While AI and machine learning offer many advantages, they also come with challenges:
Data Quality: The accuracy of ML models depends on the quality of the data used for training. Inaccurate or incomplete data can lead to poor predictions. Interpretability: ML models, especially deep learning models, can be complex and difficult to interpret, making it hard to understand why a particular prediction was made. Integration: Integrating AI tools into existing research workflows can be challenging and may require significant changes to established processes.
