The Accuracy of Skin Cancer Detection Rates with the Implementation of Dermoscopy Among Dermatology Clinicians: A Scoping Review

by Kathryn Harrison DMSc, MMS, PA-C, CAQ-Dermatology
Dr. Harrison is with Forefront Dermatology in Englewood, Colorado, and is a Diplomat Fellow of the SDPA; she was with the Doctor of Medical Science Program, AT Still University in Mesa, Arizona at the time of writing.

Funding: No funding was received for this article.

Disclosures: The author has no conflicts of interest relevant to the content of this article.

J Clin Aesthet Dermatol. 2024;17(9–10 Suppl 1):S18–S27.

Abstract

Objective: The goal is to determine if the implementation of dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians and identify the optimal training method for dermatology clinicians to become proficient in dermoscopy.

Methods: A comprehensive search through the A.T. Still Memorial Library, including the electronic health databases PubMed, Scopus, UpToDate, and CINAHL, was performed. Google Scholar search results were sorted by relevance, and the first 30 pages were included within the search due to the large quantity of results. The search keywords included “skin cancer diagnosis,” “accuracy,” “detection,” “dermoscopy,” and “dermatologists.” The search was performed in July 2023. The date limitations used within the search parameters ranged from 2017 to 2023 to review the past seven years of publications. The search evaluated reference lists and encompassed those that met the inclusion and exclusion criteria. Dermatologists, dermatology physician assistants, dermatology nurse practitioners, and primary care practitioners were eligible for inclusion. The search included literature from any country. The English language was the only language permitted within the search. Gray literature was included in the search using news, press release, and MedRxiv.

Results: A total of 28 articles met the inclusion criteria. All of the articles included were from peer-reviewed sources and in the English language. The articles came from 10 different countries of origin and were published from 2017 to 2023. The main results of the scoping review discovered that the use of dermoscopy improves the accuracy of skin cancer diagnosis. The results also demonstrated that dermoscopy training is highly variable; multiple different types of diagnostic algorithms are used in the professional medical education systems of the 10 countries included within the scoping review. The dermoscopy training algorithms recommended include pattern analysis, 7-point checklist, Menzies method, Triage Amalgamated Dermoscopy Algorithm, Australasian College of Dermatology Dermoscopy Course, 3-point checklist, ABCD rule, Skin Imaging College of China, and no particular algorithm. Of these, the three most commonly recommended included the 7-point checklist, Menzies method, and pattern analysis.

Conclusion: The results demonstrated that dermoscopy improves the accuracy of skin cancer diagnosis for dermatology clinicians and primary care providers. Key implications of these findings for practice include earlier skin cancer detection, which can lead to reduced rates of morbidity and mortality, reduced overall healthcare costs, reduced number of benign lesions biopsied, and improved patient outcomes.

Keywords: Skin cancer diagnosis, accuracy, detection, dermoscopy, dermatologists

Introduction

Skin cancer is the most common malignancy worldwide. Approximately one out of five Americans will be diagnosed with skin cancer by the age of 70 years.¹ The two most common types of cutaneous malignancies are basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). Approximately 5.4 million BCCs and SCCs are diagnosed annually in the United States (US), and that number continues to rise.² Mortality rates from BCC and SCC are low, with an estimated 2,000 to 8,000 people dying from these cancers annually in the US.² Malignant melanoma is the least common type of cutaneous malignancy and has the highest mortality rate. From 2014 to 2024, the number of invasive melanoma diagnoses increased by 32 percent.¹ In the US, the annual cost of treating skin cancers is approximately $8.1 billion.¹ When skin cancer is detected early, morbidity and mortality are significantly reduced; thus, the accuracy of diagnosis by a medical professional is essential.

The dermatology medical professional team comprises dermatologists, dermatology physician assistants (PAs), and dermatology nurse practitioners (NPs). Skin cancer detection training is provided in medical school, dermatology residency, PA and NP school, and PA/NP clinical rotations. Dermoscopy is a noninvasive technique used for examining skin lesions that can be used by a dermatology clinician to detect skin cancer. A dermatoscope consists of a transilluminating light source and magnifying lens, most commonly with 10-fold magnification.³

The first handheld dermatoscope, the DermLite, was invented in 2001 and consequently gave rise to the modern dermatoscope used in clinics today.⁴ The use of dermoscopy is steadily increasing. A 2002 survey of 456 US dermatologists reported that 23 percent of respondents used dermoscopy, and in 2009, a survey of members of the American Academy of Dermatology (AAD) reported that 48 percent of respondents used dermoscopy.⁴ A 2014 survey of 500 US dermatologists reported that 80.7 percent used dermoscopy.⁵ Prevalence of use varies by country, with dermatologists in Australia and European countries using dermoscopy at a higher frequency than US-based dermatologists.⁶

Formal training on dermoscopy provided for dermatologists, PAs, and NPs remains highly variable within the medical education system. According to a 2017 survey, only 35 percent of US dermatology residents received dermoscopy training within their residency program.⁷ Students in PA and NP programs typically gain access to dermoscopy training on elective dermatology clinical rotations if the assigned preceptor utilizes a dermatoscope in their practice.

