apps met the inclusion criteria for this review.
“We identified 39 unique smartphone apps that met our inclusion criteria (Tables 1 and 2).”
Quote in the paperSubstantial claim - Findingown data
Only four apps provided tools to assess a skin lesion for likelihood of melanoma.
“Only four apps provided tools to assess a skin lesion for likelihood of melanoma.”
- Also stated in: § Abstract · plain
“Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign”
Quote in the paperNot a substantial claim - Also stated in: § Abstract · plain
- Findingown data
Eighteen apps aimed to monitor skin lesions.
“Nineteen apps catalogued and classified lesions by capturing images, and applying integral algorithms, and 18 apps aimed to monitor skin lesions.”
Quote in the paperNot a substantial claim- Rests on an assumption: Change in a skin lesion is an important predictor of melanoma.
- Findingown data
Seven apps provided limited or no information on how photographic images were processed and analysed to provide advice on likelihood of melanoma.
“It was noteworthy that seven apps (Mole Detective, Mole Monitor, Skin of Mine, Skin Prevention -Photo Body Map, Skin Scanner, Skin Vision, SkinXM) provided limited or no information on how the photographic images were processed and analysed to provide advice on likelihood of melanoma.”
Quote in the paperNot a substantial claim - Findingnone given
A single app (UMSkinCheck) offered assessment of a person's baseline risk factor, using eight risk factors to calculate 5-year absolute risk for melanoma.
“A single app offered assessment of a person's baseline risk factor: UMSkinCheck used eight risk factors (region, sex, race, age, complexion, tanning, small moles and freckling) to calculate 5-year absolute risk for melanoma.”
Quote in the paperNot a substantial claim
- Findingown data
There was no information about the model used to underpin UMSkinCheck's 5-year absolute risk calculation.
“However, there was no information about the model used to underpin this calculation.”
Quote in the paperNot a substantial claim - Findingown data
The commonest function of the apps was to provide general information about melanoma and/or skin cancer (n=22).
“The commonest function was to provide general information about melanoma and/or skin cancer (n = 22).”
Quote in the paperNot a substantial claim - Findingown data
The majority of the 39 apps were available only for Apple devices.
“Most are available for Apple Inc. devices only (26, 66Á7%), with eight available for Android devices only (20Á5%), and just five available for both platforms (12Á8%).”
- Also stated in: § Abstract · plain
“Thirty-nine apps were identified with the majority available only for Apple users.”
Quote in the paperNot a substantial claim - Also stated in: § Abstract · plain
- Findingcitation
The ABCDE method was most commonly used to provide education about skin self-examination and self-assessment of moles (n=12).
“The ABCDE method (A, Asymmetry; B, Border; C, Colour; D, Diameter; E, Evolving) was most commonly used to provide education about skin self-examination and self-assessment of moles (n = 12).16”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [16] Robinson, 2009, Use of photographs illustrating ABCDE criteria in skin self-examination, Arch Dermatol source
Not checked in the free run.
- Findingown data
The Doctor Mole and Skin Doctor apps used a computerized algorithm based on the ABCDE method to assess a skin lesion.
“The Doctor Mole and Skin Doctor apps used a computerized algorithm based on the ABCDE method, and the Melanoma Visual Risk Calculator provided a risk approximation based on patients' responses to a VAS.”
Quote in the paperNot a substantial claim - Findingown data
Nineteen apps catalogued and classified lesions by capturing images and applying integral algorithms.
“Nineteen apps catalogued and classified lesions by capturing images, and applying integral algorithms, and 18 apps aimed to monitor skin lesions.”
Quote in the paperNot a substantial claim - Findingown data
Nine apps offered prompt expert review of images taken by the user.
“A few (n = 9) offered expert review of images.”
Quote in the paperNot a substantial claim - Findingnone given
Clinician involvement in the development of apps was clearly stated in only four apps.
“Clinician involvement in the development of apps was clearly stated in only four apps.”
Quote in the paperNot a substantial claim - Findingown data
The expert-review apps functioned only in the U.S.
“These were often accompanied by advice on making an appointment with a dermatologist, and functioned only in the U.S.”
Quote in the paperNot a substantial claim - Findingown data
None of the apps were reported to have been validated for diagnostic accuracy or utility using established research methods.
“None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods.”
- Also stated in: § Development and performance of apps · plain
“None of the apps were reported to have been validated.”
- Also stated in: § Discussion · hedged
“None of the apps appeared to have been validated for diagnostic accuracy or utility using established research methods.”
Quote in the paperSubstantial claim - Also stated in: § Development and performance of apps · plain
- Findingcitation
Twelve apps made use of the ABCDE method to inform and educate users.
“Twelve apps made use of the ABCDE method 27 to inform and educate users.”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [27] Rigel, 2005, ABCDE - an evolving concept in the early detection of melanoma, Arch Dermatol source
Not checked in the free run.
- Findingown data
The review was not able to distinguish whether commercial or other apps were updated most regularly.
“we were not able to distinguish whether commercial or other apps were updated most regularly.”
Quote in the paperNot a substantial claim - Findingnone given
Some apps had not been updated for more than 3 years.
“There was limited information about whether the apps were developed with clinician involvement, and some apps had not been updated for more than 3 years.”
Quote in the paperNot a substantial claim - Findingown data
Cataloguing and classifying lesions was mainly undertaken by comparing the users' own photographic images against a set of exemplar images.
“Cataloguing and classifying lesions was mainly undertaken by comparing the users' own photographic images obtained by the smartphone against a set of exemplar images, such as in Embarrassing Bodies -My MoleChecker.”
Quote in the paperNot a substantial claim - Findingnone given
The Doctor Mole and Skin Doctor apps used a computerized algorithm based on the ABCDE method to help people assess the risk of melanoma directly for a specific lesion.
“The Doctor Mole and Skin Doctor apps also used a computerized algorithm based on the ABCDE method to help people assess the risk of melanoma directly for a specific lesion.”
Quote in the paperNot a substantial claim - Findingown data
Some apps contained integral algorithms for photographic analysis such as pattern recognition and real-time computer image analysis technology.
“Some apps contained integral algorithms for photographic analysis such as pattern recognition and real-time computer image analysis technology in Doctor Mole and SpotMole, enabling the user to take a photographic image of the mole for assessment of each of the ABCDE criteria, and reporting changes in the features of the mole compared with previous photographic images.”
Quote in the paperNot a substantial claim - Findingown data
There was limited information about whether the apps were developed with clinician involvement.
“There was limited information about whether the apps were developed with clinician involvement, and some apps had not been updated for more than 3 years.”
Quote in the paperNot a substantial claim - Findingnone given
Fewer apps used automated image analysis for skin self-examination.
“Fewer apps used automated image analysis for skin self-examination.”
Quote in the paperNot a substantial claim - Findingown data
Only one app incorporated a validated risk model giving individuals information about their future risk of melanoma.
“Four apps provided a risk assessment to patients about the probability that a lesion was malignant or benign, and only one app incorporated a validated risk model giving individuals information about their future risk of melanoma.”
Quote in the paperNot a substantial claim - Findingown data
A similar number of apps enabled patients to capture and store images of their skin lesions either for review by a dermatologist or for self-monitoring to identify change.
“A similar number enabled patients to capture and store images of their skin lesions either for review by a dermatologist or for self-monitoring to identify change, an important predictor of melanoma.”
Quote in the paperNot a substantial claim- Rests on an assumption: Change in a skin lesion is an important predictor of melanoma.
- Findingown data
Even when updated recently, there was no discussion around whether the updates were based on new evidence.
“Even when updated recently, there was no discussion around whether the updates were based on new evidence”
Quote in the paperSubstantial claim - Findingown data
Some apps have not been updated for more than 3 years.
“We found that some apps have not been updated for more than 3 years.”
Quote in the paperNot a substantial claim - Findingnone given
Almost a quarter of the apps used a teledermatology model to send the image for expert review.
“Almost a quarter of the apps used a teledermatology model to send the image for expert review.”
