Saidatri Arige^1*, Lakshmana Rao Atmakuri¹_, Rasheed Shaik², Manasa Gude³,  Vijaya Kumar Ghanta⁴ and Ramesh Alluri⁵

¹Department of Pharmaceutical Analysis, V. V. Institute of Pharmaceutical Sciences, Gudlavalleru, Andhra Pradesh, India.

²Department of Pharmaceutics, V. V. Institute of Pharmaceutical Sciences, Gudlavalleru, Andhra Pradesh, India.

³Department of Pharmacology, V. V. Institute of Pharmaceutical Sciences, Gudlavalleru, Andhra Pradesh, India.

⁴Department of Pharmacy Practice, KVSR Siddhartha College of Pharmaceutical Sciences, Vijayawada, Andhra Pradesh, India.

⁵Department of Pharmacology, Vishnu Institute of Pharmaceutical Education & Research, Narsapur, Telangana, India.

Corresponding Author E-mail: sai.dhatri_arige@yahoo.co.in 

DOI : https://dx.doi.org/10.13005/bpj/3071

Abstract

Digital dermoscopy is an advanced, non-invasive imaging technique that enhances conventional dermoscopy by utilizing computerized imaging technology to assess skin lesions, aiding in the early-stage diagnosis of skin cancers, including melanoma, basal cell carcinoma (BCC) and squamous cell carcinoma (SCC). This technique provides high-resolution images, enabling detailed examination and magnification of lesions, which helps clinicians identify subtle changes in lesion morphology over time. Additionally, it allows for long-term monitoring through serial imaging, facilitating more accurate follow-up assessments. Integrating Artificial Intelligence (AI) and machine learning algorithms has significantly improved diagnostic accuracy by enhancing the detection of malignancies, allowing for faster and more reliable analysis of dermoscopic images. Despite its advantages, digital dermoscopy faces several challenges that limit its widespread use. Image quality can be compromised by device limitations, lighting conditions, and operator proficiency, which can affect the accuracy of diagnoses. Furthermore, the technique remains heavily dependent on clinician expertise, as the interpretation of dermoscopic images is complex and requires specialized training. Moreover, the integration of digital dermoscopy into electronic health records (EHR) and telemedicine systems presents logistical and financial challenges, including issues related to data storage, accessibility, and reimbursement. This review provides an in-depth examination of the principles, advantages, limitations and prospects of digital dermoscopy, focusing on its potential to advance dermatological practice through improved diagnostic capabilities, more efficient patient management, and enhanced early detection of skin cancers.

Keywords

Artificial Intelligence; Basal cell carcinoma; Digital Dermoscopy; Melanoma; Skin lesions; Squamous cell carcinoma.

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Copy the following to cite this article: Arige S, Atmakuri L. R, Shaik R, Gude M, Ghanta V. K. Alluri R. Digital Dermoscopy: Advancements in Skin Cancer Diagnosis and Monitoring. Biomed Pharmacol J 2025;18(March Spl Edition).

Copy the following to cite this URL: Arige S, Atmakuri L. R, Shaik R, Gude M, Ghanta V. K. Alluri R. Digital Dermoscopy: Advancements in Skin Cancer Diagnosis and Monitoring. Biomed Pharmacol J 2025;18(March Spl Edition). Available from: https://bit.ly/4gZ4cur

Introduction

Dermoscopy or dermatoscopy is an essential skin diagnostic method that does not require a skin lesion biopsy or excision and involves a dermatoscopy that provides a high-resolution enhancement of light reflection, making it possible to identify skin layers. Employing this technique also helps to increase the diagnostic accuracy of skin malignancies on account of the examination of such aspects as pigment networks, blood vessels, and follicular openings which are usually not conspicuous to the naked eye.¹ Manual dermoscopy has been used widely to diagnose many skin ailments, but it has its weaknesses, such as the inability to store images, compare images at different times, and the lack of automated analysis.

