This narrative review discusses the importance of high-quality data in machine learning and artificial intelligence (AI) for radiology. It outlines techniques and tools for ensuring data consistency, standardization, traceability, correct annotation, and de-identification, with a focus on radiological imaging and freely available software. Key topics include image resolution, pixel depth, file formats for medical images, anonymization and pseudonymization to protect patient privacy, image annotation tools, and data harmonization and normalization. The review emphasizes the need for standardized image curation and annotation to enable advanced techniques like federated learning, which addresses data governance and privacy without sharing data. It highlights the role of software tools such as ImageJ and 3D Slicer in processing medical images for AI research. Anonymization techniques, including facial structure removal, are discussed, along with free and commercial tools for image annotation. The review also covers medical image formats, including DICOM, NIfTI, and Analyze, and their use in radiology. It discusses key imaging concepts such as pixel depth, photometric interpretation, and metadata. Free software for medical imaging is highlighted, including ImageJ, 3D Slicer, and ITK-Snap, which are used for image processing, segmentation, and anonymization. The review addresses data privacy regulations, such as GDPR and HIPAA, and the importance of obtaining patient consent for data usage. It discusses anonymization and pseudonymization methods, data encryption, access control, and data retention periods. The review also covers the use of AI in image annotation, where pre-trained models can assist in identifying regions of interest, improving efficiency and accuracy. Finally, the review discusses data curation and storage, emphasizing the need for secure, consistent, and high-quality data storage. It highlights the importance of harmonization techniques to ensure that variables such as scanner models and acquisition protocols do not affect AI predictions. The review concludes that high-quality images and annotations are essential for developing accurate and robust AI algorithms in radiology, and that various software tools are available to support this process.This narrative review discusses the importance of high-quality data in machine learning and artificial intelligence (AI) for radiology. It outlines techniques and tools for ensuring data consistency, standardization, traceability, correct annotation, and de-identification, with a focus on radiological imaging and freely available software. Key topics include image resolution, pixel depth, file formats for medical images, anonymization and pseudonymization to protect patient privacy, image annotation tools, and data harmonization and normalization. The review emphasizes the need for standardized image curation and annotation to enable advanced techniques like federated learning, which addresses data governance and privacy without sharing data. It highlights the role of software tools such as ImageJ and 3D Slicer in processing medical images for AI research. Anonymization techniques, including facial structure removal, are discussed, along with free and commercial tools for image annotation. The review also covers medical image formats, including DICOM, NIfTI, and Analyze, and their use in radiology. It discusses key imaging concepts such as pixel depth, photometric interpretation, and metadata. Free software for medical imaging is highlighted, including ImageJ, 3D Slicer, and ITK-Snap, which are used for image processing, segmentation, and anonymization. The review addresses data privacy regulations, such as GDPR and HIPAA, and the importance of obtaining patient consent for data usage. It discusses anonymization and pseudonymization methods, data encryption, access control, and data retention periods. The review also covers the use of AI in image annotation, where pre-trained models can assist in identifying regions of interest, improving efficiency and accuracy. Finally, the review discusses data curation and storage, emphasizing the need for secure, consistent, and high-quality data storage. It highlights the importance of harmonization techniques to ensure that variables such as scanner models and acquisition protocols do not affect AI predictions. The review concludes that high-quality images and annotations are essential for developing accurate and robust AI algorithms in radiology, and that various software tools are available to support this process.