Quote in the paperNot a substantial claim - Findingown data
One-third of the apps provided information only.
“One-third of these apps provided information only.”
Quote in the paperNot a substantial claim - Findingnone given
This review identified smartphone apps for detecting melanoma by nonspecialist users including previously unaffected individuals, patients previously diagnosed with skin cancer, and generalist clinicians.
“This review has identified smartphone apps for detecting melanoma by nonspecialist users including previously unaffected individuals, patients previously diagnosed with skin cancer, and generalist clinicians.”
Quote in the paperSubstantial claim - Findingown data
One-third of the apps only promoted awareness of cancer symptoms or solar protection, with no potential for interaction or specialist overview.
“Nevertheless, one-third of the apps that we identified only promoted awareness of cancer symptoms or solar protection, with no potential for interaction or specialist overview, despite the strong evidence suggesting that the provision of information alone is not an effective intervention to improve patient self-management of chronic disease in primary care.6,18”
Quote in the paperSubstantial claim - Findingown data
Most of the apps provided more than one type of function.
“Most of the apps provided more than one type of function.”
Quote in the paperNot a substantial claim - Findingown data
6 of the reviewed apps provided UVR/sun exposure advice.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claim - Findingown data
18 of the reviewed apps provided image analysis.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claim - Findingown data
15 of the reviewed apps provided skin self-examination.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claim - Findingown data
19 of the reviewed apps provided monitoring/tracking.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claim - Findingown data
4 of the reviewed apps provided risk factor assessment.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claim - Findingown data
Our review has shown little evidence of clinical- or research-based input.
“Our review has shown little evidence of clinical-or research-based input”
Quote in the paperSubstantial claim - Premisecitation
A recent comparison of the accuracy of four smartphone apps in assessing melanoma risk demonstrated wide variation in performance and utility.
“However, a recent comparison of the accuracy of four smartphone apps in assessing melanoma risk demonstrated wide variation in performance and utility.7”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [7] Wolf, 2013, Diagnostic inaccuracy of smartphone applications for melanoma detection, JAMA Dermatol source
Not checked in the free run.
- Premisecitation
There is evidence that the revised 'Glasgow' 7-point checklist has higher sensitivity for diagnosing cutaneous melanoma when compared with the ABCDE method.
“there is evidence that the revised 'Glasgow' 7-point checklist has higher sensitivity for diagnosing cutaneous melanoma when compared with the ABCDE method.28”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [28] Healsmith, 1994, An evaluation of the revised seven-point checklist for the early diagnosis of cutaneous malignant melanoma, Br J Dermatol source
Not checked in the free run.
- Interpretationcitation
Assessment of clinical photographs and/or dermoscopic images is likely reliable in smartphone apps assuming adequate image quality.
“suggesting the likely reliability of this method in smartphone apps assuming adequate image quality.”
Quote in the paperSubstantial claim - Premisecitation
Assessment of clinical photographs and/or dermoscopic images has given good diagnostic values, up to 98% sensitivity.
“assessment of clinical photographs and/or dermoscopic images have given good diagnostic values (up to 98% sensitivity)”
Quote in the paperSubstantial claim0 of 3 sources checked- cites [21] Kroemer, 2011, Mobile teledermatology for skin tumour screening: diagnostic accuracy of clinical and dermoscopic image tele-evaluation using cellular phones, Br J Dermatol source
Not checked in the free run.
- cites [22] Lamel, 2012, Application of mobile teledermatology for skin cancer screening, J Am Acad Dermatol source
Not checked in the free run.
- cites [23] Massone, 2007, Melanoma screening with cellular phones, PLoS ONE source
Not checked in the free run.
- Interpretationown data
Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change.
“Smartphone apps for detecting melanoma by nonspecialist users have a range of functions including information, education, classification, risk assessment and monitoring change.”
Quote in the paperSubstantial claim - Interpretationreasoning
Apps for melanoma detection require further validation of their utility and safety.
“Despite their potential usefulness, and while clinicians may choose to use apps that provide information to educate their patients, apps for melanoma detection require further validation of their utility and safety.”
Quote in the paperSubstantial claim- Rests on an assumption: Smartphone apps for the identification or management of cancer including melanoma have not been subject to any sort of validation or regulatory controls in the U.S.A., the U.K. or elsewhere.
- Premisecitation
Communication with clinicians may improve adherence to e-healthcare interventions.
“as communication with clinicians may improve adherence to e-healthcare interventions, 20”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] Mohr, 2011, Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions, J Med Internet Res source
Not checked in the free run.
- Premisecitation
The provision of information alone is not an effective intervention to improve patient self-management of chronic disease in primary care.
“despite the strong evidence suggesting that the provision of information alone is not an effective intervention to improve patient self-management of chronic disease in primary care.6,18”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [6] Bender, 2013, A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer, J Med Internet Res source
Not checked in the free run.
- cites [18] Bodenheimer, 2002, Patient self-management of chronic disease in primary care, JAMA source
Not checked in the free run.
- Premisecitation
The two most popular smartphone providers together provide more than 90% of cancer-related apps.
“which together provide more than 90% of cancer-related apps 6”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [6] Bender, 2013, A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer, J Med Internet Res source
Not checked in the free run.
- Premisecitation
Adding self-monitoring techniques could be more effective than information-provision alone.
“Adding self-monitoring techniques could be more effective than information-provision alone.19”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [19] Bandura, 2004, Health promotion by social cognitive means, Health Educ Behav source
Not checked in the free run.
- Interpretationcitation
Apps that facilitate communication with skin specialists may have utility, particularly for individuals previously diagnosed with a skin cancer.
“Moreover, as communication with clinicians may improve adherence to e-healthcare interventions, 20 apps that facilitate communication with skin specialists may have utility, particularly for individuals previously diagnosed with a skin cancer.”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [20] Mohr, 2011, Supportive accountability: a model for providing human support to enhance adherence to eHealth interventions, J Med Internet Res source
Not checked in the free run.
- Interpretationreasoning
There is less clear evidence around the diagnostic accuracy or utility of automated image analysis for skin self-examination.
“there is less clear evidence around its diagnostic accuracy or utility.”
Quote in the paperSubstantial claim - Interpretationown data
There is a profound mismatch between the promise of the app developers and evidence of clinical validity and utility, as demonstrated in this study.
“There seems to be a profound mismatch between the promise of the app developers on the one hand and evidence of clinical validity and utility on the other hand, as demonstrated in this study.”
Quote in the paperSubstantial claim - Interpretationnone given
This review is the first to evaluate the content of smartphone apps available for melanoma detection for previously unaffected individuals or those previously diagnosed with a skin cancer, and generalist clinicians.
“This is the first review that evaluates the content of smartphone apps available for melanoma detection for previ-ously unaffected individuals or those previously diagnosed with a skin cancer, and generalist clinicians.”
Quote in the paperSubstantial claim - Premisecitation
Previous reviews of smartphone apps focused mainly on those developed by Apple Inc.
“Previous reviews of smartphone apps focused mainly on those developed by Apple Inc. 3,30”
Quote in the paperSubstantial claim0 of 2 sources checked- cites [3] Rosser, 2011, Smartphone applications for pain management, J Telemed Telecare source
Not checked in the free run.
- cites [30] Pandey, 2013, Smartphone apps as a source of cancer information: changing trends in health information-seeking behavior, J Cancer Educ source
Not checked in the free run.
- Premisecitation
Diagnostic delay experienced by people with melanoma may be significantly associated with mortality.
“which may be significantly associated with mortality.36”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [36] Abdel-Rahman, 2009, What if cancer survival in Britain were the same as in Europe: how many deaths are avoidable?, Br J Cancer source
Not checked in the free run.
- Classified as probably not a substantial claim. 29
- May combine two claims. 20
- The quote only partly states this claim. 2
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataOne app (UMSkinCheck) stated it was specifically designed for non-Hispanic white U.S. citizens.
“One app (UMSkinCheck) stated it was specifically designed for non-Hispanic white U.S. citizens.”
Quote in the paperNot a substantial claimMay combine two claims.
