-Data quality and integrity: Establish rigorous processes for data collection, review, and cleansing. High-quality data is essential for training reliable AI models.
-Data integration and management: Working with various data types, including chemical data, biological data, clinical outcomes, patient records, and realworld evidence. Integrate these data sources and establish effective data management systems and practices.
-Human-machine interaction: Preparing your organization for the integrated use of AI through data and AI competency programs, continuing education, and change management.
-Legal and ethical considerations: Understanding AI law. Ethical implications of AI, including bias in AI models and the impact of AI decisions in clinical settings.
Check out the incredible speaker line-up to see who will be joining Selena.
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