Artificial intelligence (AI) is increasingly applied in radiology to support diagnostic decision-making and workflow efficiency. However, its deployment in healthcare is challenged by persistent sex- and gender-related biases. Male-dominated training datasets and limited representation of women in research and leadership can reduce the generalizability of diagnostic models to female patients. This chapter examines how these imbalances may contribute to underdiagnosis or misclassification of sex-specific disease phenotypes, while also highlighting the potential of bias-mitigated AI methods to improve healthcare outcomes. Strategies discussed include rigorous subgroup analyses, targeted data collection, and the integration of algorithmic auditing and fairness-oriented machine learning techniques. Emphasizing a multidisciplinary approach, the chapter calls for collaborative efforts among clinicians, data scientists, and policymakers to ensure that AI-driven tools mitigate bias and promote equity in precision imaging.