FindingcitationOnly one app acknowledged any research and developmental processes; the Melanoma Visual Risk Calculator was based on the diagnostic value of the ABCDE criteria for patient users.
“Even though several of the apps incorporated the ABCDE method, only one app acknowledged any research and developmental processes; the Melanoma Visual Risk Calculator was based on the diagnostic value of the ABCDE criteria for patient users.17”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [17] Thomas, 1998, Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors, Dermatology source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataThe Melanoma Visual Risk Calculator provided a risk approximation based on patients' responses to a VAS.
“The Doctor Mole and Skin Doctor apps used a computerized algorithm based on the ABCDE method, and the Melanoma Visual Risk Calculator provided a risk approximation based on patients' responses to a VAS.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingown dataFour apps offered guidance on how to find a dermatologist in the U.S.A.
“Four apps (Dermatology Planet, iDoc24, Skin Cancer, Skin of Mine) also offered guidance on how to find a dermatologist in the U.S.A.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataMost apps (n=28) were available to download for free, with the remaining apps having an added cost (range £0.69-£4.99).
“Most of the apps (n = 28) were available to download for free, with the remaining apps having an added cost to download (range £0Á69-£4Á99).”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone giveniSkin claimed to gather information about a mole and then calculate risk of melanoma, but provided no further information about the method involved.
“iSkin claimed to gather information about a mole and then calculate risk of melanoma, but provided no further information about the method involved.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingnone givenThere was little evidence of clinical or research-based input into the design of these apps or of evaluation of their utility.
“There was little evidence of clinical or research-based input into the design of these apps or of evaluation of their utility, so clinicians should be cautious about supporting the use of such apps to detect melanoma.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingnone givenUsing smartphone apps to monitor skin lesions was undertaken via the storage of photographic images, and users were encouraged to track the evolution of individual lesions by comparing serial images over time.
“Using smartphone apps to monitor skin lesions was undertaken via the storage of photographic images of their skin lesions.Users were encouraged to track the evolution of individual lesions by comparing serial images over time.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThe technology can use a 'teledermatology' model, and send the image for expert review and advice.
“Furthermore, the technology can use a 'teledermatology' model, and send the image for expert review and advice.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataSome apps used simple comparisons with stored images, while others used more sophisticated algorithms.
“While some such as Doctor Mole and Skin Prevention used simple comparisons with stored images, others used more sophisticated algorithms, such as Skin Analytics, which claimed to use 'real time vision technology'.”
Quote in the paperNot a substantial claimMay combine two claims.
Findingown dataThere is surprisingly scanty scientific evidence about the development or evaluation of the smartphone apps included in the review; only one app provided a single reference to a peer-reviewed publication.
“We also found surprisingly scanty scientific evidence about the development or evaluation of the smartphone apps included in our review; only one app provided a single reference to a peer-reviewed publication.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenMost apps identified were developed in the U.S.A.
“Most apps that we identified were developed in the U.S.A.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing), S4 (based).
Findingnone givenThree apps (Doctor Mole -Skin Cancer app, Mollie's Fund, n evus) had their Apple and Android versions updated in different years.
“Three apps (Doctor Mole -Skin Cancer app, Mollie's Fund, n evus) had their Apple and Android versions updated in different years.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
Findingnone givenThese apps also suggested that users could share the images during consultations with their dermatologist or family physician.
“These apps also suggested that users could share the images during consultations with their dermatologist or family physician.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Findingown dataSome apps provided feedback to patients based on the ABCDE method; others allowed users to set reminders to take another photograph for comparison after a selected time.
“Some, such as the Doctor Mole app, provided feedback to patients based on the ABCDE method; others, such as the Embarrassing Bodies -My Mole-Checker and the Mole Detective apps allowed users to set reminders to take another photograph for comparison after a selected time.”
Quote in the paperNot a substantial claimThe quote only partly states this claim.
Findingown data22 of the reviewed apps provided melanoma/skin cancer information and/or education.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing). The quote only partly states this claim.
Findingown data9 of the reviewed apps provided dermatologist review.
“Total | 22 | 6 | 15 | 4 | 18 | 9 | 19”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S2 (checkable).
InterpretationreasoningClinicians should be cautious about supporting the use of such apps to detect melanoma.
“There was little evidence of clinical or research-based input into the design of these apps or of evaluation of their utility, so clinicians should be cautious about supporting the use of such apps to detect melanoma.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S1 (asserted), S3 (load-bearing).
PremisecitationApps for detecting cancers tend to lack scientific and specialty input.
“It has also been suggested that apps for detecting cancers tend to lack scientific and specialty input, 8”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [8] O'Neill, 2012, Colorectal smartphone apps: opportunities and risks, Colorectal Dis source
Not checked in the free run.
May combine two claims.
PremisecitationReviews of smartphone apps for dermatology in general and for colorectal cancer have reported similar findings of scanty scientific evidence.
“Reviews of smartphone apps for dermatology in general, 7,11 and for colorectal cancer, 8 have reported similar findings.”
Quote in the paperNot a substantial claim0 of 3 sources checked- cites [7] Wolf, 2013, Diagnostic inaccuracy of smartphone applications for melanoma detection, JAMA Dermatol source
Not checked in the free run.
- cites [11] Hamilton, 2012, Medical professional involvement in smartphone ‘apps’ in dermatology, Br J Dermatol source
Not checked in the free run.
- cites [8] O'Neill, 2012, Colorectal smartphone apps: opportunities and risks, Colorectal Dis source
Not checked in the free run.
May combine two claims.
InterpretationreasoningSmartphone apps have the potential to improve patient and nonspecialist clinician assessment and patient-clinician communication about potential skin cancer.
“Therefore, while they have the potential to improve patient and nonspecialist clinician assessment and patient-clinician communication about potential skin cancer there is the risk that these apps could actually harm users.”
Quote in the paperSubstantial claimMay combine two claims.
PremisecitationSmartphone apps for melanoma detection could actually harm users, for example by providing inaccurate or misleading advice, being used as a substitute for clinical consultation, or delaying melanoma diagnosis.
“The advice could be inaccurate or misleading, apps could be used as a substitute for a clinical consultation, and they could even delay melanoma diagnosis.12”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [12] Robson, 2012, Caution in melanoma risk analysis with smartphone application technology, Br J Dermatol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Study design and conductnone givenThe proforma was completed based only on the online descriptions; no apps were downloaded for data extraction purposes.
“The proforma was completed based only on the online descriptions; no apps were downloaded for data extraction purposes.”
Quote in the paperNot a substantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA recently published systematic review found only two randomized studies that had used smartphone technology.
“a recently published systematic review found only two randomized studies that had used smartphone technology.9”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [9] Dickinson, 2014, Using technology to deliver cancer follow-up: a systematic review, BMC Cancer source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationA 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.
“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”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [7] Wolf, 2013, Diagnostic inaccuracy of smartphone applications for melanoma detection, JAMA Dermatol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
Interpretationown dataImage analysis allows for self-monitoring, and the use of reminders and alerts promote the added use of monitoring skin changes over time.
“For example, image analysis allows for self-monitoring, and the use of reminders and alerts promote the added use of monitoring skin changes over time.”
Quote in the paperNot a substantial claim- Rests on an assumption: Change in a skin lesion is an important predictor of melanoma.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationTwo out of three U.S. clinicians already use smartphone health apps in their practice to manage a range of conditions.
“two out of three U.S. clinicians already use smartphone health apps in their practice to manage a range of conditions. 1”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [1] Senior, 2011, Smart phones: new clinical tools in oncology?, Lancet Oncol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationMany experts remain cautious about the utility of the thousands of apps currently available for the prevention, detection and management of cancer.
“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”
Quote in the paperNot a substantial claim0 of 2 sources checked- cites [6] Bender, 2013, A lot of action, but not in the right direction: systematic review and content analysis of smartphone applications for the prevention, detection, and management of cancer, J Med Internet Res source
Not checked in the free run.
