It aids in diagnosing, treating, and monitoring various medical conditions, thus allowing healthcare professionals to obtain detailed and non-invasive images of organs, tissues, and physiological processes.

In recent years, the field has witnessed advancements in computer-aided diagnosis, integrating Artificial intelligence and Deep learning techniques to automatize medical image analysis and assist radiologists in detecting abnormalities and improving diagnostic accuracy.

[7] MONAI provides a robust suite of libraries, tools, and Software Development Kits (SDKs) that encompass the entire process of building medical imaging applications.

Through this collaboration, MONAI Label trains an AI model for a specific task and continually improves its performance as it receives additional annotated images.

[8] Within MONAI Core, researchers can find a collection of tools and functionalities for dataset processing, loading, Deep learning (DL) model implementation, and evaluation.

For instance, it has been utilized in academic research involving automatic cranio-facial implant design,[29] brain tumor analysis from Magnetic Resonance images,[30] identification of features in focal liver lesions from MRI scans,[31] radiotherapy planning for prostate cancer,[32] preparation of datasets for fluorescence microscopy imaging,[33] and classification of pulmonary nodules in lung cancer.

[34] In healthcare settings, hospitals have leveraged MONAI to enhance mammography reading by employing Deep learning models for breast density analysis.