Automated analysis typically involves several steps: 1. Data Collection: Gathering data from various sources such as electronic health records, genomic databases, and clinical trials. 2. Data Preprocessing: Cleaning and organizing the data to ensure it is in a suitable format for analysis. 3. Algorithm Development: Creating and training machine learning models to recognize patterns and make predictions based on the data. 4. Validation and Testing: Ensuring the models are accurate and reliable by testing them on independent datasets. 5. Deployment: Integrating the models into clinical workflows to assist in diagnosis and treatment planning.
