---
id: "lib-2026-10-07-dual-vs-single-suggestion-ai-support-for"
type: "library"
title: "Dual- vs Single-Suggestion AI Support for Radiographic Interpretation in Residents: Randomized Multireader Study"
lang: "EN"
org: "arXiv (3 hospitals, China)"
source_type: "Paper"
date: "2026-10-07"
week: "2026-W41"
url: "https://nosov.ai/library/lib-2026-10-07-dual-vs-single-suggestion-ai-support-for/"
original_url: "https://arxiv.org/abs/2610.09589"
tier: "A"
tier_name: "Strong evidence"
tier_reason: "Registered, prospective, randomized three-arm multireader trial (ChiCTR2600129243) with 123 residents at three hospitals and pre-specified outcomes; strong method, moderate sample and a purposively selected case set."
summary_basis: "full-text"
original_minutes: "40"
tags: ["future of work", "enablement", "human ai teaming", "rct"]
---

# Dual- vs Single-Suggestion AI Support for Radiographic Interpretation in Residents: Randomized Multireader Study

Tier A (Strong evidence): Registered, prospective, randomized three-arm multireader trial (ChiCTR2600129243) with 123 residents at three hospitals and pre-specified outcomes; strong method, moderate sample and a purposively selected case set.

Summary based on the full text.

## In short

A randomized multireader study from Nanchang University hospitals and the Hong Kong University of Science and Technology (first authors Lin Wu and Zhe Xu), posted to arXiv on 7 October 2026. Junior doctors read radiographs first alone, then with either one AI suggestion (GPT-5.4) or two (GPT-5.4 plus Kimi-K2.6, or plus Gemini-3.6 Flash). A second opinion roughly doubled accuracy on cases where the shared GPT-5.4 suggestion was wrong. Overall accuracy gains were larger with two suggestions for radiology residents, but not for residents from other specialties.

## Key numbers

- 40.1% / 40.4% vs 20.0%: radiology residents' accuracy on cases where GPT-5.4 was wrong, with two AI suggestions vs one
- 31.3% / 31.0% vs 12.1%: same comparison for non-radiology residents
- 46.3% to 61.5%: mean diagnostic accuracy of all 123 residents before and after AI assistance
- 65.0%: accuracy of each of the three AI models on the 60-case set

## What it reports

- 154 residents assessed, 132 randomized 1:1:1, 123 analyzed (mean age 24.1, 65 women; 60 radiology, 63 non-radiology), all with under 3 years of experience.
- Each read the same 60 radiographs (30 chest, 30 abdominal). All three models were right on 39 of 60 cases (65.0%); a senior-resident baseline scored 54.3% unaided.
- Across all residents, accuracy rose from 46.3% to 61.5% with AI (15.2 percentage points; 95% CI 13.7-16.8). Gains: 13.9 pp single, 15.0 and 16.8 pp dual.
- Radiology residents gained 6.69 pp (B vs A) and 7.87 pp (C vs A) more with dual support (Holm-adjusted P = .030). No difference for non-radiology residents (P = .20).
- On the 21 cases where GPT-5.4 was wrong: radiology residents scored 40.1% and 40.4% with two suggestions vs 20.0% with one; non-radiology 31.3% and 31.0% vs 12.1% (all P < .001).
- The specialty interaction for the dual-suggestion effect was 10.44 pp (95% CI 4.36-16.52).
- Interpretation time and confidence change did not differ across support conditions.

## How they got it

Prospective three-arm trial at three hospitals in China, July to September 2026, registered as ChiCTR2600129243. Cases came from 218 examinations at one centre (2023-2024) with confirmed diagnoses; 60 were selected so each model had identical overall accuracy. Model outputs were generated once and shown with rationale; residents recorded an unaided and then an AI-assisted diagnosis on a smartphone or tablet.

## Limits

- The 60 cases were purposively selected to balance disease mix and AI accuracy, so they do not reflect routine case mix.
- The benefit depends on the second model disagreeing when the first is wrong; other model pairs may behave differently.
- Mechanisms (attention, how residents weighted each suggestion) were not measured.

## Why it matters for companies

Most enterprise AI tools show one answer from one model, and the trial shows how strongly a single wrong answer pulls junior staff along: with one suggestion, accuracy on those cases fell to 12-20%. Showing a second independent model's answer roughly doubled accuracy on those cases, with no added reading time. For review-heavy work such as claims, contracts or screening, a disagreement signal between two models is a cheap design choice. The effect was larger for people with domain training, so the second opinion helps experts more than novices.

Original: https://arxiv.org/abs/2610.09589