AI is transforming healthcare by improving diagnoses, therapy and care of patients. However, AI is susceptible to bias through unbalanced training datasets, algorithmic flaws and healthcare system inequities. The bias can translate into unequal healthcare decisions that disproportionately affect marginalized groups of people. To address this, diverse, well-curated datasets and bias-detecting training processes like debiasing through adversarial processes are needed. Explainable AI models and ongoing fairness audits ensure accountability of these processes. Ethical guidelines need to ensure protection of patient confidentiality and informed consent. If left unchecked, AI bias can perpetuate healthcare inequities instead of resolving them. By combining engineering solutions with ethical oversight, medical professionals can create fairer AI-driven tools that benefit all patients. This article explores the challenges of bias in medical AI and strategies to make these technologies more equitable for diverse populations.

This narrative review synthesizes existing literature on the mental health of transgender individuals in Iran, identifies critical research gaps and outlines directions for future inquiry. The findings offer valuable insights for clinicians, researchers and policymakers contributing to the development of culturally informed mental health interventions and evidence-based policy initiatives.