CONCLUSION
This chapter has sought to provide an introduction and basic structural framework to the study of dermatology by addressing terminology, morphology, pattern recognition, and a number of techniques that will all serve to enhance the practice of clinicopathologic correlation. The desired end results are more accurate diagnoses and better care of patients.
Additional figures and table, Acute cutaneous eruptions in otherwise healthy individuals, available in our eBook (see inside front cover for access code).
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In summary, machine learning and AI will almost certainly guide and help physicians in clinical practice in the future. Maybe it will not be necessary to calculate scores like PASI by hand anymore or it will be easier to narrow down diagnostic gray areas in melanocytic lesions, conduct population-wide skin cancer screenings, and perform semi-automated telemedicine/triage. Automated predictive staging systems may replace TNM staging systems. However, what will become a reality depends on physicians and researchers, technical advances, patients, politics, companies, ethical considerations, and regulatory agencies. There are also many other applications and techniques that are not or only briefly covered here, given limitations on content. Finally, the field of machine learning is fast-paced, and critical review of relevant literature is paramount in understanding new concepts and possibilities of AI.