- cites [7] Wolf, 2013, Diagnostic inaccuracy of smartphone applications for melanoma detection, JAMA Dermatol source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe revised 'Glasgow' 7-point checklist was developed and recently validated in a U.K. primary care population.
“particularly for use in the U.K. population where it was developed and recently validated in a primary care population.29”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [29] Walter, 2013, Using the 7-point checklist as a diagnostic aid for pigmented skin lesions in general practice: a diagnostic validation study, Br J Gen Pract source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationAnother recent review has identified patients as having a stronger desire to be informed about underpinning scientific evidence than commercial stakeholders.
“Another recent review has also identified patients as having a stronger desire to be informed about underpinning scientific evidence than commercial stakeholders.32”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [32] Dehzad, 2014, Adopting health apps, what's hindering doctors and patients?, Health source
Not checked in the free run.
Classified as probably not a substantial claim.
Interpretationnone givenThis review is the first to outline the content of available apps, whether they are derived from research evidence, and whether they have been validated.
“It is also the first review to outline the content of available apps, whether they are derived from research evidence, and whether they have been validated.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
InterpretationreasoningIt is likely that small platforms would use the same apps as some were available via both of the major platforms, and the authors are therefore confident that few are missing.
“It is likely that small platforms would use the same appsas some were available via both of the major platformsand we are therefore confident that few are missing.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe review was restricted to the descriptions available from the online stores; more information may have been available after purchasing or using the apps, but this was not undertaken to ensure equity across the review.
“It was restricted to the descriptions available from the online stores; more information may have been available after purchasing or using the apps, but this was not undertaken in order to ensure equity across the review.”
Quote in the paperSubstantial claimProbably not a substantial claim: may not pass S3 (load-bearing).
PremisecitationThe European Medicines Agency has signalled similar intentions to regulate mobile health software.
“the European Medicines Agency has signalled similar intentions.34”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [34] European Medicines Agency Guidelines on the qualification and classification of stand alone software used in healthcare within the regulatory framework of medical devices http://ec.europa.eu/health/medical-devices/files/meddev/2_1_6_ol_en.pdf
Not checked in the free run.
May combine two claims.
Study design and conductreasoningThe review included only apps available in English; therefore it may have failed to identify apps published in other languages.
“We reviewed apps only available in English; therefore we may have failed to identify any apps published in other languages.”
Quote in the paperNot a substantial claimClassified as probably not a substantial claim. May combine two claims.
Study design and conductreasoningThe review only reviewed apps applicable to Apple and Android phones because more than 90% of apps are available via these two platforms.
“Finally, we only reviewed apps applicable to Apple and Android phones as we knew that more than 90% of apps are available via these two platforms.”
Quote in the paperSubstantial claim- Rests on an assumption: More than 90% of smartphone apps are available via Apple and Android platforms.
May combine two claims.
PremisecitationA recent systematic review mapping mobile health research over the last decade identified 117 publications, but concluded that few included large or rigorous evaluation of the novel technologies.
“A recent systematic review mapping mobile health research over the last decade identified 117 publications, but concluded that few included large or rigorous evaluation of the novel technologies.31”
Quote in the paperSubstantial claim0 of 1 sources checked- cites [31] Fiordelli, 2013, Mapping mHealth research: a decade of evolution, J Med Internet Res source
Not checked in the free run.
Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.
PremisecitationThe U.S. Food and Drug Administration (FDA) does not currently regulate the mobile health industry, but there are plans to do so in a similar way to regulation for medical devices.
“although the U.S. Food and Drug Administration (FDA) does not currently regulate the mobile health industry, there are plans to do so in a similar way to regulation for medical devices; 33”
Quote in the paperNot a substantial claim0 of 1 sources checked- cites [33] U.S. Food and Drug Administration Mobile Medical Applications http://www.fda.gov/MedicalDevices/ProductsandMedicalProcedures/ConnectedHealth/MobileMedicalApplications/ucm255978.htm
Not checked in the free run.
- Rests on an assumption: The U.S. Food and Drug Administration (FDA) does not currently regulate the mobile health industry.
The paper relies on this without support.
Change in a skin lesion is an important predictor of melanoma.
“an important predictor of melanoma”
“an important predictor of melanoma”
The paper relies on this without support.
Earlier detection of melanoma would allow timely treatment and could improve outcomes.
“Earlier detection of melanoma would allow timely treatment and could improve outcomes.”
The paper relies on this without support.
Smartphone apps for the identification or management of cancer including melanoma have not been subject to any sort of validation or regulatory controls in the U.S.A., the U.K. or elsewhere.
“Furthermore, smartphone apps for the identification or management of cancer including melanoma have not been subject to any sort of validation or regulatory controls in the U.S.A., the U.K. or elsewhere.”
The paper relies on this without support.
Online store descriptions of apps are sufficient to evaluate app content and allow users to make an informed choice.
“we felt that users should be able to make an informed choice about the apps by reading their description in the online stores.”
The paper relies on this without support.
More than 90% of smartphone apps are available via Apple and Android platforms.
“we knew that more than 90% of apps are available via these two platforms.”
The paper relies on this without support.
The mobile health app market is unregulated.
“particularly as the market is unregulated”
The paper relies on this without support.
The ABCDE method is widely used in the U.S.A.
“where the ABCDE method is widely used”
The paper relies on this without support.
The U.S. Food and Drug Administration (FDA) does not currently regulate the mobile health industry.
“although the U.S. Food and Drug Administration (FDA) does not currently regulate the mobile health industry”
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?
Kassianos 2015 identified 39 applications, and we were only able to identify test accuracy evaluations for five.
“It is notable that Kassianos 2015 identified 39 applications, and we were only able to identify test accuracy evaluations for five.”
From Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma (2018) · cites this paper as Kassianos 2015
Cites
Health Promotion by Social Cognitive Means
Bandura, A. (2004) · Health Education & Behavior · doi:10.1177/1090198104263660 · Cited by 7,238 · see citing works
Patient Self-management of Chronic Disease in Primary Care
Bodenheimer, T. (2002) · JAMA · doi:10.1001/jama.288.19.2469 · Cited by 3,598 · see citing works
Mohr, D. C., Cuijpers, P., & Lehman, K. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1602 · Cited by 1,097 · see citing works
Mapping mHealth research: a decade of evolution
Fiordelli, M., Diviani, N., & Schulz, P. J. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2430 · Cited by 570 · see citing works
Chomutare, T., Fernandez-Luque, L., Årsand, E., & Hartvigsen, G. (2011) · Journal of Medical Internet Research · doi:10.2196/jmir.1874 · Cited by 451 · see citing works
Diagnostic Inaccuracy of Smartphone Applications for Melanoma Detection
Wolf, J. A., Moreau, J. F., Akilov, O., Patton, T., English, J. C., Ho, J., & Ferris, L. K. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.2382 · Cited by 393 · see citing works
Smartphone applications for pain management
Rosser, B. A., & Eccleston, C. (2011) · Journal of Telemedicine and Telecare · doi:10.1258/jtt.2011.101102 · Cited by 364 · see citing works
Bender, J. L., Yue, R. Y. K., To, M. J., Deacken, L., & Jadad, A. R. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2661 · Cited by 346 · see citing works
Muessig, K., Pike, E. C., Legrand, S., & Hightow-Weidman, L. (2013) · Journal of Medical Internet Research · doi:10.2196/jmir.2301 · Cited by 300 · see citing works
Pandey, A., Hasan, S., Dubey, D., & Sarangi, S. (2013) · Journal of Cancer Education · doi:10.1007/s13187-012-0446-9 · Cited by 213 · see citing works
Kroemer, S., Frühauf, J., Campbell, T. M., Massone, C., Schwantzer, G., Soyer, H. P., & Hofmann-Wellenhof, R. (2011) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2011.10208.x · Cited by 202 · see citing works
Semiological value of ABCDE criteria in the diagnosis of cutaneous pigmented tumors
Thomas, L., Tranchand, P., Berard, F., Secchi, T., Colin, C., & Moulin, G. (1998) · Dermatology · doi:10.1159/000017969 · Cited by 195 · see citing works
ABCDE - an evolving concept in the early detection of melanoma
Rigel, D. S., Friedman, R. J., Kopf, A. W., & Polsky, D. (2005) · Archives of Dermatology · doi:10.1001/archderm.141.8.1032 · Cited by 189 · see citing works
Colorectal smartphone apps: opportunities and risks
O’Neill, S., & Brady, R. R. W. (2012) · Colorectal Disease · doi:10.1111/j.1463-1318.2012.03088.x · Cited by 165 · see citing works
What if cancer survival in Britain were the same as in Europe: how many deaths are avoidable?
