Mount Sinai trials conversational AI nurse for cardiac prep calls

This Mount Sinai trial is a concrete example of large language models quietly moving into safety‑critical workflows under careful supervision. Rather than replacing clinicians, the Sofiya agent automates repetitive but essential pre‑procedure tasks—calling patients, confirming instructions, gathering clinical data—while handing off edge cases to nurses. With completion rates near 88% and error rates falling over time, the system shows that conversational AI can be hardened through staged deployment, collaborative debugging with clinicians, and clear fallbacks.

That matters for the race to AGI because it demonstrates a viable pattern for “agentic” systems in high‑stakes environments: constrain scope, embed them in institutional processes, and instrument them ruthlessly. As more hospitals adopt similar agents, they will generate rich operational datasets that feed back into medical‑domain fine‑tuning and evaluation, making subsequent models more reliable in clinical contexts. It also chips away at the narrative that powerful AI must wait on perfect safety before touching real patients; instead, we’re seeing incremental encroachment, with each successful deployment raising expectations about what AI systems should be allowed to handle.