The use of a dermatoscope has increased in popularity among dermatology clinicians to improve the diagnostic accuracy of cutaneous malignancies, decrease the number of benign skin lesions biopsied, reduce overall healthcare costs, and improve patient outcomes. For these reasons, a scoping review is essential to systematically map the current research on this topic and identify any potential gaps in knowledge. This review aims to address the following questions: 1. Does the implementation of dermoscopy improve the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians? and 2. If dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection, what is the optimal training method for dermatology clinicians to become proficient in dermoscopy?

Methods

The methods for this scoping review followed the JBI Manual for Evidence Synthesis guidelines.⁸ Nine steps were followed to complete the scoping review.

1. A scoping review protocol, “The Accuracy of Skin Cancer Detection Rates with the Implementation of Dermoscopy Among Dermatology Clinicians: A Scoping Review Protocol,” was followed and can be accessed at the Open Science Framework (OSF) website at https://osf.io/anryd/ (DOI 10.17605/OSF.IO/ANRYD). A deviation from the protocol was necessary due to the large quantity of search results from Google Scholar; therefore, the search was limited to the first 30 pages containing 10 articles per page.
2. Eligibility criteria were clearly defined.
3. Information sources were described, including electronic databases and search dates.
4. Search strategy was described, including any limitations encountered.
5. Selection of sources of evidence process was described in a flow diagram and narrative form.
6. Data charting process methods were described.
7. Data items were listed and defined.
8. Critical appraisal of individual sources of evidence was performed.
9. Synthesis of the results was described and summarized in tables, figures, and narrative form.
      

Inclusion criteria. Population. The specific population examined for the skin cancer detection scoping review included dermatologists, dermatology PAs, dermatology NPs, and primary care clinicians. Skin cancer accuracy and specificity were investigated using visual examination and dermoscopy. The population included clinicians of any age, race, sex, ethnicity, and years of experience. Dermatologists who practice as Mohs surgeons and all other medical specialties were excluded from the review. This cohort was excluded because they do not perform routine skin cancer screenings and therefore are not relevant to include in the scoping review.

Concept. The core concept of the skin cancer detection scoping review was to conduct an extensive literature review to assess the accuracy and specificity of skin cancer detection among dermatology clinicians with the implementation of dermoscopy. The accuracy of diagnosis was assessed in various study designs, including systematic review, retrospective cohort, quasi-experimental, case report, survey, randomized controlled trial, scoping review, qualitative, and meta-analysis.

Context. Dermoscopy is used worldwide by dermatologists and primary care medical professionals. The scoping review included studies conducted and published globally. There were no limitations on the geographic area, specific setting, specific community, or patient Fitzpatrick skin phototype. The Fitzpatrick skin phototype is established at birth, and the classification (I–VI) depends on the amount of melanin present in the skin and the effects of ultraviolet light exposure on the skin.⁹ The study incorporated all seasons to conduct a comprehensive literature review, as skin cancer diagnosis has a statistically higher likelihood in the months of May to October for all types of cutaneous malignancies.¹⁰

Search strategy. A comprehensive search through the A.T. Still Memorial Library was conducted, including the electronic health databases PubMed, Scopus, UpToDate, and CINAHL. Google Scholar was also searched, with the results limited to the first 30 pages sorted by relevance. The search keywords included “skin cancer diagnosis,” “accuracy,” “detection,” “dermoscopy,” and “dermatologists.” The date limitations used within the search parameters ranged from 2017 to 2023 to review the past seven years of publications. The search evaluated reference lists and encompassed those that met the inclusion and exclusion criteria. The search included literature from any country. The English language was the only language permitted within the search. Gray literature was included in the search using news, press releases, and MedRxiv.

