Evidence map›Paper›PMID 38504016›Full record

ArticleNature medicine2024

Heterogeneity and predictors of the effects of AI assistance on radiologists.

Feiyang Yu, Alex Moehring, Oishi Banerjee, Tobias Salz, Nikhil Agarwal, Pranav Rajpurkar

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Nature medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 92 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
92citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

92 citing papers in PubMed, 2 syntheses or guidelines pooled it.

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  14. Artificial intelligence-based anatomical recognition improves surgeon decision-making during robotic gastrectomy.Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association · 2026
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32 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Feiyang Yu *Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.ORCID 0000-0001-6748-1070
Alex Moehring *Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA, USA.ORCID 0000-0001-6822-6354
Oishi BanerjeeDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
Tobias SalzDepartment of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Nikhil AgarwalDepartment of Economics, Massachusetts Institute of Technology, Cambridge, MA, USA.
Pranav RajpurkarDepartment of Biomedical Informatics, Harvard Medical School, Boston, MA, USA. pranav_rajpurkar@hms.harvard.edu.ORCID 0000-0002-8030-3727

Funding

Alfred P. Sloan Foundation 2022-17182
6 · The paper itself

Abstract

The integration of artificial intelligence (AI) in medical image interpretation requires effective collaboration between clinicians and AI algorithms. Although previous studies demonstrated the potential of AI assistance in improving overall clinician performance, the individual impact on clinicians remains unclear. This large-scale study examined the heterogeneous effects of AI assistance on 140 radiologists across 15 chest X-ray diagnostic tasks and identified predictors of these effects. Surprisingly, conventional experience-based factors, such as years of experience, subspecialty and familiarity with AI tools, fail to reliably predict the impact of AI assistance. Additionally, lower-performing radiologists do not consistently benefit more from AI assistance, challenging prevailing assumptions. Instead, we found that the occurrence of AI errors strongly influences treatment outcomes, with inaccurate AI predictions adversely affecting radiologist performance on the aggregate of all pathologies and on half of the individual pathologies investigated. Our findings highlight the importance of personalized approaches to clinician-AI collaboration and the importance of accurate AI models. By understanding the factors that shape the effectiveness of AI assistance, this study provides valuable insights for targeted implementation of AI, enabling maximum benefits for individual clinicians in clinical practice.

Indexed as

AlgorithmsArtificial IntelligenceHumansRadiologists

Identifiers

PMID38504016
PMCPMC10957478

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.