Table 1: Comparison of Traditional Dermoscopy vs Digital Dermoscopy

Feature	Traditional Dermoscopy	Digital Dermoscopy
Image Capture	Real-time visual observation	High-resolution digital images with zooming and storage
Documentation	No capability to store images	Allows storage, comparison, and longitudinal tracking of images
Diagnostic Support	No automated analysis	AI-powered analysis, automated algorithms for diagnosis
Monitoring of Lesions	No tracking of lesion progression	Enables longitudinal monitoring of lesions over time
Remote Consultation (Teledermatology)	Limited capability	High-quality digital images sent for remote evaluation
Cost	Lower initial cost	Higher initial investment, but potentially lower long-term cost

Digital dermatoscopy is the combination of normal dermatoscopes with digital high-magnification cameras. The identification of the chain of images using digital systems for building up images and also for storage and comparison leads to a higher accuracy in the diagnosis. Prominent models of dermoscopy use digital dermoscopy systems to incorporate additional features such as automated image analysis and incorporation of artificial intelligence algorithms to help detect and determine the probability of malignancy of lesional skin.^2-3 These advancements in digital systems help overcome the limitations of traditional dermoscopy by enabling objective image analysis, reducing clinician biases, and providing more analyzable results. They supplement dermoscopy and assist in eliminating the biases inherent to this traditional method of skin imaging by providing more analyzable results.

Figure 1: Basic structure of a DermatoscopeClick here to view Figure

The Principles of Digital Dermoscopy

The key principles of digital dermoscopy include:^4-5

High-Resolution Imaging

It uses high-definition cameras to capture images of skin lesions at a high resolution power. By imaging at this high resolution, clinicians are able to scan for fine details in the structures of the skin, which is key to detecting small changes in lesions and ensuring accurate diagnosis.

Polarized Light

This technology minimizes surface reflections from the skin and enhances the image quality of deeper skin layers and structures that are less frequently seen, such as blood vessels and pigment patterns. Polarized light works by filtering out light that is reflected off the skin’s surface, allowing deeper structures to be visualized without interference from superficial reflections. As a result, it improves the ability to identify malignant features. However, this technique may compromise the overall clarity of the dermoscopic images by removing some surface details, which could impact the visualization of superficial features.

Image Documentation and Tracking

One of the most significant advantages of digital dermoscopy is its ability to systematically document and store skin lesions over time. The ability to track lesion changes and detect suspicious changes early, including signs of skin cancer development, enables clinicians to monitor lesion evolution and diagnose earlier.

Artificial Intelligence (AI) Integration

More digital dermoscopy systems are now partially or fully aided by AI and machine learning algorithms to help analyze dermoscopic images. With these tools, patterns associated with different skin conditions, such as melanoma and non-melanoma skin cancers (like basal cell carcinoma and squamous cell carcinoma), can be identified, which increases diagnostic accuracy and decreases human error. Prominent examples of AI algorithms currently in use include deep learning models like convolutional neural networks (CNNs) for detecting melanoma and systems developed by companies like SkinVision and MoleMap, which have demonstrated clinical success in accurately identifying malignant lesions in real time.

Teledermatology

Telemedicine is also supported by digital dermoscopy through the ability to transfer high-quality images to dermatologists for remote evaluation. This is particularly helpful in underserved areas, where access to specialists is limited, providing timely diagnosis and treatment.

Additional Considerations Regarding AI Integration

While AI-powered digital dermoscopy systems have shown significant promise, there are still limitations and challenges that must be addressed. One major challenge is the possibility of false positives or false negatives, which can occur when the AI system misinterprets the image or fails to recognize subtle malignant features. This emphasizes the importance of human oversight in the diagnosis process, as dermatologists must still review AI-assisted findings to ensure accurate diagnosis and avoid misdiagnosis. Furthermore, AI algorithms may struggle with certain skin tones or uncommon skin conditions, which could lead to reduced accuracy in diverse patient populations.

