Chiara Bellatreccia, Daniele Zama, Arianna Dondi, Luca Pierantoni, Andreozzi Laura, Iria Neri, Marcello Lanari, Andrea Borghesi,
Roberta Calegari
Proceedings of the 2nd Workshop on AI bias: Measurements, Mitigation, Explanation Strategies
CEUR Workshop Proceedings
2025
AI-based diagnosis of skin diseases holds considerable promise for increasing healthcare accessibility, however,
its effectiveness is currently limited by several challenges, including fairness. This study analyzes a real-world
dataset collected from an Italian hospital, characterized by limited data availability, leading to poor diversity
and representation—particularly evident in the scarcity of data for certain diseases and darker skin tones.
Such limitations result in substantial classification biases. Additionally, the dataset includes non-dermoscopic,
consumer-grade images that suffer from quality issues like inconsistent lighting and blurriness, complicating the
training of fair and efficient AI models. Conventional strategies to mitigate these problems, such as synthesizing
images for underrepresented groups, are hindered by the difficulty in accurately identifying skin tones from
poor-quality images. Our research introduces a novel pipeline designed to enhance both the accuracy and fairness
of skin disease diagnosis by addressing the challenges posed by real-world data. The proposed solution involves
a two-stage approach: 1) data pre-processing and augmentation to obtain images that more accurately represent
darker skin tones, generated through a state-of-the-art diffusion model; and 2) disease classification employing
deep learning models. This methodology addresses data scarcity and improves fairness, with thorough validation
of real-world data showing enhanced reliability and fairness in predictions across various skin diseases.