---
title: Cleared at 93%. Deployed at 64%. Clinical AI drifts
description: Across real-world scans, the pulmonary embolism triage tool caught 64.1% of cases against a 93% cleared figure, with sensitivity falling 15% a year at one site.
canonical: https://hangingcontext.com/stories/2026-11-13/clinical-ai-drifts-after-deployment/
last-updated: 2026-11-13
url: https://hangingcontext.com/stories/2026-11-13/clinical-ai-drifts-after-deployment/
published: 2026-11-13T00:51:58Z
edition: 2026-11-13T00
newsroom: health-biotech
beat: None
trust: reported
source_count: 3
cited_manifest_ids: ['1794499999680772203', '1794506595633807967', '1794502747804783779']
---

# Cleared at 93%. Deployed at 64%. Clinical AI drifts

*Across real-world scans, the pulmonary embolism triage tool caught 64.1% of cases against a 93% cleared figure, with sensitivity falling 15% a year at one site.*

Reported · 3 Manifests · Health & Biotech · Published 2026-11-13 00:51 UTC

## Key facts

- Deployed sensitivity of the Aidoc BriefCase PE triage tool was 64.1%, against 93% sensitivity reported in the FDA 510(k) summary. (self-reported) [cites: 1794499999680772203]
- Sensitivity was 88.6% for central PE, 63.1% for lobar/segmental PE, and 33.3% for subsegmental PE. (self-reported) [cites: 1794499999680772203]
- Sensitivity differed by 13 percentage points between the two highest-volume sites (68.7% vs 55.7%), and at one site sensitivity fell 15% per year. (self-reported) [cites: 1794499999680772203]
- AToP's Transformer model achieved an AUROC of 0.77 and PR-AUC of 0.34 for 30-day unplanned readmission prediction. (self-reported) [cites: 1794506595633807967]

## The gap

An FDA-cleared AI triage tool for pulmonary embolism looked strong in its clearance paperwork and much weaker in the ward. In a preprint posted on medRxiv, researchers report that the deployed sensitivity of the Aidoc BriefCase PE triage tool was 64.1%, against 93% sensitivity reported in the FDA 510(k) summary. The deployed figure comes with a 95% confidence interval of 61.5-66.6% — well below the cleared benchmark, not a borderline miss. [cites: 1794499999680772203]

The gap is the story. Clearance measures a model under controlled conditions; deployment measures it against real scans, real sites and real drift. The study is a post-market check, and it finds the tool's real-world performance significantly lower than the number on its clearance. [cites: 1794499999680772203]

## Where it misses

Sensitivity varies by where the clot is. The tool caught 88.6% of central pulmonary embolisms, 63.1% of lobar or segmental cases, and 33.3% of subsegmental cases. The tool's sensitivity falls as the embolus gets more peripheral — and subsegmental cases are where it is weakest. [cites: 1794499999680772203]

> **From Synorb** — Claims with a source link. A Manifest holds a Signal of structured claims and a Brief, plus the source it came from. Your agent can check a sentence against it. [Read the guide](https://synorb.com/connect/start)


Performance also varies by site. Sensitivity differed by 13 percentage points between the two highest-volume sites — 68.7% against 55.7%. The same tool, the same task, and a 13-point gap depending on where it runs. A gap that size between sites running the same tool suggests the difference is not the model alone but the scans, the workflow, or the patients each site sees. [cites: 1794499999680772203]

## The drift

Performance also moves over time. At one site, the false-positive rate fell 40% per year while sensitivity fell 15% per year. The tool was getting quieter and less sensitive at the same time — a pattern the study describes as temporal drift, and one that a one-off clearance check cannot catch. [cites: 1794499999680772203]

That is the core problem with cleared-but-unmonitored AI: nobody re-runs the clearance test every month, so a slow decline can run for a year before anyone notices. The pattern is only visible because someone kept measuring after deployment. A one-off clearance check cannot catch a decline that unfolds over a year. A tool that is cleared once and never re-checked can drift while everyone assumes the clearance number still holds. [cites: 1794499999680772203]

## The watchers

Other preprints this week build the kind of monitoring the triage study implies is missing. AToP, an explainability framework for Transformer-based predictions from electronic health records, converts per-token Integrated Gradients into structured temporal pattern narratives — visit-anchored stories about why a model made a call, rather than a ranked list of tokens. In its test, the underlying Transformer model trained on MIMIC-IV v3.1 achieved an AUROC of 0.77 and PR-AUC of 0.34 for 30-day unplanned readmission prediction. [cites: 1794506595633807967]

AToP also surfaces rare, high-attribution markers that standard rankings miss — including psychosis, self-harm overdose, suicidal ideation, and severe MDD — and identifies risk-increasing patterns such as psychiatric sequences and oncologic progressions. MedCheckpoint takes a different angle: it automates postmarketing pharmacovigilance with LLM agents that enforce study-design locks and source-traceable grounding, outperforming baseline coding agents in report quality across five dimensions, with higher run-to-run consistency — and it prevents misleading failures on infeasible comparisons or unmeasurable outcomes. [cites: 1794506595633807967, 1794502747804783779]

Neither system fixes a drifting triage tool on its own. But together they point at the missing layer in clinical AI: continuous, explainable monitoring after deployment, so that a 64.1% does not pass for a 93% unnoticed. [cites: 1794499999680772203]

## What is still unresolved

- What drives the site-level gap between the two highest-volume sites.
- Whether the drift continues or stabilises over longer deployment.
- Whether explainability frameworks like AToP catch drift in triage tools specifically.

## Decision card

- **Useful for:** Clinical AI developers and hospital teams deploying triage tools; Regulators designing post-market monitoring for clinical AI
- **Not for:** Judging an individual patient's scan — this is about population-level tool performance
- **Compare against:** FDA 510(k) clearance benchmarks; Continuous post-market monitoring data

## Provenance ledger

| Manifest id | Title | Source | Claims used | Published |
|---|---|---|---|---|
| `1794499999680772203` | Silent Drift and Site-level Variance in Clinical AI: Real-world performance and subgroup analysis for post-market monitoring of an FDA-cleared artificial intelligence (AI) triage tool for high-risk use case of pulmonary embolism | [medrxiv-diagnostics-imaging-pathology](https://medrxiv.org/content/10.64898/2026.11.10.26367739v1.full) | 1794500128439772548, 1794500128442772549, 1794500128443772550, 1794500128444772551 | 11-12 00:00 |
| `1794506595633807967` | AToP: An Explainability Framework for Transformer-Based EHR Predictions Using Attribution-Guided Temporal Pattern Mining | [medrxiv-health-ai-informatics](https://medrxiv.org/content/10.64898/2026.11.10.26367753v1.full) | 1794506818127808140, 1794506818123808139, 1794506818130808141, 1794506818132808142 | 11-12 00:00 |
| `1794502747804783779` | Automating postmarketing pharmacovigilance with large language model agents | [medrxiv-health-ai-informatics](https://medrxiv.org/content/10.64898/2026.11.10.26367711v1.full) | 1794503069349785571, 1794503069351785572, 1794503069352785573 | 11-12 00:00 |

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Cited Manifest ids:

- `1794499999680772203`
- `1794506595633807967`
- `1794502747804783779`

```sh
for id in 1794499999680772203 1794506595633807967 1794502747804783779; do
  curl -s -H "Authorization: Bearer $SYNORB_KEY" "https://api.synorb.com/manifests/by-id/$id"
done
```

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