Applications in Dermatology

Skin Cancer Early Detection

Skin cancer (melanoma, basal cell carcinoma (BCC), squamous cell carcinoma (SCC)) is one of the most common cancers worldwide. Improving patient outcomes relies on early detection and numerous studies have shown that digital dermoscopy plays a critical role in the early identification of skin cancers. Digital dermoscopy has been shown to improve sensitivity and specificity when used in addition to clinical examination.⁶ For instance, studies have demonstrated that digital dermoscopy increases the sensitivity of melanoma detection to approximately 95% compared to 80% with clinical examination alone. Additionally, its specificity has been reported to exceed 80%, helping to distinguish malignant lesions from benign ones with greater accuracy.

Figure 2: Example of a Melanoma Lesion under DermoscopyClick here to view Figure

Melanoma Diagnosis

Although it is the least common form of skin cancer, melanoma is the deadliest, and early detection is essential for improving survival rates. Digital dermoscopy enables clinicians to identify characteristic features of melanoma, such as asymmetry, irregular borders, multiple colors, and atypical blood vessels, with much greater precision.⁷ Recent studies have shown that digital dermoscopy can reduce diagnostic errors in melanoma detection by up to 50% when compared to traditional clinical examinations. Different algorithms have been developed to assist clinicians in diagnosing melanoma, such as the 7-point checklist and ABCD rule, which combine dermoscopic features to assist in identifying high-risk lesions.^8,9 Moreover, AI-enhanced digital dermoscopy systems have been shown to improve diagnostic accuracy by up to 20% when compared to traditional methods, providing a promising tool for early melanoma detection.

Non-Melanoma Skin Cancers

Strains of common non-melanoma skin cancers—both BCC and SCC—are also prevalent and can lead to significant morbidity. Digital dermoscopy helps to distinguish these malignancies from benign lesions such as seborrheic keratosis and actinic keratosis.^10,11 In addition, digital dermoscopy can be used to monitor the evolution of pre-cancerous lesions, with subtle changes indicating the onset of malignancy.¹² For instance, studies have demonstrated that digital dermoscopy can detect BCC with sensitivity of up to 90% and specificity of around 85%. This makes it a valuable tool for identifying both melanoma and non-melanoma cancers early.

Lesions over Time Monitoring

Digital dermoscopy has the significant advantage of recording and tracking skin lesions over time. It is especially helpful for patients who have many moles or are at high risk for developing skin cancer, such as those with a personal or family history of melanoma.¹³ By comparing serial images, clinicians can detect subtle changes in lesion size, shape, and/or color, which may indicate the development of malignancy.¹⁴ Longitudinal tracking with digital dermoscopy has been shown to improve the detection of melanoma recurrences by up to 35%, underscoring its importance in continuous skin monitoring.¹⁵

Figure 3: Digital Dermoscopy WorkflowClick here to view Figure

Teledermatology

In recent years, teledermatology, or transmitting digital images for remote consultation, has become increasingly popular. Teledermatology offers one such tool—digital dermoscopy—to provide patients in rural or underserved areas with expert dermatologic care without requiring a patient to travel. Studies have shown that teledermatology consultations involving digital dermoscopy result in faster diagnoses and improved access to care in underserved areas.^16-18 Relatives do not have to visit dermatologists in person for diagnosis or advice on further action, as high-quality digital images can be sent electronically. This has the potential to decrease wait times to see a dermatologist and increase access to care, particularly in areas with limited healthcare resources.

Figure 4: Digital Dermoscopy in TeledermatologyClick here to view Figure

Advantages of Digital Dermoscopy

Enhanced Visualization

Digital dermoscopy provides high-resolution images that improve the visualization of dermal and epidermal structures, enabling clinicians to detect features that may be missed during physical exams.¹⁹

Prospective Monitoring and Recordings

The ability to store and compare images over time allows for longitudinal tracking of lesions, helping to detect morphological changes and potential malignancies, particularly in high-risk patients.^20,21

Support for Artificial Intelligence Integration

AI systems in dermoscopy analyze large datasets of skin images, improving diagnostic accuracy.²² These systems have been shown to match or exceed dermatologists’ performance in detecting conditions like melanoma, although AI is designed to complement human expertise, not replace it.^23-26

Telemedicine and Remote Access

Teledermatology enables remote skin lesion analysis, expanding access to dermatological care in underserved areas. This facilitates timely consultations and triage, ensuring patients with limited access to specialists receive proper care.^27-33