Abdel-Rahman, M., Stockton, D., Rachet, B., Hakulinen, T., & Coleman, M. P. (2009) · British Journal of Cancer · doi:10.1038/sj.bjc.6605401 · Cited by 160 · see citing works
Using technology to deliver cancer follow-up: a systematic review
Dickinson, R., Hall, S., Sinclair, J. E., Bond, C., & Murchie, P. (2014) · BMC Cancer · doi:10.1186/1471-2407-14-311 · Cited by 146 · see citing works
Self-citationMelanoma Screening with Cellular Phones
Massone, C., Hofmann-Wellenhof, R., Ahlgrimm-Siess, V., Gabler, G., Ebner, C., & Peter Soyer, H. (2007) · PLoS ONE · doi:10.1371/journal.pone.0000483 · Cited by 136 · see citing works
Application of mobile teledermatology for skin cancer screening
Lamel, S. A., Haldeman, K. M., Ely, H., Kovarik, C. L., Pak, H., & Armstrong, A. W. (2012) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2011.11.957 · Cited by 125 · see citing works
HEALSMITH, M. F., BOURKE, J. F., OSBORNE, J. E., & GRAHAM-BROWN, R. A. C. (1994) · British Journal of Dermatology · doi:10.1111/j.1365-2133.1994.tb06881.x · Cited by 124 · see citing works
Mobile applications in dermatology
Brewer, A. C., Endly, D. C., Henley, J., Amir, M., Sampson, B. P., Moreau, J. F., & Dellavalle, R. P. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.5517 · Cited by 121 · see citing works
Medical professional involvement in smartphone ‘apps’ in dermatology: Correspondence
Hamilton, A. D., & Brady, R. R. W. (2012) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2012.10844.x · Cited by 119 · see citing works
Tran, K., Ayad, M., Weinberg, J., Cherng, A., Chowdhury, M., Monir, S., El Hariri, M., & Kovarik, C. (2011) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2010.01.010 · Cited by 111 · see citing works
Walter, F. M., Prevost, A. T., Vasconcelos, J., Hall, P. N., Burrows, N. P., Morris, H. C., Kinmonth, A. L., & Emery, J. D. (2013) · British Journal of General Practice · doi:10.3399/bjgp13x667213 · Cited by 95 · see citing works
Auditing the diagnosis of cancer in primary care: the experience in Scotland
Baughan, P., O'Neill, B., & Fletcher, E. A. (2009) · British Journal of Cancer · doi:10.1038/sj.bjc.6605397 · Cited by 62 · see citing works
Smartphone applications for seizure management
Pandher, P. S., & Bhullar, K. K. (2014) · Health Informatics Journal · doi:10.1177/1460458214540906 · Cited by 53 · see citing works
Lee, J., Nguyen, A. L., Berg, J. P., Amin, A., Bachman, M., Guo, Y., & Evangelista, L. S. (2014) · JMIR mhealth and uhealth · doi:10.2196/mhealth.3196 · Cited by 51 · see citing works
Enhanced Skin Self-examination: A Novel Approach to Skin Cancer Monitoring and Follow-up
Janda, M., Loescher, L. J., & Soyer, H. P. (2013) · JAMA Dermatology · doi:10.1001/jamadermatol.2013.1218 · Cited by 50 · see citing works
Adopting health apps, what's hindering doctors and patients?
Dehzad, F., Hilhorst, C., de Bie, C., & Claassen, E. (2014) · Health · doi:10.4236/health.2014.616256 · Cited by 46 · see citing works
Caution in melanoma risk analysis with smartphone application technology: Correspondence
Robson, Y., Blackford, S., & Roberts, D. (2012) · British Journal of Dermatology · doi:10.1111/j.1365-2133.2012.11046.x · Cited by 39 · see citing works
Smart phones: new clinical tools in oncology?
Senior, K. (2011) · The Lancet Oncology · doi:10.1016/s1470-2045(11)70116-4 · Cited by 29 · see citing works
Use of photographs illustrating ABCDE criteria in skin self-examination
Robinson, J. K., & Ortiz, S. (2009) · Archives of Dermatology · doi:10.1001/archdermatol.2008.604 · Cited by 19 · see citing works
Skin cancer detection by one click — are we any closer?
Janda, M., & Soyer, H. P. (2013) · The Medical Journal of Australia · doi:10.5694/mja13.11338 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Evaluating mobile phone applications for health behaviour change: A systematic review
McKay, F. H., Cheng, C. C., Wright, A., Shill, J., Stephens, H., & Uccellini, M. (2016) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x16673538 · Cited by 367 · see citing works
Jinnai, S., Yamazaki, N., Hirano, Y., Sugawara, Y., Ohe, Y., & Hamamoto, R. (2020) · Biomolecules · doi:10.3390/biom10081123 · Cited by 276 · see citing works
Freeman, K., Dinnes, J., Chuchu, N., Takwoingi, Y., Bayliss, S. E., Matin, R. N., Jain, A., Walter, F. M., Williams, H. C., & Deeks, J. J. (2020) · BMJ · doi:10.1136/bmj.m127 · Cited by 260 · see citing works
Self-citationTeledermatology: a review and update
Lee, J. J., & English, J. C. (2018) · American Journal of Clinical Dermatology · doi:10.1007/s40257-017-0317-6 · Cited by 238 · see citing works
Mobile Phone Computing and the Internet of Things: A Survey
Kamilaris, A., & Pitsillides, A. (2016) · IEEE Internet of Things Journal · doi:10.1109/jiot.2016.2600569 · Cited by 235 · see citing works
Phillips, M. R., Marsden, H., Jaffe, W., Matin, R., Wali, G. N., Greenhalgh, J., McGrath, E., James, R., Ladoyanni, E., Bewley, A., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.13436 · Cited by 231 · see citing works
Digital neuropsychology: challenges and opportunities at the intersection of science and software
Germine, L., Reinecke, K., & Chaytor, N. S. (2019) · The Clinical Neuropsychologist · doi:10.1080/13854046.2018.1535662 · Cited by 171 · see citing works
Osei, E., & Mashamba-Thompson, T. P. (2021) · Heliyon · doi:10.1016/j.heliyon.2021.e06639 · Cited by 178 · see citing works
Smartphone applications for triaging adults with skin lesions that are suspicious for melanoma
Chuchu, N., Takwoingi, Y., Dinnes, J., Matin, R. N., Bassett, O., Moreau, J. F., Bayliss, S. E., Davenport, C., Godfrey, K., O'Connell, S., et al. (2018) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd013192 · Cited by 144 · see citing works
Self-citationMcKay, F. H., Slykerman, S., & Dunn, M. (2018) · JMIR mhealth and uhealth · doi:10.2196/11130 · Cited by 117 · see citing works
Use of Smartphones for Early Detection of Melanoma: Systematic Review
Rat, C., Hild, S., Rault Sérandour, J., Gaultier, A., Quereux, G., Dreno, B., & Nguyen, J.-M. (2018) · Journal of Medical Internet Research · doi:10.2196/jmir.9392 · Cited by 107 · see citing works
“Greater control by administrative authorities is necessary [45,57,58].”
