The question should be considered from a broader perspective, as general AI tools can help scientific research.

Researchers and professionals in artificial intelligence may use various tools depending on their specific goals. Some common AI tools and frameworks used in research and development include:

TensorFlow: An open-source machine learning library developed by Google, widely used for building and training deep learning models.

PyTorch: Another popular open-source machine learning library, developed by Facebook's AI Research lab. It is known for its dynamic computation graph.

Keras: A high-level neural networks API written in Python, often used in conjunction with TensorFlow.

Jupyter Notebooks: Interactive computing environments that allow researchers to create and share documents that contain live code, equations, visualizations, and narrative text.

Pandas: A data manipulation and analysis library for Python. It provides data structures for efficiently storing large datasets and tools for working with them.

NLTK (Natural Language Toolkit): A library for working with human language data, providing easy-to-use interfaces to work with lexical and grammatical analysis.

OpenCV: A library of programming functions mainly aimed at real-time computer vision.

Researchers use these tools and frameworks to experiment with algorithms, process and analyze data, build and train machine-learning models, and develop innovative applications. The choice of tools depends on the specific requirements of the research project.