Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 20

The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.

“Initial responses to false positives in AI-Supported continuous interactions: a colonoscopy case study,”

van Berkel, Niels, Opie, Jeremy, Ahmad, Omer F., Lovat, Laurence, Stoyanov, Danail, Blandford, Ann·2022·ACM Transactions on Interactive Intelligent Systems
DOI 10.1145/3480247
Open this paper in CoChat — free account

Abstract

The use of artificial intelligence (AI) in clinical support systems is increasing. In this article, we focus on AI support for continuous interaction scenarios. A thorough understanding of end-user behaviour during these continuous human-AI interactions, in which user input is sustained over time and during which AI suggestions can appear at any time, is still missing. We present a controlled lab study involving 21 endoscopists and an AI colonoscopy support system. Using a custom-developed application and an off-the-shelf videogame controller, we record participants’ navigation behaviour and clinical assessment across 14 endoscopic videos. Each video is manually annotated to mimic an AI recommendation, being either true positive or false positive in nature. We find that time between AI recommendation and clinical assessment is significantly longer for incorrect assessments. Further, the type of medical content displayed significantly affects decision time. Finally, we discover that the participant’s clinical role plays a large part in the perception of clinical AI support systems. Our study presents a realistic assessment of the effects of imperfect and continuous AI support in a clinical scenario.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3480247

Cites

14 of 14 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 14 references

Research works printed but not linked: 38

  1. 10.1055/a-1306-7590 · DOI
  2. 10.5009/gnl.2012.6.1.64 · DOI
  3. 10.1145/3290605.3300233 · DOI · Paper page
  4. 10.18637/jss.v067.i01 · DOI · Paper page
  5. 10.1016/j.gie.2011.01.060 · DOI
  6. 10.1145/3025453.3025739 · DOI · Paper page
  7. 10.1145/3185517 · DOI · Paper page
  8. 10.1145/163298.163306 · DOI
  9. 10.1001/jama.282.19.1851 · DOI
  10. Joseph F. Hair, William C. Black, Barry J. Babin, Rolph E. Anderson, and R. L. Tatham. 2010. Multivariate Data Analysis. Pearson.
  11. 10.1001/jamainternmed.2013.12745 · DOI
  12. 10.1038/s41568-018-0016-5 · DOI · Paper page
  13. 10.1056/NEJMoa0907667 · DOI · Paper page
  14. Rafal Kocielnik Saleema Amershi and Paul N. Bennett. 2019. Will you accept an imperfect AI? Exploring designs for adjusting end-user expectations of AI systems(CHI’19). 1–14. DOI:10.1145/3290605.3300641 · DOI · Paper page
  15. 10.1158/1055-9965.2126.13.12 · doi:10.1158/1055-9965.2126.13.12
  16. 10.1016/j.gie.2011.06.033 · DOI
  17. 10.1016/j.chb.2019.06.009 · DOI
  18. 10.1007/978-3-319-93177-7_2 · DOI
  19. 10.1007/s10350-008-9315-3 · doi:10.1007/s10350-008-9315-3
  20. 10.1053/j.gastro.2018.04.003 · DOI · Paper page
  21. 10.1053/j.gastro.2018.04.003 · DOI · Paper page
  22. 10.1145/2971485.2971561 · DOI
  23. 10.1055/s-0043-109430 · DOI
  24. 10.3390/s18051654 · DOI · Paper page
  25. 10.1145/3311957.3359433 · DOI
  26. 10.2307/2346729 · DOI · Paper page
  27. L. Preisler, M. B. Søndergaard Svendsen, B. Søndergaard, L. Brink, T. Nordentoft, L. B. Svendsen, and L. Konge. 2016. Automatic and unbiased assessment of competence in colonoscopy: Exploring validity of the Colonoscopy Progression Score (CoPS). Endoscopy International Open 4, 12 (2016), E1238–E1243.
  28. 10.1145/3313831.3376506 · DOI
  29. 10.1016/j.gie.2009.05.012 · DOI
  30. 10.1145/3422156 · DOI
  31. 10.1016/j.jbi.2020.103553 · DOI
  32. 10.1145/3359130 · DOI · Paper page
  33. Jeroen C. van Rijn, Johannes B. Reitsma, Jaap Stoker, Patrick M. Bossuyt, Sander J. van Deventer, and Evelien Dekker. 2006. Polyp miss rate determined by tandem colonoscopy: A systematic review. American Journal of Gastroenterology 101, 2 (2006), 343–350.
  34. 10.1177/0163278707307899 · DOI · Paper page
  35. 10.1136/gutjnl-2018-317500 · DOI · Paper page
  36. 10.1038/s41551-018-0301-3 · DOI · Paper page
  37. 10.1145/1978942.1978963 · DOI · Paper page
  38. 10.1016/j.tgie.2019.150631 · DOI

Cited by

See all 20

Metadata

Authors
van Berkel, N., Opie, J., Ahmad, O. F., Lovat, L., Stoyanov, D., & Blandford, A.
Affiliations
  • Aalborg University, Denmark and University College London, London, United Kingdom
  • University College London, London, United Kingdom
  • University College London Hospitals, London, United Kingdom
ORCID
van Berkel, Niels
Venue
ACM Transactions on Interactive Intelligent Systems
Publisher
Association for Computing Machinery (ACM)
Published
2022-03-04
Volume
12
Issue
1
Pages
1-18
ISSN
  • 2160-6455
  • 2160-6463
DOI
10.1145/3480247
Cited by
20
References
14
Language
en
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
Wellcome/EPSRC Centre for Interventional and Surgical Sciences (NS/A000050/1)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: “Initial responses to false positives in AI-Supported continuous interactions: a colonoscopy case study,”. https://app.cochat.ai/papers/10.1145/3480247
The paper
van Berkel, N., Opie, J., Ahmad, O. F., Lovat, L., Stoyanov, D., & Blandford, A. (2022). “Initial responses to false positives in AI-Supported continuous interactions: a colonoscopy case study,”. ACM Transactions on Interactive Intelligent Systems. https://doi.org/10.1145/3480247

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.