Beyond Dr. Google: the evidence on consumer-facing digital tools for diagnosis
Millenson, M. L., Baldwin, J. L., Zipperer, L., & Singh, H. (2018) · Diagnosis · doi:10.1515/dx-2018-0009 · Cited by 91 · see citing works
A survey on automated melanoma detection
Okur, E., & Türkan, M. (2018) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2018.04.028 · Cited by 101 · see citing works
Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms
Udrea, A., Mitra, G. D., Costea, D., Noels, E. C., Wakkee, M., Siegel, D. M., de Carvalho, T. M., & Nijsten, T. E. C. (2020) · Journal of the European Academy of Dermatology and Venereology · doi:10.1111/jdv.15935 · Cited by 96 · see citing works
Subhi, Y., Bube, S. H., Bojsen, S. R., Thomsen, A. S. S., & Konge, L. (2015) · JMIR mhealth and uhealth · doi:10.2196/mhealth.4169 · Cited by 93 · see citing works
Smartphone-Based Applications for Skin Monitoring and Melanoma Detection
Chao, E. D., Meenan, C. K., & Ferris, L. K. (2017) · Dermatologic Clinics · doi:10.1016/j.det.2017.06.014 · Cited by 93 · see citing works
Zaidan, A. A., Zaidan, B. B., Albahri, O. S., Alsalem, M. A., Albahri, A. S., Yas, Q. M., & Hashim, M. S. (2018) · Health and Technology · doi:10.1007/s12553-018-0223-9 · Cited by 87 · see citing works
Smartphone apps for cancer: A content analysis of the digital health marketplace
Charbonneau, D. H., Hightower, S., Katz, A., Zhang, K., Abrams, J., Senft, N., Beebe-Dimmer, J. L., Heath, E., Eaton, T., & Thompson, H. S. (2020) · DIGITAL HEALTH · doi:10.1177/2055207620905413 · Cited by 86 · see citing works
Taufiq, M. A., Hameed, N., Anjum, A., & Hameed, F. (2016) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-49655-9_57 · Cited by 84 · see citing works
Dermoscopy for melanoma detection and triage in primary care: a systematic review
Jones, O., Jurascheck, L., van Melle, M., Hickman, S. E., Burrows, N., Hall, P. N., Emery, J. D., & Walter, F. M. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-027529 · Cited by 83 · see citing works
Self-citationA roadmap for the clinical implementation of optical-imaging biomarkers
Waterhouse, D. J., Fitzpatrick, C. R. M., Pogue, B. W., O’Connor, J. P. B., & Bohndiek, S. E. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0392-5 · Cited by 81 · see citing works
Moshi, M. R., Tooher, R. L., & Merlin, T. (2018) · International Journal of Technology Assessment in Health Care · doi:10.1017/s026646231800051x · Cited by 74 · see citing works
Zortea, M., Flores, E. S., & Scharcanski, J. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.10.031 · Cited by 73 · see citing works
Phillips, M. R., Greenhalgh, J., Marsden, H., & Palamaras, I. (2019) · Dermatology Practical & Conceptual · doi:10.5826/dpc.1001a11 · Cited by 71 · see citing works
A comprehensive survey on image-based computer aided diagnosis systems for skin cancer
Hameed, N., Ruskin, A., Abuhassan, K., & Hossain, M. A. (2016) · International Conference on Software, Knowledge, Information Management and Applications (SKIMA) · doi:10.1109/skima.2016.7916221 · Cited by 66 · see citing works
Thissen, M., Udrea, A., Hacking, M., von Braunmuehl, T., & Ruzicka, T. (2017) · Telemedicine and e-Health · doi:10.1089/tmj.2016.0259 · Cited by 63 · see citing works
Jahn, A. S., Navarini, A. A., Cerminara, S. E., Kostner, L., Huber, S. M., Kunz, M., Maul, J., Dummer, R., Sommer, S., Neuner, A. D., et al. (2022) · Cancers · doi:10.3390/cancers14153829 · Cited by 62 · see citing works
Development of Smartphone Apps for Skin Cancer Risk Assessment: Progress and Promise
de Carvalho, T. M., Noels, E., Wakkee, M., Udrea, A., & Nijsten, T. E. C. (2019) · JMIR Dermatology · doi:10.2196/13376 · Cited by 60 · see citing works
Efficacy of smartphone applications in high-risk pigmented lesions
Ngoo, A., Finnane, A., McMeniman, E., Tan, J., Janda, M., & Soyer, H. P. (2018) · Australasian Journal of Dermatology · doi:10.1111/ajd.12599 · Cited by 55 · see citing works
Improving outcomes in patients with melanoma: strategies to ensure an early diagnosis
Cormier, J. N., Voss, R. K., Woods, T. N., Cromwell, K. D., & Nelson, K. C. (2015) · Patient Related Outcome Measures · doi:10.2147/prom.s69351 · Cited by 53 · see citing works
Advances in Early Detection of Melanoma and the Future of At-Home Testing
Garrison, Z. R., Hall, C. M., Fey, R. M., Clister, T., Khan, N., Nichols, R., & Kulkarni, R. P. (2023) · Life · doi:10.3390/life13040974 · Cited by 52 · see citing works
Publicly available apps for cancer survivors: a scoping review
Adam, R., McMichael, D., Powell, D., & Murchie, P. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-032510 · Cited by 50 · see citing works
Self-citationLeachman, S. A., Cassidy, P. B., Chen, S. C., Curiel, C. N., Geller, A. C., Gareau, D. S., Pellacani, G., Grichnik, J. M., Malvehy, J., North, J. P., et al. (2015) · Cancer treatment and research · doi:10.1007/978-3-319-22539-5_3 · Cited by 49 · see citing works
Augustin, M., Wimmer, J., Biedermann, T., Blaga, R., Dierks, C., Djamei, V., Elmer, A., Elsner, P. U., Enk, A., Gass, S., et al. (2018) · JDDG Journal der Deutschen Dermatologischen Gesellschaft · doi:10.1111/ddg.13512 · Cited by 48 · see citing works
Kong, F. W., Horsham, C., Ngoo, A., Soyer, H. P., & Janda, M. (2020) · International Journal of Dermatology · doi:10.1111/ijd.15132 · Cited by 46 · see citing works
Ngoo, A., Finnane, A. R., McMeniman, E., Soyer, H. P., & Janda, M. (2018) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2018.08.004 · Cited by 42 · see citing works
Effect of Color Enhancement on Early Detection of Skin Cancer using Convolutional Neural Network
Setiawan, A. W. (2020) · IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT) · doi:10.1109/iciot48696.2020.9089631 · Cited by 40 · see citing works
Recent advances in deep learning applied to skin cancer detectionPreprint
Pacheco, A. G. C., & Krohling, R. A. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1912.03280 · Cited by 35 · see citing works
Murchie, P., Allan, J., Brant, W., Dennis, M., Hall, S. J., Masthoff, J., Walter, F. M., & Johnston, M. (2015) · BMJ Open · doi:10.1136/bmjopen-2015-007993 · Cited by 33 · see citing works
Self-citationRecent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods
Grossarth, S. N., Mosley, D. A., Madden, C., Ike, J., Smith, I. T., Huo, Y., & Wheless, L. (2023) · Current Oncology Reports · doi:10.1007/s11912-023-01407-3 · Cited by 33 · see citing works
eSkin: Study on the Smartphone Application for Early Detection of Malignant Melanoma
Jaworek-Korjakowska, J., & Kłeczek, P. (2018) · Wireless Communications and Mobile Computing · doi:10.1155/2018/5767360 · Cited by 30 · see citing works
Francese, R., Frasca, M., Risi, M., & Tortora, G. (2021) · Journal of Real-Time Image Processing · doi:10.1007/s11554-021-01109-8 · Cited by 28 · see citing works
Kostopoulos, S. A., Asvestas, P. A., Kalatzis, I. K., Sakellaropoulos, G. C., Sakkis, T. H., Cavouras, D. A., & Glotsos, D. T. (2017) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2017.05.016 · Cited by 28 · see citing works
Horsham, C., Snoswell, C. L., Vagenas, D., Loescher, L. J., Gillespie, N. A., Soyer, H. P., & Janda, M. (2020) · Dermatology · doi:10.1159/000506154 · Cited by 28 · see citing works
Yas, Q. M., Zaidan, A. A., Zaidan, B. B., Hashim, M. S., & Lim, C. K. (2017) · Journal of Circuits Systems and Computers · doi:10.1142/s0218126618300039 · Cited by 27 · see citing works
Ersser, S. J., Effah, A. N. Y., Dyson, J., Kellar, I., Thomas, S., McNichol, E., Caperon, L., Hewitt, C. E., & Muinonen‐Martin, A. (2018) · British Journal of Dermatology · doi:10.1111/bjd.17529 · Cited by 29 · see citing works
Mobile Health Apps for Breast Cancer: Content Analysis and Quality Assessment
Yang, S., Bui, C. N., & Park, K. (2023) · JMIR mhealth and uhealth · doi:10.2196/43522 · Cited by 31 · see citing works
Application of Artificial Intelligence in Clinical Dentistry, a Comprehensive Review of Literature.
