--- title: "Revisiting the ABCs of Working with AI: A Replication with Radiologists · AI Fault Line" url: https://aifaultline.com/research/p/title-ad1fbf8b98671bd7/ description: "AI Fault Line is a neutral, evidence-cited field guide to why experts looking at the same developments disagree about AI's effect on productivity, jobs, inequality, regulation and the pace of change." --- [Research](https://aifaultline.com/research/) # Revisiting the ABCs of Working with AI: A Replication with Radiologists [Daniel Martin](https://aifaultline.com/authors/network/#map=daniel-martin) · arXiv · 10 Jun 2026 [Read original source](https://arxiv.org/abs/2606.12585v1) Open access Where this sits in the debate - [Will AI actually boost productivity and growth?](https://aifaultline.com/debate/productivity/) · its finding: it depends ([why studies differ](https://aifaultline.com/debate/productivity/#why)) Direction of the finding as read from the abstract by an AI model ([AI-written](https://aifaultline.com/about/)). ## Abstract Artificial intelligence (AI) systems increasingly assist human experts, but the consequences of AI assistance on productivity can be heterogeneous. Caplin, Deming, S. Li, Martin, Marx, Weidmann, and Ye (2025b) provide evidence that two characteristics, ability and belief calibration, help to determine the returns to AI assistance. This note shows that their results replicate to a setting where professional radiologists analyze chest X-rays with access to state-of-the-art machine learning predictions. I leverage the public Collab-CXR data repository described by Moehring, Kutwal, Huang, Banerjee, Jacobi, Eber, Mendoza, Chung, Dayan, Gupta, Bui, Truong, Pareek, Langlotz, Lungren, Agarwal, Rajpurkar, and Salz (2025) and first analyzed for human-AI collaboration by Agarwal, Moehring, Rajpurkar, and Salz (2023). To faithfully reproduce the analysis in Caplin, Deming, S. Li, Martin, Marx, Weidmann, and Ye (2025b), I use the radiologist assessments from the repeated-case designs, which include 68 radiologists and 11,420 paired radiologist-patient-pathology observations. The results of this replication support the external validity of their core findings: lower baseline ability and higher calibration predict larger incremental value from AI. ## About this publication **Preprint.** A preprint — posted publicly before or without journal peer review; findings may still be revised. Published 2026 (4 months ago) Citations Not recorded for this source Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations. [Chart: published 2026] ## More on this topic Related by theme, not necessarily by method or conclusion. - [Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality](https://aifaultline.com/research/p/doi-10-2139-ssrn-4573321/) SSRN · 2023 - [Generative AI at Work](https://aifaultline.com/research/p/doi-10-1093-qje-qjae044/) The Quarterly Journal of Economics · 2025 - [The Impact of AI on Developer Productivity: Evidence from GitHub Copilot](https://aifaultline.com/research/p/doi-10-48550-arxiv-2302-06590/) arXiv · 2023 - [The Productivity J-Curve: How Intangibles Complement General Purpose Technologies](https://aifaultline.com/research/p/doi-10-1257-mac-20180386/) American Economic Journal Macroeconomics · 2020 Overview assembled automatically from collected metadata. [How this site is built](https://aifaultline.com/about/).