Source of evidence selection. The initial screening of article selection began with reviewing the title and keywords for inclusion and exclusion criteria. Articles that met the inclusion criteria were saved to the Zotero website to organize and screen for duplicates. After searching each electronic database, duplicates were identified and removed. The remaining articles were imported to the Covidence Website for further review. A detailed review of abstracts was conducted, and the articles were voted on to include, exclude, or undecided. Full-text review of the articles that were labeled uncertain was completed to determine whether they met the inclusion criteria. The final 28 articles included within the scoping review proceeded to data extraction.

Data extraction. Data from each study were systematically extracted using a data extraction template on the Covidence website. The data extracted included authors, publication date, title, journal, page numbers, URL, DOI, sample, aim of study, a summary of content, and analysis. Each source was critically appraised following the Critical Appraisal Checklist from the Student’s 4 Best Evidence website.¹¹ This method appraises the introduction, methods, results, discussion, and conclusion by having the researcher answer a series of 20 questions to ensure high-quality evidence was included in the scoping review.¹¹

Analysis and presentation of results. The accuracy, sensitivity, specificity, and dermoscopy training methodologies for skin cancer detection were analyzed. A narrative report with tables and figures was produced to summarize the findings on the impact of the implementation of dermoscopy for dermatology clinicians. The results were described to answer the research questions within the context of the study’s overall purpose. Gaps in knowledge were identified with the completion of the analysis.

The scoping review results were presented within tables, figures, and a descriptive analysis format. Charts were created based on the type of skin cancer being detected using dermoscopy, including BCC, SCC, malignant melanoma, and generalized skin cancer. A separate chart was created to present the findings on dermoscopy training methods and to address years of experience of the dermatology clinician.

Results

Search results. A total of 17,292 sources of evidence were identified. The large quantity of search results, 16,800, from Google Scholar alone, was limited to the first 30 pages containing 10 articles per page for a total of 300 articles screened by title and abstract. There were 492 search results from electronic health database through the A.T. Still Memorial library. A total of 792 records were identified and screened based on the title and abstract. There were 45 duplicates identified, and 652 articles were ineligible based on the inclusion criteria. Ninety-five unique articles were screened, and 45 were irrelevant. Full-text screening of 50 articles was completed, and of those, 22 were excluded, resulting in a total of 28 articles included in the review. Figure 1 illustrates a flow diagram of the search process.

Inclusion of sources of evidence. The articles included were published from 2017 to 2023 (2017: n=1, 2018: n=5, 2019: n=7, 2020: n=2, 2021: n=5, 2022: n=5, and 2023: n=3). The majority (35.7%) of articles included were conducted within the US (n=10). Other countries of origin included Italy (n=5), United Kingdom (n=3), Spain (n=3), Australia (n=2), Brazil (n=1), China (n=1), Colombia (n=1), Poland (n=1), and Romania (n=1). Ten different study designs were used within the included articles, including systematic review (n=8), retrospective cohort (n=6), quasi-experimental (n=3), non-study design article (n=2), case report (n=2), survey (n=2), randomized, controlled trial (n=1), scoping review (n=1), qualitative (n=1), and meta-analysis (n=1). Types of cutaneous malignancy identified included BCC (n=7), SCC (n=3), malignant melanoma in situ (n=2), malignant melanoma (n=9), cutaneous metastasis of malignant melanoma (n=3), and skin cancer unspecified (n=4). Table 1 provides an overview of the included sources of evidence.

Review findings. The scoping review objective was to determine whether the implementation of dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians and identify the optimal training method for dermatology clinicians to become proficient in dermoscopy.

Melanoma. There were 18 articles that described the use of dermoscopy to improve the diagnostic accuracy of malignant melanoma.^3,12–28 In 17 of the 18 articles, melanoma diagnostic accuracy improved with the use of dermoscopy.

Sensitivity. The sensitivity of diagnostic accuracy of melanoma was found to increase with the use of dermoscopy. Holmes et al¹⁴ demonstrated that the sensitivity for the diagnosis of melanoma increased from 60.9 percent to 85.4 percent with the use of dermoscopy and training programs, compared to visual examination alone. Michalak-Stoma et al²⁰ reported that the use of dermoscopy increased the sensitivity of detecting malignant skin lesions and reduced the number of benign lesions removed. Hoorens et al²⁵ determined that the use of dermoscopy improved the sensitivity for the diagnosis of melanoma from 76.0 percent with visual examination to 94.0 percent with dermoscopy. Within the systematic review by Shen et al,²⁸ one study with two cohorts demonstrated a sensitivity of 91.7 and 95.8 percent for diagnosis of malignant melanoma using dermoscopy.