Challenges and Limitations

Image Quality and Standardization

The accuracy of digital dermoscopy depends on image quality, which can be impacted by lighting, camera settings, and capture methods. Standardizing imaging protocols is crucial for consistent diagnostic results.^34-35

Expertise and Training

Accurate diagnosis through digital dermoscopy requires clinician proficiency in using the equipment and interpreting images. Ongoing training ensures clinicians stay updated on the latest technologies.^36-37

Integration with Electronic Health Records (EHR)

While integrating digital dermoscopy with EHR systems enhances patient care by providing easy access to lesion history and enabling longitudinal tracking, technical challenges and high costs hinder widespread adoption.³⁸

Cost and Accessibility

Despite decreasing costs, digital dermoscopy remains expensive, limiting its availability in smaller clinics or resource-limited areas. Teledermatology services also face accessibility challenges in rural settings, highlighting the need for more affordable solutions and infrastructure.^39-40

Comparative Analysis of Digital Dermoscopy Systems Findings

Several studies have compared the effectiveness of various digital dermoscopy systems in diagnosing skin cancer and monitoring lesions over time. A key comparison between high-resolution digital dermoscopy and traditional dermoscopy has demonstrated that the digital version allows for more precise documentation and the ability to analyze images over time, improving the detection of skin cancers. Additionally, studies comparing AI-enhanced digital dermoscopy systems with traditional methods have shown that AI systems can identify suspicious lesions with greater accuracy and consistency, reducing the risk of human error.⁴¹

A comparative study also highlighted the advantages of teledermatology using digital dermoscopy in underserved areas, where access to dermatologists is limited. These studies have demonstrated that remote consultations, combined with digital dermoscopy, resulted in more timely diagnoses and better outcomes for patients with skin lesions. However, challenges remain in terms of standardizing protocols and ensuring high image quality for teledermatology consultations.⁴²

Alternative Approaches to Skin Cancer Diagnosis

While digital dermoscopy has shown significant promise in enhancing diagnostic accuracy and supporting telemedicine, it’s important to also consider traditional methods of skin cancer diagnosis. These include clinical visual examinations, biopsy, and histopathological analysis, all of which have long been the gold standard for detecting skin cancer, particularly melanoma. However, these methods present several limitations compared to digital dermoscopy. Traditional visual examination relies heavily on the clinician’s experience and may miss subtle signs of malignancy. Biopsy, though definitive, is invasive and may not be ideal for patients with numerous lesions or those in the early stages of malignancy. Moreover, these conventional methods do not offer the same ability to track lesion changes over time, which digital dermoscopy excels at, providing a more comprehensive approach to skin cancer monitoring.

Despite these limitations, traditional methods still play a crucial role in diagnosis, particularly when digital dermoscopy alone cannot confirm a malignancy. A combination of clinical examination, dermoscopy, and biopsy continues to be the gold standard, especially in complex cases. Exploring this synergy between traditional and digital methods can help clinicians make more accurate decisions in practice.

Technological Advancements and Future Directions

Artificial Intelligence and Machine Learning

The future of digital dermoscopy is closely tied to advancements in artificial intelligence (AI) and machine learning. AI algorithms are increasingly adept at analyzing dermoscopic images to detect skin cancer, with studies showing that AI systems can identify melanoma and other skin cancers with accuracy comparable to dermatologists. For example, AI-powered systems have been shown to identify melanoma with up to 94% sensitivity and 91% specificity. These advancements promise to improve diagnostic accuracy, reduce human error, and enable faster, more reliable diagnoses for patients.

