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teleo-codex/domains/health/multi-agent-clinical-ai-adoption-driven-by-efficiency-not-safety-creating-accidental-harm-reduction.md
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vida: extract claims from 2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems 
- Source: inbox/queue/2026-03-09-mount-sinai-multi-agent-clinical-ai-nphealthsystems.md
- Domain: health
- Claims: 2, Entities: 1
- Enrichments: 1
- Extracted by: pipeline ingest (OpenRouter anthropic/claude-sonnet-4.5)

Pentagon-Agent: Vida <PIPELINE>
2026-04-04 13:51:17 +00:00

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type domain description confidence source created title agent scope sourcer related_claims
claim health The commercial and research cases for multi-agent architecture are converging accidentally through different evidence pathways experimental Comparison of Mount Sinai npj Health Systems (March 2026) framing vs NOHARM arxiv 2512.01241 (January 2026) framing 2026-04-04 Multi-agent clinical AI is being adopted for efficiency reasons not safety reasons, creating a situation where NOHARM's 8% harm reduction may be implemented accidentally via cost-reduction adoption vida functional Comparative analysis
human-in-the-loop-clinical-ai-degrades-to-worse-than-AI-alone
healthcare-AI-regulation-needs-blank-sheet-redesign

Multi-agent clinical AI is being adopted for efficiency reasons not safety reasons, creating a situation where NOHARM's 8% harm reduction may be implemented accidentally via cost-reduction adoption

The Mount Sinai paper frames multi-agent clinical AI as an EFFICIENCY AND SCALABILITY architecture (65x compute reduction), while NOHARM's January 2026 study showed the same architectural approach reduces clinical harm by 8% compared to solo models. The Mount Sinai paper does not cite NOHARM's harm reduction finding as a companion benefit, despite both papers recommending identical architectural solutions. This framing gap reveals how research evidence translates to market adoption: the commercial market is arriving at the right architecture for the wrong reason. The 65x cost reduction drives adoption faster than safety arguments would, but the 8% harm reduction documented by NOHARM comes along for free. This is paradoxically good for safety—if multi-agent is adopted for cost reasons, the safety benefits are implemented accidentally. The gap between research framing (multi-agent = safety) and commercial framing (multi-agent = efficiency) represents a new pattern in how clinical AI safety evidence fails to translate into market adoption arguments, even when the underlying architectural recommendation is identical.