Specificity. The specificity of diagnostic accuracy of melanoma was found to increase with the use of dermoscopy, with the exception of one study. Holmes et al¹⁴ found that a baseline specificity of 85.4 percent decreased to 73.0 percent with the 7-point checklist, 77.7 percent with the Menzies method, and 80.4 percent with the ABCD rule. Yélamos et al²¹ reported the use of dermoscopy improves specificity for the diagnosis of melanoma to 90 percent versus 81 percent with visual examination alone. Carrera et al²² reported clinicians with six or more years of dermoscopy practice had statistically different dermoscopy specificity (61.1±13.3) versus those with no dermoscopic training (45.6=/+13.6). Aviles-Izquierdo et al’s²⁴ results revealed a specificity of 81 percent regarding the accuracy of diagnosis of cutaneous metastases of malignant melanoma color-based dermoscopic patterns. Hoorens et al²⁵ demonstrated that dermoscopy increased specificity for skin cancer diagnosis significantly from 70.6 percent to 84.6 percent. Within the review by Shen et al,²⁸ one study with two cohorts revealed a specificity of 83.6 and 86.0 percent for the diagnosis of malignant melanoma using dermoscopy.

Overall diagnostic accuracy of melanoma increased with the use of dermoscopy. Adler et al¹⁵ reported the diagnostic accuracy to be 15.6 times higher for dermoscopy compared to visual examination with the naked eye. Nazzaro et al¹⁶ concluded that the addition of methodical dermoscopic evaluation for routine clinical practice is most likely the cause of the increased melanoma detection seen in the study results. Yélamos et al²¹ demonstrated improved melanoma diagnostic accuracy from 71 percent with visual examination alone to 90 percent with dermoscopy. Clinical diagnostic accuracy was higher in the clinician group with six or more years of dermoscopy practice (64.6±9.7) versus those with no dermoscopic training (59.9±9.5).²² The UpToDate article by Marghoob and Jaimes³ reported that 86 percent of dermoscopy users from 32 European countries reported that dermoscopy increased their melanoma detection rate by 70 percent. Mazzella et al²³ presented two case reports describing the use of dermoscopy to help distinguish cutaneous metastases of malignant melanoma from a benign angioma.

Harkemanne et al²⁷ evaluated both short- and long-term outcomes with dermoscopy training programs. Within the short-term cohort, eight clinical diagnostic training programs and seven dermoscopy training programs were assessed for short-term efficacy of the program. Thirteen of the studies demonstrated significant improvement; however, two did not show any significant improvement with diagnostic accuracy of malignant melanoma.²⁷ Within the long-term cohort, four clinical diagnostic training programs and three dermoscopy training programs were assessed for long-term efficacy of the program.²⁷ Three studies reported significant improvement in the general practitioners’ performances in melanoma diagnosis, demonstrating 1.25-times greater accuracy and a reduced incidence of advanced melanomas due to earlier detection. One study failed to show any improvement.²⁷

The use of dermoscopy allows for deeper evaluation of a skin lesion that can lead to earlier diagnosis, less medicolegal cases due to delayed diagnosis, and improved melanoma-specific survival rates.^{12,13,17,18,26} Refer to Table 2 and Figure 2 for a summary of these findings.

BCC and SCC. Seven articles described the use of dermoscopy to improve the diagnostic accuracy of BCC and SCC. Accuracy was determined by calculating the sensitivity, specificity, and an overall accuracy based on number of benign and malignant skin lesions biopsied.

Sensitivity. The sensitivity of diagnosis of BCC overall improved with the use of dermoscopy. Hoorens et al²⁵ found the sensitivity for BCC increased with the use of dermoscopy; however, the number failed to reach statistical significance. In the review by Shen et al,²⁸ one study indicated a sensitivity of 98.8 percent for the diagnosis of BCC using dermoscopy. Alvarez-Salafrenca et al²⁹ reported that following the Menzies Dermoscopy Training Model revealed a 97-percent sensitivity for the diagnosis of BCC. A dermoscopy training model for superficial spreading BCC showed an 81.9-percent sensitivity. Reiter et al³⁰ summarized an overall sensitivity of 91.2 percent. Five trials compared the sensitivity between dermoscopy and visual examination with the naked eye.³⁰ The sensitivity was 85 percent for dermoscopy plus naked eye exam and 66.9 percent for naked eye examination alone.³⁰ Yuki et al³² found a diagnostic sensitivity of 92.2 percent when using dermoscopy to diagnose a BCC.

