Three publications in one quarter.
One architecture, validated.

MIRA — Towards autonomous medical artificial intelligence agents.

Ferber et al. · Kather lab · doi.org/10.1038/s41586-026-10675-5

A GPT-4o agent given 11 clinical tools and over 85,000 possible action choices, operating inside a sandboxed FHIR-native digital health record on 574 real emergency department cases. It placed medication orders, ordered imaging and labs, recommended procedures, and triaged hospital admissions — autonomously, with no physician attestation gate between AI decision and system execution. Diagnostic accuracy 87.8% versus 78.1% for board-certified physicians; procedure ordering 53.5% versus 38.3%; guideline adherence 35 percentage points higher; zero critical medication safety errors across 468 orders.

The paper demonstrates that autonomous medical action is technically real today. The sandbox constraint is a research choice, not a technical barrier. The same agent pointed at a live EHR tomorrow is the regulatory and liability exposure the FDA January 2026 CDS guidance and EU MDR Article 14 assume must be governed — without specifying the architectural mechanism that does the governing. That mechanism is what the HarnessHealth provisional patent draft claims.

On the paper's publication day, its senior author described what his lab built as what the field now calls an “agent harness” — “what we build for clinical agents, long before it was a thing.”— the paper's corresponding author, on publication day, 2026

AMIE Longitudinal — Towards Conversational AI for Disease Management.

Liévin, Palepu, Weng, et al. · Google Research / DeepMind · doi.org/10.1038/s41586-026-10764-5

A two-agent Gemini system — a patient-facing dialogue agent and a long-context management reasoning agent that grounds every plan in 600+ tokenized clinical guidelines and dual-jurisdiction drug formularies. Across 100 multi-visit simulated cases and 21 board-certified primary care physicians, AMIE matched or exceeded physician performance on every management evaluation axis. Management plan appropriateness at Visit 3: 98% vs 81%. Treatment preciseness: 95% vs 67%. Explicit guideline references in 100% of recommendations vs 86% for physicians. Medication reasoning on harder questions: 57.9% vs 47.8%.

Google's stated deployment posture: research-only, not for clinical use. AMIE represents the safe end of the spectrum — physician-level reasoning that stays advisory and would pass through an attestation gate cleanly. The published 100% guideline citation rate is the quality bar the harness architecture lets physicians enforce on every AI output that reaches a patient.

Physician agreement with AI-suggested care actions in asynchronous primary care.

Stark, Mani, Dhaliwal et al. · Lotus Health AI · lotus.ai

Lotus Health AI is a 50-state-licensed medical practice in which every AI-generated clinical decision — diagnosis, prescription, lab order, referral — is reviewed and finalized by a board-certified physician before it reaches the patient. Its Series A ($35 million, closed February 2026) was led by top-tier venture firms alongside a former U.S. Chief Technology Officer and an OpenAI executive. The May 2026 research paper studies physician concordance with the AI's suggested actions — the validation track for the broader physician-in-the-loop architecture.

Lotus operates the architecture in the HarnessHealth provisional patent draft, in primary care. The harness extends the same primitive across surgical encounter coding, prior authorization, Letters of Medical Necessity, registry abstraction, and clinical documentation — anywhere AI output must be billable, signable, and defensible. Different clinical context. Same architectural mechanism. Same regulatory logic.