Clinical AI is hitting a wall. A single model can be deprecated overnight, costs shift without warning, and even the best LLMs still get complex medical reasoning wrong — the kind that matters most at the bedside.

Inside this paper, you'll get the research every clinical AI team needs to understand what's possible today:

• Why Sully's Consensus Mechanism outperforms OpenAI's o3 and Google's Gemini 2.5 Pro across every major medical benchmark tested

• How a triage-inspired architecture routes queries to specialist AI experts — mirroring the way real clinical teams actually make decisions

• The real accuracy gap: 61.2% vs. 53.5% on MedXpertQA, the benchmark specifically designed for complex clinical reasoning (not just medical trivia)

• Why single models are systematically overconfident — and how ensemble consensus produces calibration you can actually trust in high-stakes settings

• How the modular design lets you swap in newer models without rebuilding — so your clinical AI doesn't go stale every six months

If you're building, deploying, or evaluating AI for clinical decision support, this is the architecture paper you need to read first.