Part 6/11:
Data Privacy & Security: Sensitive patient and corporate data must be protected, often by deploying retrival-augmented generation (RAG) architectures that keep data within organizational premises.
Model Hallucination & Bias: Ensuring factual correctness of AI-generated responses remains critical, necessitating careful model fine-tuning and validation.
Rapid Prototyping & ROI: Quick deployment of AI tools allows organizations to test their value and scale promising solutions, emphasizing the importance of iterative, small-scale applications.
Real-world use cases included rare disease analysis, where data scarcity makes AI-assisted hypothesis generation especially valuable, opening niche markets and improving patient care.