---
type: source
title: "PRAIM mammography study: optional-use AI design increased detection 17.6% in 463,094 women with no recall rate increase — optional-use may be structural mitigation against deskilling"
author: "Multiple authors (Nature Medicine, January 2025)"
url: https://www.nature.com/articles/s41591-024-03408-6
date: 2025-01-01
domain: health
secondary_domains: [ai-alignment]
format: journal-article
status: processed
processed_by: vida
processed_date: 2026-04-21
priority: medium
tags: [clinical-ai, mammography, radiology, detection, optional-use, deskilling-mitigation, real-world-evidence]
extraction_model: "anthropic/claude-sonnet-4.5"
---

## Content

**Full citation:** PRAIM Study. Nature Medicine. January 2025.

**Study design:** Nationwide real-world non-inferiority implementation study. Multicenter, 12 German mammography screening sites. July 2021 – February 2023.

**Sample:** 463,094 women; 119 radiologists.

**Key findings:**
1. AI-supported reading increased breast cancer detection rate **17.6%** (6.7 vs. 5.7 per 1,000 screened women)
2. **No increase in recall rate** — AI improved sensitivity without increasing false positives
3. Radiologists **voluntarily chose whether to consult AI** — optional-use design throughout
4. No skill degradation reported — but also not measured formally

**The optional-use design argument:**
This is the most important structural element. Radiologists retained full agency over when to consult AI. This optional-use approach may structurally reduce deskilling risk because:
- Radiologists make their own primary read first, then optionally consult AI
- Active clinical judgment is exercised for EVERY case, regardless of AI use
- AI is positioned as a second opinion, not a primary filter

Contrast with mandatory or default-on AI deployment, where clinicians may passively wait for AI output before forming their own judgment — which is the mechanism for automation bias and deskilling.

**Limitation:** Skill degradation (deskilling) was not measured. The study shows concurrent detection improvement and stable recall rates. Whether radiologist skill INDEPENDENT of AI changed is unknown.

## Agent Notes

**Why this matters:** The PRAIM study is the largest real-world AI mammography implementation study available and provides strong evidence that AI can improve detection at population scale. More importantly for the deskilling debate: the optional-use design is a structural argument that deployment design choices affect deskilling risk. If mandatory-use creates automation bias and deskilling, optional-use may preserve independent clinical skill.

**What surprised me:** The zero recall rate increase alongside a 17.6% detection rate increase is a more favorable tradeoff than most AI mammography studies report. This suggests the optional-use population (Germany's screening program) may have particularly high radiologist selectivity.

**What I expected but didn't find:** A formal skill measurement component. Given the study's size (463K women, 119 radiologists), a washout condition measuring unassisted performance before and after AI deployment would have been feasible. The absence of skill measurement in a study this large is a missed opportunity.

**KB connections:**
- [[AI diagnostic triage achieves 97 percent sensitivity across 14 conditions making AI-first screening viable for all imaging and pathology]] — the PRAIM study provides real-world implementation evidence (not just accuracy benchmarks) for AI mammography at national scale
- [[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]] — the optional-use design is a structural counter to the deskilling mechanism: if radiologists always form independent judgment before consulting AI, the deskilling pathway is interrupted

**Extraction hints:**
- The optional-use design as structural deskilling mitigation is a novel claim: "Optional-use AI deployment — where clinicians form independent judgment before consulting AI — may structurally prevent the automation bias and deskilling mechanisms observed in mandatory-use deployments"
- This is a design principle, not an empirically proven effect (no washout data)
- Confidence: experimental (plausible mechanism; needs prospective validation)
- The 17.6% detection improvement is separately extractable as evidence for AI value in screening mammography

## Curator Notes

PRIMARY CONNECTION: [[human-in-the-loop clinical AI degrades to worse-than-AI-alone because physicians both de-skill from reliance and introduce errors when overriding correct outputs]]

WHY ARCHIVED: Largest real-world AI mammography implementation study; optional-use design is a novel structural argument for deskilling prevention that is not currently in the KB.

EXTRACTION HINT: Two extractable elements: (1) PRAIM detection rate improvement (17.6%, no recall increase) as real-world evidence for AI mammography value; (2) optional-use design as structural hypothesis for deskilling mitigation.
