Date of Award
2023
Document Type
Dissertation
Publication Status
Version of Record
Submission Date
October 2026
Thesis/Dissertation Advisor [Chair]
Marques, Oge
Abstract
Advancements in Artificial Intelligence (AI) and Machine Learning (ML) have significantly improved their application in dermatology. However, bias issues in AI systems can result in missed diagnoses and disparities in healthcare, especially for individuals with different skin types. This dissertation aims to investigate and improve the fairness and bias in machine learning models for dermatology by evaluating and enhancing their performance across different Fitzpatrick skin types. The technical contributions of the dissertation include generating metadata for Fitzpatrick Skin Type using Individual Typology Angle; outlining best practices for Explainable AI (XAI) and the use of colormaps; developing and enhancing ML models through skin color transformation and extending the models to include XAI methods for better interpretation and improvement of fairness and bias; and providing a list of steps for successful application of deep learning in medical image analysis. The research findings of this dissertation have the potential to contribute to the development of fair and unbiased AI/ML models in dermatology. This can ultimately lead to better health outcomes and reduced healthcare costs, particularly for individuals with different skin types.
Extent
164 p.
Recommended Citation
Corbin, Adam, "INVESTIGATING AND IMPROVING FAIRNESS AND BIAS IN MACHINE LEARNING MODELS FOR DERMATOLOGY" (2023). Electronic Theses and Dissertations 2020 - Present. 1216.
https://digitalcommons.fau.edu/etd_general/1216
Identifier
FA00014131
Comments
Includes bibliography.|Dissertation (PhD)--Florida Atlantic University, 2023.|FAU Electronic Theses and Dissertations Collection|Includes bibliography.