Evidence map›Paper›PMID 36697450›Full record

ArticleScientific reports2023

Non-task expert physicians benefit from correct explainable AI advice when reviewing X-rays.

Susanne Gaube, Harini Suresh, Martina Raue, Eva Lermer, Timo K Koch, Matthias F C Hudecek, Alun D Ackery, Samir C Grover, Joseph F Coughlin, Dieter Frey and 3 more

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed
3.2field-weighted citation impact, top 6% of its field
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

38 citing papers in PubMed, 84 citations in OpenAlex.

  1. Trial
  2. Trial
  3. Consistent explainable image quality assessment for medical imaging.Health information science and systems · 2026
    Article
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Review
  12. Article
  13. Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Article
  19. Human-centered explainability evaluation in clinical decision-making: a critical review of the literature.Journal of the American Medical Informatics Association : JAMIA · 2025
    Review
  20. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

13 authors at 9 institutions in 4 countries.

Susanne GaubeLMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany. susanne.gaube@psy.lmu.de.
Harini SureshMIT Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Martina RaueMIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Eva LermerLMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
Timo K KochLMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
Matthias F C HudecekDepartment of Experimental Psychology, University of Regensburg, Regensburg, Germany.
Alun D AckeryDepartment of Emergency Medicine, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Samir C GroverLi Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Joseph F CoughlinMIT AgeLab, Massachusetts Institute of Technology, Cambridge, MA, USA.
Dieter FreyLMU Center for Leadership and People Management, Department of Psychology, LMU Munich, Munich, Germany.
Felipe C KitamuraDepartamento de Diagnóstico por Imagem, Universidade Federal de São Paulo, São Paulo, Brazil.
Marzyeh GhassemiElectrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA.
Errol ColakLi Ka Shing Knowledge Institute, St. Michael's Hospital, Unity Health Toronto, Toronto, Canada.
Ludwig-Maximilians-Universität München · DEMassachusetts Institute of Technology · USLMU Klinikum · DESt. Michael's Hospital · CAUnity Health TorontoUniversidade Federal de São Paulo · BRUniversity of Regensburg · DEUniversity of Toronto · CAVector Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI)-generated clinical advice is becoming more prevalent in healthcare. However, the impact of AI-generated advice on physicians' decision-making is underexplored. In this study, physicians received X-rays with correct diagnostic advice and were asked to make a diagnosis, rate the advice's quality, and judge their own confidence. We manipulated whether the advice came with or without a visual annotation on the X-rays, and whether it was labeled as coming from an AI or a human radiologist. Overall, receiving annotated advice from an AI resulted in the highest diagnostic accuracy. Physicians rated the quality of AI advice higher than human advice. We did not find a strong effect of either manipulation on participants' confidence. The magnitude of the effects varied between task experts and non-task experts, with the latter benefiting considerably from correct explainable AI advice. These findings raise important considerations for the deployment of diagnostic advice in healthcare.

Indexed as

Artificial IntelligencePhysiciansHumansRadiographyRadiologistsX-Rays

Identifiers

PMID36697450
PMCPMC9876883
OpenAlexW4317935575

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.