Ghods, K., Azizi, A., Jafari, A., & Ghods, K. (2023) · PubMed Central · doi:10.30476/dentjods.2023.96835.1969 · Cited by 24 · see citing works
Walter, F. M., Pannebakker, M. M., Barclay, M., Mills, K., Saunders, C. L., Murchie, P., Corrie, P. G., Hall, P. N., Burrows, N., & Emery, J. D. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.0001 · Cited by 22 · see citing works
Self-citationAutomatic Focus Assessment on Dermoscopic Images Acquired with Smartphones
Alves, J. E., Moreira, D., Alves, P., Rosado, L., & Vasconcelos, M. J. M. (2019) · Sensors · doi:10.3390/s19224957 · Cited by 22 · see citing works
Singh, N., & Gupta, S. K. (2018) · Skin Research and Technology · doi:10.1111/srt.12622 · Cited by 21 · see citing works
User Authentication in Smartphones for Telehealth
Smith, K. A., Zhou, L., & Watzlaf, V. J. M. (2017) · International Journal of Telerehabilitation · doi:10.5195/ijt.2017.6226 · Cited by 20 · see citing works
Smartphone as a Portable Detector, Analytical Device, or Instrument Interface
Hernández, D. B., Marty, J., & Muñoz, R. (2017) · InTech eBooks · doi:10.5772/intechopen.69678 · Cited by 19 · see citing works
Marek, A. J., Chu, E. Y., Ming, M. E., Khan, Z. A., & Kovarik, C. (2018) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2018.02.025 · Cited by 19 · see citing works
Open-source medical devices: Healthcare solutions for low-, middle-, and high-resource settings
Maria, C. D., Pietro, L. D., Ravizza, A., Lantada, A. D., & Ahluwalia, A. D. (2019) · Clinical Engineering Handbook · doi:10.1016/b978-0-12-813467-2.00002-x · Cited by 18 · see citing works
Digital skin imaging applications, part I: Assessment of image acquisition technique features
Sun, M., Kentley, J., Wilson, B. N., Soyer, H. P., Curiel‐Lewandrowski, C. N., Rotemberg, V. M., Halpern, A. C., & ISIC Technique Working Group (2022) · Skin Research and Technology · doi:10.1111/srt.13163 · Cited by 17 · see citing works
Cazzaniga, S., Castelli, E., Landro, A. D., Mercurio, M. D., Imberti, G., Locatelli, G. A., Raponi, F., Vezzoli, P., Gambini, D. M., Damiani, G. B., et al. (2019) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2019.02.036 · Cited by 17 · see citing works
Computer-assisted diagnosis for skin cancer: have we been outsmarted?
Mar, V., Scolyer, R. A., & Long, G. V. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31285-0 · Cited by 15 · see citing works
Urner, E., Delavy, M., Catarino, R., Viviano, M., Meyer-Hamme, U., Benski, A. C., Jinoro, J., Heriniainasolo, J. L., Undurraga, M., Vuyst, H. D., et al. (2017) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6697 · Cited by 15 · see citing works
Feasibility Study of a Smartphone Application for Detecting Skin Cancers in People With Albinism
Rubagumya, F., Nyagabona, S. K., Longombe, A. N., Manirakiza, A. V. C., Ngowi, J. R., Maniragaba, T., Sabushimike, D., Urusaro, S., Ndoli, D. A., Dharsee, N. J., et al. (2020) · JCO Global Oncology · doi:10.1200/go.20.00264 · Cited by 16 · see citing works
Teledermatoskopie mittels Smartphone
Zink, A., Kolbinger, A., Leibl, M., Suárez, I. L., Gloning, J., Merkel, C., Winkler, J. K., Biedermann, T., Ring, J., & Eberlein, B. (2017) · Der Hautarzt · doi:10.1007/s00105-017-4042-0 · Cited by 15 · see citing works
Advances in the diagnosis of pigmented skin lesions
Tschandl, P., & Wiesner, T. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16109 · Cited by 15 · see citing works
Artificial Intelligence in Cutaneous Oncology
Chu, Y., An, H. G., Oh, B. H., & Yang, S. (2020) · Frontiers in Medicine · doi:10.3389/fmed.2020.00318 · Cited by 14 · see citing works
Efficient Melanoma Detection Using Texture-Based RSurf Features
Majtner, T., Yayilgan, S. Y., & Hardeberg, J. Y. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-41501-7_4 · Cited by 13 · see citing works
Ndlovu, K., Stein, N., Gaopelo, R., Annechino, M., Molwantwa, M. C., Monkge, M., Forrestel, A. K., & Williams, V. L. (2023) · JMIR Formative Research · doi:10.2196/48946 · Cited by 14 · see citing works
Bouton, C., Schmeltz, H., Lévèque, C., Gaultier, A., Quereux, G., Dréno, B., Nguyen, J., & Rat, C. (2024) · BMC Health Services Research · doi:10.1186/s12913-024-11106-9 · Cited by 12 · see citing works
Kostopoulos, S. A., Ravazoula, P., Asvestas, P. A., Kalatzis, I. K., Xenogiannopoulos, G., Cavouras, D. A., & Glotsos, D. T. (2017) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-017-9947-8 · Cited by 12 · see citing works
Marek, A. J., Chu, E. Y., Ming, M. E., & Kovarik, C. (2016) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2016.06.005 · Cited by 12 · see citing works
Mills, K., Emery, J. D., Lantaff, R., Radford, M., Pannebakker, M. M., Hall, P. N., Burrows, N., Williams, K. M., Saunders, C. L., Murchie, P., et al. (2017) · BMJ Open · doi:10.1136/bmjopen-2017-017934 · Cited by 12 · see citing works
Self-citationChin, Y. P., Huang, I., Hou, Z. Y., Chen, P., Bassir, F., Wang, H.-H., Lin, Y. T., & Li, Y. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105649 · Cited by 12 · see citing works
Effect of Image Downsizing and Color Reduction on Skin Cancer Pre-screening
Setiawan, A. W., Amir, F., & Resfita, N. (2020) · International Seminar on Intelligent Technology and Its Applications · doi:10.1109/isitia49792.2020.9163734 · Cited by 11 · see citing works
DNVF-Memorandum – Gesundheits- und Medizin-Apps (GuMAs)
Krämer, U., Borges, U., Fischer, F., Hoffmann, W. U., Pobiruchin, M., & Vollmar, H. C. (2019) · Das Gesundheitswesen · doi:10.1055/a-1038-9173 · Cited by 10 · see citing works
Bukke, S. P. N., Kumarachari, R. K., Rajasekhar, E. S. K., Jamal Basha, D., & Kamati, M. (2024) · Discover Sustainability · doi:10.1007/s43621-024-00575-x · Cited by 13 · see citing works
Sar-Graycar, L., Rotemberg, V. M., Matsoukas, K., Halpern, A. C., Marchetti, M. A., & Hay, J. L. (2020) · Journal of the American Academy of Dermatology · doi:10.1016/j.jaad.2020.07.014 · Cited by 10 · see citing works
Sun, M., Kentley, J., Wilson, B. N., Soyer, H. P., Curiel‐Lewandrowski, C. N., Rotemberg, V. M., Halpern, A. C., & the ISIC Technique Working Group (2022) · Skin Research and Technology · doi:10.1111/srt.13195 · Cited by 10 · see citing works
Automated diagnosis of melanoma
Janda, M., & Soyer, H. P. (2017) · The Medical Journal of Australia · doi:10.5694/mja17.00618 · Cited by 9 · see citing works
Leema, A. A., Balakrishnan, P. A., Gopichand, G., & Rajarajan, G. (2025) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2025.1612502 · Cited by 8 · see citing works
