The growth of scientific healthcare literature and publicly available biomedical databases has created many opportunities but also great challenges for researchers. While large amounts of biological data are now freely available, finding and connecting relevant information across disparate sources remains time-consuming and complex. LLM-powered tools offer promising solutions to this challenge, but implementing them in healthcare, where accuracy can impact patient outcomes, requires specialised approaches and careful design considerations.
This talk will share practical lessons and technical strategies to address hallucinations, complex domain-specific terminology, source citations.

The presentation will be structured into three main sections:

1. The challenge of scientific data retrieval (5 mins)

   1. Overview of the current landscape of biological databases and scientific literature
   2. Common challenges researchers face when searching for information across multiple sources
   3. Specificities of healthcare domain where accuracy is critical

2. Technical architecture for LLM-powered scientific search (15 mins)

   1. Reliable approaches to querying structured databases using natural language
   2. Vector database implementation for semantic search across scientific literature
   3. Strategies to ensure retrieved information is properly attributed to sources
   4. Real-world performance considerations: balancing accuracy, latency, and cost

3. Lessons learned and future directions (5 mins)

   1. Performance metrics and user feedback from academic researchers
   2. Challenges and limitations of current approaches
   3. Future directions for AI-assisted scientific discovery

Throughout the talk, I'll provide concrete examples of how these technologies can be applied to real research questions, in a production environment, demonstrating the practical value of AI agents in accelerating scientific discovery.

Intended audience: This talk is designed for data scientists, ML / Software engineers, bioinformaticians, and researchers interested in leveraging AI for scientific data retrieval and analysis.
While examples will focus on biological data, the principles and techniques discussed are applicable across scientific domains. Basic familiarity with Python and AI concepts will be helpful but is not required.