In recent years, the intersection of healthcare and technology has witnessed remarkable advancements, with artificial intelligence (AI) playing a pivotal role in transforming various aspects of patient care. One noteworthy development is the emergence of AI-powered apps designed to detect skin cancer, providing a faster and more accessible means of early diagnosis. With a simple tap and scan, these apps can detect irregularities in moles, lesions, or other skin abnormalities, providing instant risk assessments. The integration of AI algorithms allows for swift image analysis, and users receive real-time feedback on the potential risk level associated with the scanned skin feature. This life-changing innovation holds the potential to revolutionize dermatology and significantly improve patient outcomes.
The Challenge of Skin Cancer
Skin cancer is one of the most common types of cancer globally, with melanoma being the deadliest form. According to the World Health Organization (WHO), skin cancer is the most common cancer worldwide, with an estimated 2 to 3 million non-melanoma skin cancers and 132,000 melanoma skin cancers diagnosed annually. Exposure to ultraviolet (UV) radiation, primarily from the sun and artificial sources like tanning beds, is a significant contributor to skin cancer development. Prolonged and unprotected exposure to UV radiation can cause DNA damage in skin cells, increasing the likelihood of malignant transformation.
Alarmingly, a significant percentage of skin cancer cases can be prevented through adopting sun-protective behaviors and early detection practices. However, the shortage of dermatologists, especially in rural or underserved areas, poses a challenge to timely diagnosis. To combat this, AI-powered skin cancer detection apps leverage the capabilities of machine learning algorithms to analyze images of moles, lesions, or other skin abnormalities. These apps aim to assist individuals in assessing the potential risk of skin cancer at an early stage, facilitating timely medical intervention.
How AI works in Skin Cancer Detection
Image Recognition Algorithms: AI algorithms are trained on vast datasets of dermatological images, learning to recognize patterns associated with both benign and malignant lesions. These images act similarly to a gigantic photo album filled with pictures of various skin conditions, including benign and malignant ones, like moles or lesions. The algorithm then actively studies the images to understand the subtle differences between normal and problematic skin features. They also analyze specific patterns associated with both benign and malignant skin conditions. These patterns could include things like the shape of a mole, the color variations in a lesion, or the irregularities in the borders of s skin feature.. Here’s the cool part: the more images these algorithms see, the better they become at recognizing these patterns. Over time, the AI gets really good at spotting the tiny details that might indicate a potential problem with the skin
Risk Assessment: By evaluating various features such as asymmetry, border irregularity, color variation, and size, the algorithm can assign a risk score to the analyzed skin lesion. If everything looks normal, it gets a low-risk score, saying, “This skin sport seems okay.” But if there are asymmetry, irregular borders, strange colors, or it’s too big, the risk score goes up, like saying, “Hmm, this might need some attention”
Continual Learning: AI systems such as this one are continually refining their performance and more accurate over time because it keeps adding new information to its knowledge bank, ensuring improved accuracy over time.
Notable AI-Powered Skin Cancer Detection Apps
First Derm: First Derm allows users to seek opinions from dermatologists on various skin concerns, also employing AI.
Skin Vision: Using a database of dermatologist-reviewed images, SkinVision allows users to capture and analyze skin sports, offering indications and advice on whether to consult a healthcare professional
DermDetect: This app employs a deep learning algorithm to assess skin lesions, providing users with instant risk assessments and recommendations for further evaluation
Challenges
While the potential of AI-powered skin cancer detection is promising, it is important to acknowledge the challenges and criticisms associated with these applications. A problem with using an AI-powered app is that it can produce false positives or negatives, leading to unnecessary anxiety or delayed medical attention. According to the British Association of Dermatologists, out of 43 apps identified, 26 were able to correctly identify melanoma. However, it has been found that these apps impacted patient visits to the hospital, decreasing patient visits to the dermatologists. AI apps are innovative, but should not be trusted by, or guaranteed reliability. If an individual suspects of an odd growing shape growing on their skin but is hesitant to reach out to the doctor, it is best for that individual to consult their doctor for further examination rather than relying solely on an app where it can risk several false results.
References:
Li, Z., Koban, K. C., Schenck, T. L., Giunta, R. E., Li, Q., & Sun, Y. (2022). Artificial intelligence in dermatology image analysis: Current developments and future trends. Journal of Clinical Medicine, 11(22), 6826. https://doi.org/10.3390/jcm11226826
Radiation: Ultraviolet (UV) radiation and skin cancer. (n.d.). Retrieved December 19, 2023, from https://www.who.int/news-room/questions-and-answers/item/radiation-ultraviolet-(uv)-radiation-and-skin-cancer
Rat, C., Hild, S., Rault Sérandour, J., Gaultier, A., Quereux, G., Dreno, B., & Nguyen, J.-M. (2018). Use of smartphones for early detection of melanoma: Systematic review. Journal of Medical Internet Research, 20(4), e135. https://doi.org/10.2196/jmir.9392
Sun, M. D., Kentley, J., Mehta, P., Dusza, S., Halpern, A. C., & Rotemberg, V. (2022). Accuracy of commercially available smartphone applications for the detection of melanoma. British Journal of Dermatology, 186(4), 744–746. https://doi.org/10.1111/bjd.20903 Assessed and Endorsed by the MedReport Medical Review Board