Integration of Dermoscopy with Other Diagnostic Tools

Digital dermoscopy may be integrated with other diagnostic tools to create a more holistic approach to skin cancer detection. Combining dermoscopy with genetic testing, ultrasound, and biopsies could lead to a more comprehensive understanding of a patient’s condition and enhance diagnostic accuracy. One challenge is the practical integration of genetic data with dermoscopic images, which may require advancements in data processing and patient management systems. Advances in technology may also facilitate real-time collaboration between dermatologists and pathologists, allowing for more accurate diagnoses and personalized treatment plans.⁴³

Enhanced Mobile Dermoscopy Platforms

The development of mobile dermoscopy platforms is a promising advancement. Mobile devices like smartphones and tablets, combined with portable dermatoscopes, allow clinicians to capture high-quality images in various settings, including remote areas. Smartphone attachments like DermLite or iPhone dermoscopy lenses enable high-resolution image capture and teledermatology consultations. This expands access to dermatologic care, especially in rural or underserved communities, where specialists may not be readily available. Mobile dermoscopy systems support telemedicine by enabling remote consultations with dermatologists for expert evaluation.⁴⁴

Global Impact and the Role of Digital Dermoscopy in Public Health

Reducing Mortality from Skin Cancer Globally

Digital dermoscopy holds the potential to significantly reduce skin cancer mortality rates across the globe by improving the early detection and treatment of skin cancers, particularly melanoma. Early detection is critical for improving outcomes, as melanoma is highly treatable when diagnosed at an early stage. Digital dermoscopy enables clinicians to capture high-resolution images of lesions that might otherwise be missed, providing a tool for accurate and timely diagnosis. The use of digital dermoscopy in clinical practice, especially when paired with AI-assisted diagnostic tools, can identify suspicious lesions and assist in making early interventions before the cancer reaches an advanced and fatal stage. In regions where skin cancer rates are high, such as Australia and the United States, and in areas with significant at-risk populations, the widespread adoption of digital dermoscopy could reduce mortality rates through faster detection and more targeted treatments.⁴⁵

Access to Dermatologic Care in Low-Resource Settings

In many developing countries, access to dermatologic care is limited due to a shortage of trained dermatologists and the high cost of medical services. Digital dermoscopy offers a solution by allowing clinicians in low-resource settings to conduct detailed skin examinations without requiring specialized expertise in traditional dermatology techniques. Digital images can be transmitted to dermatologists in larger cities or more developed regions for remote analysis. This process, known as teledermatology, provides essential dermatologic care to underserved populations. Furthermore, digital dermoscopy’s cost-effective nature and the initial setup in place make it an attractive option for resource-limited settings. Countries in Africa, Asia, and Latin America could greatly benefit from this technology, improving access to dermatological diagnoses and treatments and ultimately reducing the burden of skin diseases in these regions.⁴⁶

Effectiveness in Teledermatology for Rural Areas

Teledermatology, which involves the remote evaluation of dermatologic conditions through digital imaging, is particularly effective in rural and remote areas, where access to dermatologists is often scarce. Digital dermoscopy plays a pivotal role in this context by providing high-quality images of skin lesions that can be sent to dermatologists for evaluation. This method not only reduces the need for patients to travel long distances but also improves the speed of diagnosis and the efficiency of care delivery. Studies have shown that teledermatology using digital dermoscopy can achieve diagnostic accuracy similar to that of in-person consultations, especially in the case of common conditions like melanoma, basal cell carcinoma, and other skin diseases. This enables patients in rural areas to receive timely, accurate diagnoses, which is crucial for effective treatment and reducing the risk of complications. By enabling faster and more reliable diagnosis, teledermatology contributes to improving public health in underserved populations.⁴⁷

Comparative Studies and Evidence

Clinical Trials Comparing Digital Dermoscopy to Traditional Methods

Several clinical trials have compared digital dermoscopy with traditional methods to assess its accuracy and effectiveness in diagnosing skin cancer. In one notable study, digital dermoscopy was found to offer higher sensitivity and specificity than clinical examination alone in detecting melanoma. The study showed that digital dermoscopy could identify melanoma in its early stages, which is crucial for improving patient outcomes. Furthermore, when combined with dermoscopic algorithms and AI assistance, digital dermoscopy outperformed traditional methods in terms of diagnostic accuracy. Another comparative trial demonstrated that digital dermoscopy was significantly more accurate in detecting non-melanoma skin cancers, such as basal cell carcinoma (BCC), compared to clinical inspection alone.⁴⁸