Specificity. The overall specificity of diagnosis of BCC improved with dermoscopy. Alvarez-Salafrenca et al²⁹ reported that following Menzies Dermoscopy Training Model revealed 93- and 92-percent specificity for differentiating BCC from melanoma and other benign pigmented skin lesions, respectively. A dermoscopy training model for superficial spreading BCC showed a specificity of 81.8 percent.²⁹ Reiter et al³⁰ revealed an overall specificity of 94.8 percent for the dermoscopy diagnosis of BCC. In the review by Shen et al,²⁸ one study indicated a specificity of 89.7 percent for the diagnosis of BCC using dermoscopy. Yuki et al³² revealed a diagnostic specificity of 96.0 percent when using dermoscopy to diagnose a BCC.

There are limited articles on the use of dermoscopy to improve the diagnostic accuracy of SCC. One study showed that the sensitivity of diagnosis of SCC increased with the use of dermoscopy; however, it failed to reach statistical significance.²⁵ There were no articles included within this scoping review regarding the specificity of SCC.

Dinnes et al’s³¹ Cochrane Review meta-analysis found in-person evaluations of dermoscopy to be more accurate than visual inspection alone for the detection of BCC, with a relative diagnostic odds ratio of 8.2 (95% confidence interval: 3.5–19.3; p<0.001). Jones et al¹⁹ displayed a significant increase in skin lesions excised and detection of keratinocyte carcinomas, including BCC and SCC, after the dermoscopy education program was completed, resulting in a median of five after the dermoscopy education program, compared to a median of three prior to the program (p=0.013). See Table 3 and Figure 3 for a summary of these findings.

Skin cancer unspecified. Five articles described the use of dermoscopy to improve the diagnostic accuracy of generalized skin cancer diagnosis. Fee et al³³ completed a qualitative review with 12 general practitioners, and on interview, participants reported an improved accuracy of skin lesion diagnosis with dermoscopy. Cyr et al³⁴ performed a study that revealed dermoscopy training improved diagnostic accuracy of cutaneous malignancies. The mean pre-test scores were 20.1 for attending physicians, 20.3 for resident physicians, and 15.8 for medical students. The mean post-test scores increased to 24.5 for attending physicians, 25.9 for resident physicians, and 24.1 for medical students. Jones et al’s³⁵ summarized results of the review suggested that implementing dermoscopy in primary care improves accuracy of skin cancer diagnosis compared to visual examination with the naked eye. De Bedout et al³⁶ demonstrated an improved sensitivity for any skin cancer using clinical images alone, from 60.1 percent to 72.4 percent after dermoscopy training. Sinz et al³⁷ revealed an overall sensitivity for all types of nonpigmented malignancies was 77.6 percent with dermoscopy and 72.3 percent without dermoscopy. See Table 4 and Figure 4 for a summary of these results.

Dermoscopy training methods. A total of 21 articles (Table 5 and Figure 5) addressed dermoscopy training methods and algorithms implemented to become proficient in using dermoscopy for skin cancer detection.^{3,13–15,17,19–22,25–32,34–36,38} The three most commonly used training methods included the 7-point checklist (n=4), Menzies method (n=4), and pattern analysis (n=4). Less common methods and algorithms investigated in this scoping review included the ABCD rule (n=3), clinical experience (n=3), and continuous training unspecified (n=3).

7-point checklist. Mihulecea et al¹³ concluded that the 7-point checklist proved to be a useful tool for the classification of melanocytic nevi and for differentiating them from melanoma. Michalak-Stoma et al²⁰ determined that the 7-point checklist is insufficient to diagnose melanoma in situ; however, it is an accurate method for detecting malignant melanoma. Barcaui and Miot et al²⁶ reported that the 7-point checklist was used by 14 respondents (2%) and concluded that it is a simplified method best used for non-dermatology clinicians. Marghoob and Jaimes³⁸ concluded that novices in dermatology will benefit from quantitative methods, such as the 7-point checklist.

Menzies method. Yuki et al³² concluded that when dermatology residents used the 2-step algorithm and Menzies method, their diagnostic accuracy improved, and sensitivity increased with experience, then plateaued after seven months. Marghoob and Jaimes³⁸ concluded that novices in dermatology benefit from the quantitative methods, such as the Menzies method. Barcaui and Miot²⁶ evaluated diagnostic algorithms in pigmented lesions, and 18 (2%) respondents used the Menzies method. Researchers determined the Menzies method is a simplified method best used for nondermatology clinicians.²⁶ Alvarez-Salafranca et al²⁹ advised that the Menzies method is useful for diagnosing BCC.

