Decision-only medical AI: early evidence and pairing with language models

Recently introduced decision-only AI returns probabilities over specified options rather than text. We tested the clinical consistency and potential bias susceptibility of the two available decision models, Jev and OpenAI Decisions, against a large language model (LLM), GPT-6 Luna, then paired each decision model with the LLM. Across 2,134 case presentations and 2,483,222 model calls, we measured decision changes after adding labels or pressure cues without changing clinical findings. In the original controlled challenges, these additions changed 13.4 more decisions per 100 than neutral wording for the decision models and 14.0 for the LLM. Pairing reduced this to 2.6, about 81% lower in development and 42% to 48% lower in held-out reports. Triage accuracy stayed near 81% to 82%. The paired Decisions workflow cost at most US$0.77 per 1,000 entries. We see this as a practical proof of concept for reducing framing sensitivity and flagging influential text for review.