Industry

Regulated, scaling fast, and failing in safety-critical ways.

The volume of clinical AI shipping into hospitals has multiplied 240× in a decade. The evaluation infrastructure has not kept up.

Better Data. Better AI. Better Patient Outcomes.

1,450+

FDA-authorized AI/ML medical devices as of Dec 2025 — up from 6 in 2015.

295

New FDA authorizations in 2025 alone — a record year, almost all via 510(k).

PCCP

FDA's Predetermined Change Control Plan formalises lifecycle oversight of learning AI/ML devices.

EU AI Act

High-risk medical AI is regulated alongside MDR/IVDR — post-market clinical monitoring is now an obligation.

Where models fail

The literature is converging: generalist evaluation misses clinical harm.

  1. 01

    Hallucination at clinical scale

    Frontier LLMs still fabricate diagnoses, medications and contraindications in safety-critical scenarios, even when summary quality looks high to a generalist reviewer.

  2. 02

    Safety ≠ accuracy

    Safety and accuracy follow different scaling laws in clinical LLMs: a bigger model can be more accurate on average yet more dangerous on the long tail. Generalist labelers cannot tell the difference.

  3. 03

    Real-world complexity

    Large-scale simulations of common presentations expose systematic reasoning failures across age, sex and comorbidity — failures invisible in standard multiple-choice evals.

  4. 04

    Documentation drift

    Hallucination rates in medical text summarisation remain material — the same task AI scribes ship every day in production EHRs.

Regulators expect human oversight

FDA's AI/ML SaMD action plan and the EU AI Act both require demonstrable human-in-the-loop validation for high-risk clinical AI. Crowd labelers do not meet the bar.

Buyers expect clinical evidence

Health systems, payers and pharma procurement teams increasingly demand clinician-validated evaluation datasets before contracting AI vendors.

RLHF is moving specialist

Domain-expert feedback — not crowd ratings — is the differentiator for safety-critical models.

Where SORAMEDAI fits

Positioned exactly where regulators, the literature and the buyers are converging.

  • Triple-blind, three-doctor consensus on every task — auditable for regulators.
  • Domain-specific reviewer matching across various fields.
  • Clinical failure-mode reports your safety team can submit as evidence.
  • Rapid turnaround with clinical-grade quality.

Validate your model against the same standard regulators will.

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