Pattern analysis. Mihulecea et al¹³ also found the pattern analysis algorithm to be a useful tool for the classification of melanocytic nevi and for differentiating them from melanoma. Barcaui and Miot²⁶ concluded that pattern analysis algorithm is the best method to detect malignant melanoma. Marghoob and Jaimes³⁸ concluded that the pattern analysis algorithm has a superior specificity compared to other quantitative scoring systems and is preferred by most experienced clinicians because it is highly sensitive and specific and requires in-depth knowledge of both global and local features of benign nevi and melanoma.

ABCD rule. Marghoob and Jaimes³ concluded that in a systematic review of 27 studies, the degree of experience of the clinician did not affect the performance of simpler algorithms, including the ABCD rule. Barcaui and Miot²⁶ reviewed the diagnostic algorithms in pigmented lesions and summarized that 125 (15%) respondents used the ABCD rule. Marghoob and Jaimes³⁸ conclude that novices in dermatology benefit from quantitative methods, such as the ABCD rule.

Clinical experience. Yélamos et al²¹ concluded that during the training phase, clinicians tend to increase sensitivity and lower specificity. However, after gaining experience, the clinicians’ specificity also increases. Marghoob and Jaimes³ reported that in a systematic review of 27 studies, the degree of experience of the clinician improved diagnostic accuracy of complex algorithms, including pattern analysis. Reiter et al³⁰ determined that experience significantly increased the sensitivity, but not specificity, of BCC diagnosis using dermoscopy.

Continuous training unspecified method/algorithm. Hoorens et al²⁵ concluded that a trend was noted toward increased sensitivity and specificity with increased training; however, training for more than 10 hours did not reach statistically significant superior results. Continuous training for dermatologists is recommended and courses should also focus on benign skin lesion evaluation.²⁵ Harkemanne et al²⁷ concluded that one-day training was sufficient to improve the confidence of general practitioners with an emphasis on melanoma diagnosis. To retain dermoscopy diagnostic skills over the long-term, refresher courses are recommended at regular intervals and should not exceed seven months after the initial training.²⁷ Jones et al³⁵ summarized that the optimal length of dermoscopy training was not identified. Previous studies have shown improvement after dermoscopy training lasting two days and 10 weeks.³⁵ Primary care providers likely need short training courses with regular updates.³⁵

Other methods for dermoscopy training include 3-point checklist (n=1), Triage Amalgamated Dermoscopy Algorithm (TADA) (n=1), Australasian College of Dermatology Dermoscopy Course (n=1), mastery learning courses (n=1), Skin Imaging College of China (n=1), no pattern analysis or algorithm (n=1), and 2-step algorithm (n=1).

Discussion

This scoping review concluded that the implementation of dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians and identified the optimal dermoscopy training methods and algorithms. A total of 28 articles met the inclusion and exclusion criteria to be selected for the scoping review. All of the articles included were from peer-reviewed sources and written in the English language. Articles were from 10 different countries and published from 2017 to 2023. The results were heterogeneous. Articles were selected that evaluated generalized skin cancer detection and specific types of skin cancer detection, and study designs included review articles, case reports, and dermoscopy training programs collecting pre- and post-test data from participants.

The optimal length of dermoscopy training for a dermatology clinician to become proficient remains unclear. There was conflicting evidence from several articles included in this scoping review, with training ranging from 90 minutes to several months, and brief refresher courses were also included. This scoping review identified a gap in current knowledge, indicating a need for further research on the optimal length of dermoscopy training.

Limitations. There are some limitations to this scoping review. First, relevant sources could have been omitted from this review due to the possibility of them not being visible within the search results. The review was also required to limit the search results from Google Scholar to the first 30 pages due to the large quantity of search results. Furthermore, this scoping review was limited to a solo researcher; therefore, there was no collaboration on article inclusion, exclusion, or data extraction characteristics. Another limitation identified is the lack of studies that specifically included a sample population  with PAs and NPs. The implications for PA and NP academic training and clinical practice are inferred based on the data for physicians, which does not account for the differences in academic training and clinical practice.

Conclusion

The results demonstrate that dermoscopy improves the accuracy of skin cancer diagnosis for dermatology clinicians and primary care providers. Key implications of these findings for practice include earlier skin cancer detection, which can lead to reduced rates of morbidity and mortality, reduced overall healthcare costs, reduced number of benign lesions biopsied, and improved patient outcomes.

