The field of medical diagnosis is rapidly evolving with the integration of artificial intelligence (AI) and machine learning (ML) techniques. Recent studies have focused on addressing bias and improving data quality in AI-powered diagnostic models, particularly in the context of otoscopic image analysis and pain detection. The development of synthetic datasets, such as SynPAIN, has shown promise in mitigating algorithmic bias and improving model generalizability. Furthermore, research has highlighted the importance of careful data curation and bias mitigation to ensure equitable AI deployment in pathology. Noteworthy papers include: SynPAIN, a large-scale synthetic dataset for pain detection, which demonstrates the utility of synthetic data in identifying algorithmic bias and improving model performance. The paper on bias analysis for synthetic face detection introduces an evaluation framework for analyzing bias in synthetic face detectors and provides an extensive case study on the bias level of state-of-the-art detectors.