AI and GenAI is revolutionising scientific research by enhancing efficiency, accuracy, and creativity. The benefits of using AI and GenAI was highlighted in a landmark study by Nature, who surveyed more than 1,600 researchers around the world.

Applications of GenAI in Research

1. Deep learning across fields is transforming data analysis and knowledge generation

• Applications: Healthcare, aiding in disease detection and drug recovery, or climate science.
• Examples: Google DeepMind to develop AlphaFold, a protein folding prediction system that solved a 50-year-old challenge in biology decades earlier than anticipated.

2. Obtaining insights from unstructured data

• Example: Healthcare - data can be multi-modal and fragmented - GenAI can pull this data together, enabling researchers make predictions and model health interventions.

3. Learn from existing content and generate predictions of new content

• Example: Molecular research - using deep neural networks that use data about how molecules interact to accurately simulate the behaviour at the atomic level.

4. Expediting information synthesis

• Example: LLMs - increasingly being used to accelerate text-based tasks such as academic writing, conducting literature reviews, or producing summaries.

5. Addressing complex coding challenges

• Example: Opportunity for scientific researchers to convert code from one computer language to another, or between applications.

6. Task automation capability

• Example: Automate time and labour intensive tasks within the research workflow - productivity gains for scientists and increase potential to test diverse hypotheses beyond human capacity.

See Appendix C for more data and charts from the global researcher survey.