Habgood, E., Walter, F. M., O’Hare, E., McIntosh, J., McCormack, C., & Emery, J. D. (2020) · Australasian Journal of Dermatology · doi:10.1111/ajd.13244 · Cited by 8 · see citing works
Self-citationA New Clustering-based Thresholding Method for Human Skin Segmentation Using HSV Color Space
Feitosa, R. D. F., Soares, A. D. S., & Pereyra, L. C. (2018) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc.2018.8538604 · Cited by 8 · see citing works
Open-Source Medical Devices: Concept, Trends, and Challenges Toward Equitable Healthcare Technology
Maria, C. D., Lantada, A. D., Pietro, L. D., Ravizza, A., & Ahluwalia, A. D. (2022) · Springer eBooks · doi:10.1007/978-3-030-79363-0_1 · Cited by 7 · see citing works
Segmentation of Melanoma Skin Lesions Using Anisotropic Diffusion and Adaptive Thresholding
Khan, A. H., Latif, G., Iskandar, D. N. F. A., Alghazo, J. M., & Butt, M. (2018) · International Conference on Biomedical Engineering and Technology (ICBET) · doi:10.1145/3208955.3208961 · Cited by 7 · see citing works
Medical Selfies: Emotional Impacts and Practical Challenges
Diethei, D., Colley, A., Kalving, M., Salmela, T., Häkkilä, J., & Schöning, J. (2020) · International Conference on Human-Computer Interaction with Mobile Devices and Services (MobileHCI) · doi:10.1145/3379503.3403555 · Cited by 7 · see citing works
Automated Diagnosis of Skin Lesions
Santos, F., da Silva, F. M. T. P., & Georgieva, P. (2020) · IEEE International Conference on Intelligent Systems · doi:10.1109/is48319.2020.9200090 · Cited by 7 · see citing works
Habgood, E., McCormack, C. J., Walter, F. M., & Emery, J. D. (2021) · JMIR Dermatology · doi:10.2196/22583 · Cited by 6 · see citing works
Self-citationKavitha, N., Prasad, N. S., Sujeeth, T., Naresh, P., Swathi, R., Sujith, A. V. L. N., & Dileep Kumar Reddy, P. (2025) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v26i1.3501 · Cited by 6 · see citing works
Congdon, N. M., & Davis, C. M. (2023) · Skin Health and Disease · doi:10.1002/ski2.295 · Cited by 6 · see citing works
Smartphone diagnosis of skin cancer: there's not yet an app for that
Finnane, A. R., & Soyer, H. P. (2015) · British Journal of Dermatology · doi:10.1111/bjd.13842 · Cited by 5 · see citing works
Reilly, F., Wani, N., Hall, S., Morgan, H. M., Allan, J., Constable, L., Ntessalen, M., & Murchie, P. (2023) · JMIR Dermatology · doi:10.2196/39544 · Cited by 5 · see citing works
Self-citationMarchetti, M. A., Sar-Graycar, L., Dusza, S. W., Nanda, J. K., Kurtansky, N. R., Rotemberg, V. M., & Hay, J. L. (2022) · JMIR Dermatology · doi:10.2196/36256 · Cited by 5 · see citing works
Oda, J., & Takemoto, K. (2025) · Scientific Reports · doi:10.1038/s41598-025-03546-y · Cited by 5 · see citing works
Deep Transfer Learning for Automated Diagnosis of Skin Lesions from PhotographsPreprint
Rocheteau, E., & Kim, D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2011.04475 · Cited by 8 · see citing works
Beshar, I., Agustí, N., Barajas, K. L., Krause, K. J., Nitecki, R., & Rauh‐Hain, J. A. (2025) · International Journal of Gynecological Cancer · doi:10.1016/j.ijgc.2025.102825 · Cited by 4 · see citing works
Mukherjee, S., Rao, S. R., & Poddar, A. (2025) · Melanoma Research · doi:10.1097/cmr.0000000000001074 · Cited by 4 · see citing works
Kostopoulos, S. A., Glotsos, D. T., Asvestas, P. A., Konstandinou, C., Xenogiannopoulos, G., Sidiropoulos, K., Nikolatou, E. K., Perakis, Κ., Mantzouratos, S., Sakkis, T., et al. (2016) · Image Analysis & Stereology · doi:10.5566/ias.1446 · Cited by 4 · see citing works
An Augmented Reality Mobile Application for Skin Lesion Data Visualization
Francese, R., Frasca, M., Risi, M., & Tortora, G. (2020) · International Conference on Information Visualisation (IV) · doi:10.1109/iv51561.2020.00018 · Cited by 4 · see citing works
Complex Event Processing for Health Monitoring
Pérez-Vereda, A., Flores-Martin, D., Canal, C., & Murillo, J. M. (2019) · Communications in computer and information science · doi:10.1007/978-3-030-16028-9_1 · Cited by 4 · see citing works
Resolution invariant wavelet features of melanoma studied by SVM classifiers
Surowka, G., & OGORZAŁEK, M. J. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211318 · Cited by 4 · see citing works
Artificial Intelligence Based Application Accuracy in Diagnostics of C1-C2 Chronic Venous Disease
Zolotukhin, I. A., Kvasnikov, B. B., Linnik, O. Z., Shiyakhmetov, S. B., & Butova, K. G. (2024) · Journal of Venous Disorders · doi:10.17116/flebo202418021132 · Cited by 3 · see citing works
Pandian, T., Krishnamurthy, V. D., & Hieken, T. J. (2017) · Journal of Dermatology Research and Therapy · doi:10.23937/2469-5750/1510049 · Cited by 3 · see citing works
Automated diagnosis: shedding the light on skin cancer
Raphael, A. P., & Soyer, H. P. (2018) · British Journal of Dermatology · doi:10.1111/bjd.16219 · Cited by 3 · see citing works
- Authors
- Kassianos, A. P., Emery, J. D., Murchie, P., & Walter, F. M.
- Affiliations
- University of Cambridge (GB)
- The University of Melbourne (AU)
- University of Aberdeen (GB)
- Corresponding author
- Walter, F.M.
- Venue
- British Journal of Dermatology
- Publisher
- Oxford University Press (OUP)
- Published
- 2015-05-06
- Volume
- 172
- Issue
- 6
- Pages
- 1507-1518
- ISSN
- 0007-0963 (ISSN-L)
- OpenAlex
- W1998412697
- Semantic Scholar
- 653bd15f84933868fb9cf36147d9360fc691961a
- Cited by
- 151
- References
- 32
- Citations by year
- 2026: 8 · 2025: 6 · 2024: 8 · 2023: 11 · 2022: 8 · 2021: 11 · 2020: 21 · 2019: 18 · 2018: 20 · 2017: 21 · 2016: 13 · 2015: 6
- FWCI
- 2.15
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- British Journal of Dermatology (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Ktisis at Cyprus University of Technology (Cyprus University of Technology) (submittedVersion, cc-by-nc-nd)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion, cc-by-nc)
- Funders
- Health Services and Delivery Research (HS&DR) Programme (RG 68235)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › melanin and skin pigmentation
- Keywords
- melanoma detection, mobile health apps, skin self-examination, ABCDE method, melanoma risk assessment, patient education, app validation, mobile health
- MeSH terms
- Smartphone, Humans, Melanoma, Patient Education as Topic, Skin Neoplasms, Self-Examination, Telemedicine, Risk Assessment, Mobile Applications
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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.
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