Recent discoveries in skin cancer and technological advances making
accurate diagnostics far more practical are rapidly changing how skin cancer is
diagnosed. Imaging skin tumors or relevant skin pathology with high-resolution, noninvasive anatomical methods have increased the diagnostic precision for these very
difficult-to-diagnose entities. The advent of imaging technologies such as Reflectance
Confocal Microscopy (RCM) correlative Optical Coherence Tomography (OCT) now
allows a high-resolution visualization of skin lesions, subsequently allowing for
increased diagnostic accuracy. Novel molecular diagnostics, including gene expression
profiling and mutational analysis, reveal the underlying genetic changes associated
with skin cancers and pave the way for personalized therapy. Integrating artificial
intelligence (AI) into dermatology may yield numerous advantages, including but not
limited to improved clinical accuracy, the ability to detect skin cancers at early stages,
recommendations for skin lesion and eruption treatments, and enhanced continuity of
care. AI-driven diagnostic tools can diagnose skin diseases faster and more accurately
than human dermatologists, which could augment the abilities of dermatologists. Noninvasive methodologies, including molecular imaging, liquid biopsies, and Raman
spectroscopy, have recently become promising diagnostic methods for skin cancer
biomarkers without tissue samples. Wearable diagnostics and nanotechnology advances
provide a valuable opportunity for point-of-care testing and continuous monitoring.
Despite these developments, challenges remain regarding their cost, accessibility, and
integration into clinical practice. This chapter will elaborate on the ultimate
transformative effect of these technological advancements, the remaining challenges,
and the future of skin cancer diagnosis, pointing out the importance of continued
innovation in improving patient outcomes and facilitating clinical procedures.
Keywords: Artificial intelligence, biopsy, Computer-aided diagnosis systems, Diagnosis, Fluorescence in situ hybridization, Machine learning, Melanoma, Next-generation sequencing, Non-melanoma, Polymerase chain reaction, Skin cancer.
