Evidence map›Paper›PMID 40119020›Full record

Trial reportJournal of imaging informatics in medicine2025

Evaluation of a Retrieval-Augmented Generation-Powered Chatbot for Pre-CT Informed Consent: a Prospective Comparative Study.

Felix Busch, Lukas Kaibel, Hai Nguyen, Tristan Lemke, Sebastian Ziegelmayer, Markus Graf, Alexander W Marka, Lukas Endrös, Philipp Prucker, Daniel Spitzl and 6 more

Abstract readComparative StudyRandomized Controlled Trial
In one paragraph

Trial report in Journal of imaging informatics in medicine, 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. Review
  3. Article
  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

16 authors.

Felix Busch *School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany. felix.busch@tum.de.ORCID http://orcid.org/0000-0001-9770-8555
Lukas Kaibel *Institute for Computer Science, Free University of Berlin, Berlin, Germany.
Hai NguyenInstitute for Computer Science, Free University of Berlin, Berlin, Germany.
Tristan LemkeSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Sebastian ZiegelmayerSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Markus GrafSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Alexander W MarkaSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Lukas EndrösSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Philipp PruckerSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Daniel SpitzlSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Markus MergenSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Marcus R MakowskiSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Keno K BressemSchool of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Sebastian PetzoldtClinic for General, Visceral and Minimally Invasive Surgery, DRK Kliniken Berlin Köpenick, Berlin, Germany.
Lisa C Adams *School of Medicine and Health, Department of Diagnostic and Interventional Radiology, Klinikum Rechts Der Isar, TUM University Hospital, Technical University of Munich, Munich, Germany.
Tim Landgraf *Institute for Computer Science, Free University of Berlin, Berlin, Germany. tim.landgraf@fu-berlin.de.ORCID http://orcid.org/0000-0003-4951-5235

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to investigate the feasibility, usability, and effectiveness of a Retrieval-Augmented Generation (RAG)-powered Patient Information Assistant (PIA) chatbot for pre-CT information counseling compared to the standard physician consultation and informed consent process. This prospective comparative study included 86 patients scheduled for CT imaging between November and December 2024. Patients were randomly assigned to either the PIA group (n = 43), who received pre-CT information via the PIA chat app, or the control group (n = 43), with standard doctor-led consultation. Patient satisfaction, information clarity and comprehension, and concerns were assessed using six ten-point Likert-scale questions after information counseling with PIA or the doctor's consultation. Additionally, consultation duration was measured, and PIA group patients were asked about their preference for pre-CT consultation, while two radiologists rated each PIA chat in five categories. Both groups reported similarly high ratings for information clarity (PIA: 8.64 ± 1.69; control: 8.86 ± 1.28; p = 0.82) and overall comprehension (PIA: 8.81 ± 1.40; control: 8.93 ± 1.61; p = 0.35). However, the doctor consultation group showed greater effectiveness in alleviating patient concerns (8.30 ± 2.63 versus 6.46 ± 3.29; p = 0.003). The PIA group demonstrated significantly shorter subsequent consultation times (median: 120 s [interquartile range (IQR): 100-140] versus 195 s [IQR: 170-220]; p = 0.04). Both radiologists rated overall quality, scientific and clinical evidence, clinical usefulness and relevance, consistency, and up-to-dateness of PIA high. The RAG-powered PIA effectively provided pre-CT information while significantly reducing physician consultation time. While both methods achieved comparable patient satisfaction and comprehension, physicians were more effective at addressing worries or concerns regarding the examination.

Indexed as

Generative Artificial IntelligenceInformed ConsentTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedPatient SatisfactionProspective StudiesArtificial intelligenceLarge language modelsMedical informaticsNatural language processingRadiology

Identifiers

PMID40119020
PMCPMC12701176

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.