Evidence map›Paper›PMID 39907950›Full record

ArticleInternational journal of computer assisted radiology and surgery2025

German surgeons' perspective on the application of artificial intelligence in clinical decision-making.

Jonas Henn, Tijs Vandemeulebroucke, Simon Hatterscheidt, Jonas Dohmen, Jörg C Kalff, Aimee van Wynsberghe, Hanno Matthaei

Abstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. 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

7 authors.

Jonas HennDepartment of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany. jonas.henn@ukbonn.de.ORCID http://orcid.org/0000-0001-6608-8381
Tijs VandemeulebrouckeBonn Sustainable AI Lab, Institute of Science and Ethics, University of Bonn, Bonn, Germany.
Simon HatterscheidtDepartment of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Jonas DohmenDepartment of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.ORCID http://orcid.org/0000-0002-6907-3844
Jörg C KalffDepartment of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.
Aimee van WynsbergheBonn Sustainable AI Lab, Institute of Science and Ethics, University of Bonn, Bonn, Germany.
Hanno MatthaeiDepartment of General, Visceral, Thoracic and Vascular Surgery, University Hospital Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.

Funding

Ministerium für Wirtschaft, Innovation, Digitalisierung und Energie des Landes Nordrhein-Westfalen Project: Innovative Secure Medical Campus
6 · The paper itself

Abstract

purposeArtificial intelligence (AI) is transforming clinical decision-making (CDM). This application of AI should be a conscious choice to avoid technological determinism. The surgeons' perspective is needed to guide further implementation.

methodsWe conducted an online survey among German surgeons, focusing on digitalization and AI in CDM, specifically for acute abdominal pain (AAP). The survey included Likert items and scales.

resultsWe analyzed 263 responses. Seventy-one percentage of participants were male, with a median age of 49 years (IQR 41-57). Seventy-three percentage of participants carried out a senior role, with a median of 22 years of work experience (IQR 13-28). AI in CDM was seen as helpful for workload management (48%) but not for preventing unnecessary treatments (32%). Safety (95%), evidence (94%), and usability (96%) were prioritized over costs (43%) for the implementation. Concerns included the loss of practical CDM skills (81%) and ethical issues like transparency (52%), patient trust (45%), and physician integrity (44%). Traditional CDM for AAP was seen as experience-based (93%) and not standardized (31%), whereas AI was perceived to assist with urgency triage (60%) and resource management (59%). On median, generation Y showed more confidence in AI for CDM (P = 0.001), while participants working in primary care hospitals were less confident (P = 0.021).

conclusionParticipants saw the potential of AI for organizational tasks but are hesitant about its use in CDM. Concerns about trust and performance need to be addressed through education and critical evaluation. In the future, AI might provide sufficient decision support but will not replace the human component.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelClinical Decision-MakingSurgeonsAdultFemaleGermanyHumansMaleMiddle AgedSurveys and QuestionnairesAcute abdominal painArtificial intelligenceClinical decision-makingMachine learningSurgery

Identifiers

PMID39907950
PMCPMC12055908

What Socratic holds

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LicenceCC BY
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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 Socratic graph.