Recent advancements in artificial intelligence (AI) have demonstrated notable improvements in medical diagnostics, particularly in cancer detection and prenatal assessments. AI systems have enhanced mammogram sensitivity, especially for aggressive interval breast cancers, by flagging lesions that clinicians later confirmed, enabling earlier intervention while maintaining clinician involvement. At Radboud University Medical Center, AI has been shown to reduce radiologist workload without compromising breast cancer screening accuracy. In addition, an AI model developed at Shenzhen University generates high-quality breast ultrasound reports, aiding junior radiologists in improving diagnosis of BI-RADS categories. Another AI model at the City University of New York (CUNY) achieved state-of-the-art performance in breast cancer detection on MRI scans and has been released as open-source software. Furthermore, AI has been applied successfully in prenatal care, with models accurately identifying and measuring the nuchal translucency plane in real-time ultrasound assessments, facilitating workflow integration. Beyond breast cancer, AI identified early prostate cancer in over 80% of biopsy samples previously classified as healthy by pathologists, detecting subtle tissue changes invisible to the human eye. Additional AI applications include expert-level quantification of paraspinal muscle volume and fat measurements via MRI at Griffith University and the development of AI systems trained with pathologists’ eye-tracking data that replicate expert diagnostic expertise while reducing annotation workload. These developments underscore AI's growing role in enhancing diagnostic accuracy and efficiency across multiple medical imaging domains.