The evidence suggests that the optimal training method for dermatology clinicians to become proficient in dermoscopy is pattern analysis. While there are several other reliable methods and algorithms available, the evidence supports pattern analysis as the optimal training method for dermatology clinicians. Other methods, including the 7-point checklist, Menzies method, ABDC rule, 3-point checklist, TADA, and the 2-step algorithm, can be useful for less experienced clinicians or primary care providers.   

Dermoscopy training is highly variable within the professional medical education system worldwide. The 10 countries included within this scoping review all offered differing educational programs with an assortment of the methods, algorithms, and length of time required for proficiency to train medical professionals on dermoscopy in medical school, residency, or post-graduate symposiums. Based on these results, even a brief introduction to dermoscopy training for 1 to 2 days in medical school, PA school, and NP school would result in improved accuracy of skin cancer diagnosis.

Implications of the findings for research. Key implications of these findings for research include the need for further studies to evaluate the best methods, including the length of training, to become proficient in pattern analysis.

Implications of the findings for practice. Dermatology clinicians should use dermoscopy for patients who present for a skin cancer screening appointment. Dermatology clinicians should acquire proficiency in the pattern analysis method to determine if a skin lesion is benign or malignant.

Acknowledgements

This scoping review manuscript was written to complete the A.T. Still University Doctor of Medical Sciences capstone project. The author must acknowledge her capstone facilitator, Dr. Tony Stephas, for his support during this process.