Meta-Analysis of AI-assisted Dermoscopy Systems

Meta-analyses of AI-assisted dermoscopy systems have shown that artificial intelligence can play a critical role in enhancing the diagnostic capabilities of digital dermoscopy. In a systematic review and meta-analysis, researchers evaluated the performance of AI models in diagnosing melanoma and other skin cancers. The results indicated that AI-assisted systems were able to achieve diagnostic accuracy comparable to experienced dermatologists. In some cases, AI systems demonstrated a higher level of consistency, as they are not subject to the variability and subjectivity that may affect human diagnosticians. These systems have the potential to significantly reduce diagnostic errors and help healthcare providers in regions with limited access to trained dermatologists. The integration of AI with digital dermoscopy could therefore revolutionize skin cancer diagnosis, making it faster and more accurate, and improving outcomes globally.⁴⁹

Outcomes in Teledermatology Using Digital Dermoscopy

The outcomes of teledermatology using digital dermoscopy have been extensively studied, with several studies highlighting the effectiveness of remote consultations in diagnosing and managing skin diseases. One study evaluated the diagnostic accuracy of teledermatology using digital dermoscopy in remote areas of the United States and found that teledermatology had a diagnostic accuracy rate of 89% for skin cancer, which is comparable to in-person examinations. Teledermatology has also been shown to reduce wait times for consultations and improve patient access to dermatologic care. In rural and underserved areas, digital dermoscopy paired with teledermatology has been particularly effective in providing timely diagnoses and follow-up care for patients, leading to better management of skin conditions and improved public health outcomes. Furthermore, outcomes have been promising in terms of reducing the need for unnecessary biopsies, as digital dermoscopy allows for more accurate assessments of lesions.^50-56

Conclusion

Digital dermoscopy is transforming the field of dermatology by enhancing the accuracy of skin cancer diagnosis and improving patient outcomes. Its ability to capture high-resolution images, track lesion progression, and integrate artificial intelligence for diagnostic support positions it as a critical tool in modern dermatologic care. Despite challenges such as standardization, clinician training, and costs, the ongoing advancements in AI and imaging technology are addressing these barriers. Moreover, digital dermoscopy’s role in teledermatology expands access to care, particularly in underserved areas, reducing delays in diagnosis and treatment. However, while digital dermoscopy shows significant promise in early skin cancer detection, its impact on mortality rates and overall patient outcomes is still being evaluated, and more concrete studies or case examples are needed to establish these effects definitively.

As digital dermoscopy continues to evolve, it promises to play an essential role in improving the efficiency and accessibility of dermatologic care globally. Its integration into routine practice and continued development will shape the future of dermatology, but it is important to acknowledge that, at present, there are limitations to the technology that must be addressed, including image standardization and the need for ongoing clinician training. Furthermore, the increasing use of AI and machine learning might revolutionize dermoscopy, but it remains uncertain whether these technologies will make dermoscopy a standard part of routine skin exams across diverse healthcare systems.

Future research should focus on improving the quality of dermoscopic images, refining AI training, and exploring ways to integrate digital dermoscopy into routine healthcare systems. Such efforts will help overcome existing challenges and move digital dermoscopy closer to becoming a ubiquitous tool in dermatology.

Acknowledgement

The authors would like to acknowledge V. V. Institute of Pharmaceutical Sciences, Gudlavalleru. 

Funding Sources

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Conflict of Interest

The author(s) do not have any conflict of interest.

Data Availability Statement

This statement does not apply to this article.

Ethics Statement

This research did not involve human participants, animal subjects, or any material that requires ethical approval.

Informed Consent Statement

This study did not involve human participants, and therefore, informed consent was not required.

Clinical Trial Registration

This research does not involve any clinical trials

Author Contributions

Saidatri Arige: Conceptualization, Original Draft;

Lakshmana Rao Atmakuri: Data Collection, Analysis;

Rasheed Shaik: Visualization, Supervision;

Manasa Gude : Funding Acquisition, Resources;

Vijaya Kumar Ghanta: Writing, Project Administration;

Ramesh Alluri: Review, Editing. 

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