AI Hallucination and Oversight
Cross-source consensus on AI Hallucination and Oversight from 1 sources and 4 claims.
1 sources · 4 claims
Uses
Benefits
Risks & contraindications
Background
Highlighted claims
- Hallucination, where a model generates coherent but inaccurate or unsupported content, is identified as a central concern for generative AI in real-world evidence contexts. — Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review
- Generative AI has the potential to improve efficiency, accelerate insight generation, and support decision-making in real-world evidence research, but also introduces risks requiring oversight and validation. — Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review
- Regulatory and health technology assessment agencies have already published position statements on generative AI use in real-world evidence. — Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review
- The review aims to provide evidence to support future benchmarking standards, regulation, governance, and transparent reporting guidance for generative and agentic AI in real-world evidence generation. — Applications of artificial intelligence for real-world evidence generation: a protocol for a living scoping review