Objective:

To understand how researchers are adapting to AI in their work and the factors influencing their trust in AI-generated insights.

Approach:

• Survey Insights: The report surveyed over 3,200 academic and corporate researchers globally regarding AI adoption and trust.
• AI Usage: 58% of researchers now use AI for tasks like literature reviews and data synthesis.
• Trust and Training: Only 22% find AI trustworthy; nearly half feel undertrained in its use.
• AI in Drug Development: AI is significantly impacting data synthesis, target identification, and lead optimization in drug discovery.

Key Findings:

• 58% of researchers use AI, up from 37% in early 2023.
• 61% believe AI will drive new knowledge in the next 2-3 years.
• AI helps in consolidating vast data for drug discovery, saving time.
• Researchers express concerns about the 'black box' problem in AI.

Interpretation:

To build confidence in AI, researchers desire tools that cite sources, are trained on current literature, and demonstrate factual accuracy.

Limitations:

• Trust in AI remains low, with only 22% considering it trustworthy.
• Many researchers feel they lack adequate training to use AI effectively.

Conclusion:

For AI to be effectively integrated into research, it must be transparent, evidence-based, and reliable.

Sources:

• Elsevier Researcher of the Future Report