Overview of AI in Skin Cancer Diagnosis

Use of Metadata: Metadata, which includes clinical data (patient demographics, medical history) and technical data (image acquisition details), has shown promise in improving AI performance. Research by Yap et al. found that combining imaging with metadata led to better detection of melanoma and other lesions compared to using only a single image. Roffman et al. demonstrated that a CNN trained on questionnaire data alone achieved 88.5% sensitivity and 62.2% specificity for predicting non-melanoma skin cancer risk.
AI in Smartphone Apps: Numerous smartphone apps claim to offer automated skin lesion diagnoses, but a Cochrane review highlighted a lack of robust evidence supporting their efficacy. The review identified only two studies with high bias risk, showing variable sensitivity (7-73%) and specificity (37-94%) for melanoma diagnosis. Overall, these apps have not yet proven to be reliably accurate and may miss melanoma cases.
Alternative Diagnostic Tests: Besides dermoscopy, other diagnostic methods are being explored. AI applications have shown promise in:
- OCT Images: CNN-based classification of basal cell carcinoma (BCC) achieved 95.4% sensitivity and specificity.
- RCM Mosaics: AI performed semantic segmentation of melanocytic lesions with 76% sensitivity and 94% specificity.
- RCM Image Stacks: AI delineated the stratum corneum and dermal-epidermal junction.
- Hyperspectral Imaging: CNN-based classification of selected nevi and melanomas reached 100% sensitivity but 36% specificity.

One of the most promising applications of AI is in diagnostic imaging, especially for skin cancer detection. Diagnoses in this field often depend on the subjective visual interpretation of clinical and dermoscopic images, making it an ideal candidate for AI applications.

Artificial intelligence (AI) involves creating intelligent machines capable of performing tasks autonomously. Machine learning (ML), a branch of AI, focuses on enabling computers to learn tasks without explicit programming. Modern ML techniques use extensive datasets to identify patterns for classification, showing significant potential in various domains. Among these techniques, deep neural networks, particularly convolutional neural networks (CNNs), have gained prominence due to their superior representation and classification capabilities. CNNs are specialized neural networks designed for image analysis and are typically trained using supervised learning, where images in the training dataset are labeled with diagnoses, serving as the ground truth. These labels are crucial for the system to learn the relationship between input data and diagnoses. (1)

In 2017, Esteva et al. demonstrated the effectiveness of CNNs for image-based classification in dermatology (2). Their novel system, using deep CNNs trained on a large dataset of nearly 130,000 clinical images, including 3,000 dermoscopic images, was evaluated for its ability to differentiate between keratinocyte carcinomas and seborrheic keratosis, as well as melanomas and nevi. The CNN's performance was on par with that of human experts. Subsequent studies by Haenssle et al. (2020) and Tschandl et al. (2019b) confirmed these findings across various image sets and skin disease categories. A meta-analysis of 70 studies on automated systems for diagnosing pigmented skin lesions reported an overall sensitivity of 74% (95% CI 66-80%) and specificity of 84% (95% CI 79-88%) for melanoma diagnosis, comparable to dermatologists' performance. (3)

In the domain of identifying changing or newly emerging lesions on whole-body photographs, AI has already seen widespread utilization. However, the consensus is that AI will complement rather than replace physicians, serving as a tool to support rather than replace human interaction, especially in sensitive areas like cancer diagnosis. (4)

In areas such as aiding nonexperts in diagnosing and treating skin lesions, AI’s potential is largely untapped but promising.

Advantages of AI-Assisted Diagnosis

Speed: AI can quickly process and analyze large volumes of images.
AI could improve access to specialist-level expertise, especially in regions experiencing a shortage of dermatologists and long waiting times for specialist appointments.
AI-based systems are expected to provide higher accuracy compared to human experts.
Consistency: AI algorithms provide consistent results regardless of external factors like fatigue or subjective bias, which can affect human dermatologists.
AI can act as a supplementary tool for dermatologists, providing a second opinion or flagging suspicious lesions for further review
AI systems can also serve as educational resources, helping medical students and practitioners.
In the long run, AI can potentially reduce healthcare costs.