References

1. The Skin Cancer Foundation. Skin cancer facts & statistics. Updated Feb 2024. Accessed 8 Mar 2024. https://www.skincancer.org/skin-cancer-information/skin-cancer-facts/
2. American Cancer Society. Key statistics for basal and squamous cell skin cancers. Updated 31 Oct 2023. Accessed 8 Mar 2024. https://www.cancer.org/cancer/basal-and-squamous-cell-skin-cancer/about/key-statistics.html
3. Marghoob AA, Jaimes N. Overview of dermoscopy. UpToDate. Updated 9 Aug 2023. Accessed 5 Mar 2024. https://www.uptodate.com/contents/overview-of-dermoscopy
4. Dodson J, Lio P. Dermoscopy: past and present. J Integrative Dermatol. 2023. https://www.jintegrativederm.org/article/68088-dermoscopy-past-and-present
5. Murzaku EC, Hayan S, Rao BK. Methods and rates of dermoscopy usage: a cross-sectional survey of US dermatologists stratified by years in practice. J Am Acad Dermatol. 2014;71(2):393–395.
6. Ring C, Cox N, Lee JB. Dermatoscopy. Clin Dermatol. 2021;39(4):635–642.
7. Fried LJ, Tan A, Berry EG. Dermoscopy proficiency expectations for US dermatology resident physicians: results of a modified Delphi survey of pigmented lesion experts. JAMA Dermatol. 2021;157(2):189–197.
8. Aromataris E, Munn Z, eds. JBI Manual for Evidence Synthesis. JBI; 2020.
9. Oakley A. Fitzpatrick skin phototype. DermNet. 2012. Accessed 24 Apr 2024. https://dermnetnz.org/topics/skin-phototype
10. Samia AM, Nenow J. Seasonal variation in the diagnosis of skin cancers from 1983 to 2017 in Greenville, North Carolina. Cureus. 2022;14(3):e23254.
11. Will R. Critical appraisal: A checklist. Student’s 4 Best Evidence. 6 Sep 2016. Accessed 23 Aug 2023. https://s4be.cochrane.org/blog/2016/09/06/critical-appraisal-checklist/
12. Bolcato V, Michelerio A. Dermoscopy for cutaneous melanoma: under the eye of both the dermatologist and the legal doctor. Dermatol Pract Concept. 2022;12(3):e2022100.
13. Jitian Mihulecea CR, Iancu GM, Leventer M, Rotaru M. The many roles of dermoscopy in melanoma detection. Life. 2023;13(2):477.
14. Holmes GA, Vassantachart JM, Limone BA, et al. Using dermoscopy to identify melanoma and improve diagnostic discrimination. Fed Pract. 2018;35(Suppl 4):S39–S45.
15. Adler NR, Kelly JW, Guitera P, et al. Methods of melanoma detection and of skin monitoring for individuals at high risk of melanoma: new australian clinical practice. Med J Aust. 2019;210(1):41–47.
16. Nazzaro G, Passoni E, Pozzessere F, et al. Dermoscopy use leads to earlier cutaneous melanoma diagnosis in terms of invasiveness and size? A single-center, retrospective experience. J Clin Med. 2022;11(16):4912.
17. Dinnes J, Deeks JJ, Chuchu N, et al. Dermoscopy, with and without visual inspection, for diagnosing melanoma in adults. Cochrane Database Syst Rev. 2018;12(12):CD011902.
18. Conforti C, Giuffrida R, Vezzoni R, et al. Dermoscopy and the experienced clinicians. Int J Dermatol. 2020;59(1):16–22.
19. Jones SM, Walker H, Maitland C. A dermoscopy training program for Victorian GPs to improve skin cancer prevention and detection. Public Health Res Pract. 2022;32(1):3212207.
20. Michalak-Stoma A, Małkińska K, Krasowska D. Usefulness of dermoscopy to provide accurate assessment of skin cancers. Clin Cosmet Investig Dermatol. 2021;14:733–746.
21. Yélamos O, Braun RP, Liopyris K, et al. Usefulness of dermoscopy to improve the clinical and histopathologic diagnosis of skin cancers. J Am Acad Dermatol. 2019;80(2):
    365–377.
22. Carrera C, Segura S, Aguilera P, et al. Dermoscopy improves the diagnostic accuracy of melanomas clinically resembling seborrheic keratosis: cross-sectional study of the ability to detect seborrheic keratosis-like melanomas by a group of dermatologists with varying degrees of experience. Dermatology. 2017;233(6):471–479.
23. Mazzella C, Costa C, Cappello M, Scalvenzi M. Difficult to diagnose small cutaneous melanoma metastases mimicking angiomas: utility of dermoscopy. Int J Dermatol. 2018;57(9):1085–1087.
24. Avilés-Izquierdo JA, Ciudad-Blanco C, Sánchez-Herrero A, et al. Dermoscopy of cutaneous melanoma metastases: a color-based pattern classification. J Dermatol. 2019;46(7):564–569.
25. Hoorens I, Vossaert K, Lanssens S, et al. Value of dermoscopy in a population-based screening sample by dermatologists. Dermatol Pract Concept. 2019;9(3):200–206.
26. Barcaui CB, Miot HA. Profile of the use of dermoscopy among dermatologists in Brazil. An Bras Dermatol. 2020;95(5):602–608.
27. Harkemanne E, Baeck M, Tromme I. Training general practitioners in melanoma diagnosis: a scoping review of the literature. BMJ Open. 2021;11(3):e043926.
28. Shen X, Yu RX, Shen CB, et al. Dermoscopy in China: current status and future prospective. Chin Med J (Engl). 2019;132(17):2096–2104.
29. Álvarez-Salafranca M, Ara M, Zaballos P. Dermoscopy in basal cell carcinoma: an updated review. Actas Dermosifiliogr (Engl Ed). 2021;112(4):330–338.
30. Reiter O, Mimouni I, Gdalevich M, et al. The diagnostic accuracy of dermoscopy for basal cell carcinoma: a systematic review and meta-analysis. J Am Acad Dermatol. 2019;80(5):1380–1388.
31. Dinnes J, Deeks JJ, Chuchu N, et al. Visual inspection and dermoscopy, alone or in combination, for diagnosing keratinocyte skin cancers in adults. Cochrane Database Syst Rev. 2018;12(12):CD011901.
32. Yuki A, Takatsuka S, Abe R, Takenouchi T. Diagnostic accuracy of dermoscopy for 934 basal cell carcinomas: a single-center retrospective study. J Dermatol. 2023;50(1):64–71.
33. Fee JA, McGrady FP, Hart ND. Dermoscopy use in primary care: a qualitative study with general practitioners. BMC Prim Care. 2022;23(1):47.
34. Cyr PR, Craig W, Ahrns H, et al. Teaching skin cancer detection to medical students using a dermoscopic algorithm. PRiMER. 2021;5:6.
35. Jones OT, Jurascheck LC, van Melle MA, et al. Dermoscopy for melanoma detection and triage in primary care: a systematic review. BMJ Open. 2019;9(8):e027529.
36. De Bedout V, Williams NM, Muñoz AM, et al. Skin cancer and dermoscopy training for primary care physicians: a pilot study. Dermatol Pract Concept. 2021;11(1):e2021145.
37. Sinz C, Tschandl P, Rosendahl C, et al. Accuracy of dermatoscopy for the diagnosis of nonpigmented cancers of the skin. J Am Acad Dermatol. 2017;77(6):1100–1109.
38. Marghoob AA, Jaimes N. Dermoscopic evaluation of skin lesions. UpToDate. Updated Mar 2024. Accessed 24 Mar 2024. https://www.uptodate.com/contents/dermoscopic-evaluation-of-skin-lesions