Investigating the Use of Artificial Intelligence for Skin Cancer Detection and Diagnosis in Dermatology.

Thesis Abstract

Abstract
This thesis explores the implementation of artificial intelligence (AI) technology for enhancing skin cancer detection and diagnosis within the field of dermatology. Skin cancer is a prevalent and potentially life-threatening condition that requires early detection for effective treatment. The traditional methods of diagnosing skin cancer rely on visual inspection by dermatologists, which can be subjective and prone to human error. In recent years, AI algorithms have shown promise in accurately identifying skin lesions and aiding in the early detection of skin cancer. The primary objective of this research is to investigate the effectiveness of AI technology in improving the accuracy and efficiency of skin cancer detection and diagnosis. The study will focus on analyzing existing AI algorithms, such as deep learning and machine learning models, that have been developed for skin cancer detection. The research will also explore the challenges and limitations associated with implementing AI technology in dermatology practice. The methodology employed in this study will involve a comprehensive review of the current literature on AI applications in dermatology, particularly in the context of skin cancer detection. The research will also include the development of a pilot AI model for skin cancer detection using a dataset of skin lesion images. The performance of the AI model will be evaluated based on its accuracy, sensitivity, and specificity in comparison to human dermatologists. The findings of this research are expected to contribute to the growing body of knowledge on the potential benefits and limitations of using AI technology in dermatology. The results will provide insights into the feasibility of incorporating AI algorithms into clinical practice for improving skin cancer detection rates and patient outcomes. The implications of this study may lead to the development of more accurate and efficient tools for dermatologists to enhance their diagnostic capabilities and ultimately save lives. In conclusion, this thesis aims to bridge the gap between AI technology and dermatology by investigating its role in skin cancer detection and diagnosis. By leveraging the capabilities of AI algorithms, dermatologists can potentially improve the accuracy and speed of diagnosing skin cancer, leading to better patient outcomes and overall healthcare efficiency. The research findings will contribute to the advancement of AI applications in dermatology and pave the way for future innovations in skin cancer detection methods.