Evidence map›Paper›PMID 41882171›Full record

ArticleScientific reports2026

Investigating expectations and needs regarding the use of large language models at Bavarian university clinics.

Juraj Vladika, Alexander Fichtl, Florian Matthes

Abstract read
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Juraj VladikaDepartment of Computer Science, TUM School of Computation, Information, and Technology, Technical University of Munich, Garching, Germany. juraj.vladika@tum.de.
Alexander FichtlDepartment of Computer Science, TUM School of Computation, Information, and Technology, Technical University of Munich, Garching, Germany.
Florian MatthesDepartment of Computer Science, TUM School of Computation, Information, and Technology, Technical University of Munich, Garching, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent advancements in Artificial Intelligence (AI) have been driven by Large Language Models (LLMs), powerful tools capable of generating coherent text and solving diverse analytical tasks. While LLMs hold great potential to enhance healthcare by assisting physicians and improving patient treatment, their clinical adoption is limited, and there is a lack of statistically grounded information on the opinions of medical professionals, personnel, and students regarding LLM usage. To address this gap, we conducted an online survey from April to October 2024, gathering insights from 120 participants across five Bavarian university clinics (in Germany), including physicians, medical students, and administrative staff. Findings show that many participants already use LLMs for research support, summarization, translation, and report drafting. Most believe LLMs will positively influence their field, acknowledge their potential to automate mundane tasks, and believe they will help to achieve a more personalized, evidence-based, and cost-effective patient treatment. However, concerns were shown regarding their opaque nature, data privacy, and the potential loss of patient trust. Participants overwhelmingly feel their institutions are not well-prepared for LLM adoption, with suggestions for improvement including increased education and specialized training, investments in digitalization and infrastructure, ensuring legal compliance, and encouraging technological openness. We hope these insights will inform the design of future medical AI solutions.

Indexed as

Artificial IntelligenceLarge Language ModelsAdultFemaleGermanyHumansMaleStudents, MedicalSurveys and QuestionnairesUniversitiesArtificial intelligenceDigital transformationLarge language modelsMedical AINatural language processing

Identifiers

PMID41882171
PMCPMC13031305

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.