Steven

Founder @ | Elevating people, not replacing them | Clinical Trials, Pharma, AI & Transformation | 🚴 Gravel + Global | Speaker

[Steven @AIwithSteven]

Decisions worth defending are decisions worth proving.
Full essay → https://t.co/g5E54bu7aV

@DrugInfoAssn 2026 → Poster Session II, Tuesday June 16, Abstract 116114, Pennsylvania Convention Center.

Come find me at the poster.

#DIA2026 #ClinicalTrials #RegulatoryAffairs #ArtificialIntelligence #FormalVerification

[Steven @AIwithSteven]

A signature on a clinical decision is not paperwork.
It is a human staking their license, their reputation, and their judgment on the answer being right.

Most AI in regulated industries asks that human to attest to a confidence score they cannot reconstruct.
🧵

[Steven @AIwithSteven]

AI can predict.

Only humans can judge.

The architecture exists so the judgment is informed by evidence the human can reconstruct.

The human is the defensibility.

[Steven @AIwithSteven]

I'm excited to attend #654: India Town Hall: India’s Evolving Regulatory Landscape - Policy Reforms and Digital Systems Accelerating Innovation at @DrugInfoAssn 2026 Global Annual Meeting #diaglobal2026 https://t.co/eXf2ECMakF @sched

[Steven @AIwithSteven]

AI can assemble the evidence. Only the human can judge it. The certificate is what makes the judgment defensible.

We'll be working through these questions at #DIA2026 in Philadelphia.

Provably right, not probably right.

ARCH (working paper): https://t.co/hj7OyD3QX1

[Steven @AIwithSteven]

When an AI system touches a clinical trial decision, someone has to answer one question: prove it was right.
A proof certificate is that proof. It doesn't replace the human. It backs them.
Here's what it actually is 🧵

[Steven @AIwithSteven]

A pathway to portability → bind the certificate to the CDISC USDM as a native extension, so it's expressed in a standard regulators already consume.

That binding pathway was contributed by Jessica Stuyvenberg, the ARCH Framework, a working paper now open for review.

[Steven @AIwithSteven]

I don't have all the answers. The framework ships with six open questions, on purpose.

It's an invitation to set a validation standard before regulators impose one retroactively.

RA, clinical ops, AI governance.

I'd rather you argue with it than agree.

👇
https://t.co/qNS6s6RV13

[Steven @AIwithSteven]

Every AI tool in clinical trials can tell you how confident it is.

None can prove what they actually verified.

A confidence interval is a guess with a decimal point. A proof certificate either checks out or it fails.

Published the framework for the second kind this morning. 🧵

[Steven @AIwithSteven]

Three gates. Every trial document and every trial decision passes through all three:

1️⃣ Regulatory compliance → checked against the jurisdiction that governs the site
2️⃣ Formal proof → structure verified in <100ms
3️⃣ Human decision → always

The output isn't a recommendation you trust. It's a receipt you can check.

[Steven @AIwithSteven]

Não é uma conclusão. Deixamos seis questões em aberto de propósito.

Se você é de assuntos regulatórios ou de IA, quero que discorde, não que concorde.

Link 👇
https://t.co/jJnp8YV1DJ

[Steven @AIwithSteven]

Toda ferramenta de IA em ensaios clínicos te diz o quão confiante ela está.

Nenhuma consegue provar que estava certa.
Um score de confiança é um chute com vírgula. Um certificado de prova verifica, ou falha. Não existe meio-termo.

A gente publicou o framework do seguo tipo. 🧵

[Steven @AIwithSteven]

E não foi traduzido de um documento gringo.

Foi escrito a partir do Brasil: CFM 2.454, Lei 14.874, LGPD, a estrutura CEP/CONEP. A taxonomia de risco mapeia direto sobre as quatro classes da RDC 751.

Aceito para apresentação no @DrugInfoAssn 2026, em Filadélfia. Base brasileira, padrão global.