Sustainable AI-Powered Systems and Cybersecurity for Health Diagnostics

Abstract

Artificial intelligence (AI) has transformed early disease diagnosis, providing exceptional speed and accuracy. However, fairness and bias challenges remain in healthcare AI, leading to discriminatory outcomes and worsening existing health inequities. These biases often arise from data imbalances, model architecture, and decision-making processes, with socioeconomic factors exacerbating historical disparities. Curating inclusive datasets with diverse demographic and socioeconomic representation is essential. Techniques like adversarial debiasing and data augmentation improve equity, while algorithmic fairness methods, such as regularization and fairness constraints, ensure equitable treatment. Differential privacy protects sensitive attributes, and ensemble methods like fairness-aware boosting uphold accuracy. Ongoing innovation is crucial for developing transparent, fair, and accurate AI systems in healthcare.

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