What Challenges Does AI Face in Bioanalytical Sciences?
Despite its potential, AI faces several challenges in the field: 1. Data Quality: The effectiveness of AI models depends heavily on the quality of the input data. Inconsistent or incomplete data can lead to inaccurate predictions. 2. Interpretability: Many AI models, especially deep learning networks, are often considered "black boxes." Understanding how these models make decisions can be difficult. 3. Regulatory Hurdles: The use of AI in clinical settings is subject to strict regulatory frameworks, which can slow down its adoption. 4. Ethical Concerns: The deployment of AI in healthcare raises ethical issues, including data privacy and the potential for biased algorithms.
