Evidence map›Paper›PMID 36855245›Full record

ArticleMedical education online2023

Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots.

Julia-Astrid Moldt, Teresa Festl-Wietek, Amir Madany Mamlouk, Kay Nieselt, Wolfgang Fuhl, Anne Herrmann-Werner

Abstract read
In one paragraph

Article in Medical education online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
63citing papers in PubMed, 4 pooled it
–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

63 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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  16. Applications of Chatbots in Improving Patient Care Outcomes:Sultan Qaboos University medical journal · 2026
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3 more citing papers are in PubMed but not listed here.

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

6 authors.

Julia-Astrid MoldtUniversity of Tuebingen, Tuebingen, Germany.ORCID 0000-0002-2418-150X
Teresa Festl-WietekUniversity of Tuebingen, Tuebingen, Germany.ORCID 0000-0003-1450-1757
Amir Madany MamloukInstitute for Neuro- and Bioinformatics, University of Luebeck, Luebeck, Germany.ORCID 0000-0001-9709-1620
Kay NieseltInstitute for Bioinformatics and Medical Informatics, University of Tuebingen, Germany.ORCID 0000-0002-1283-7065
Wolfgang FuhlInstitute for Bioinformatics and Medical Informatics, University of Tuebingen, Germany.ORCID 0000-0001-7128-298X
Anne Herrmann-WernerUniversity of Tuebingen, Tuebingen, Germany.ORCID 0000-0003-2413-7047

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) in medicine and digital assistance systems such as chatbots will play an increasingly important role in future doctor - patient communication. To benefit from the potential of this technical innovation and ensure optimal patient care, future physicians should be equipped with the appropriate skills. Accordingly, a suitable place for the management and adaptation of digital assistance systems must be found in the medical education curriculum. To determine the existing levels of knowledge of medical students about AI chatbots in particular in the healthcare setting, this study surveyed medical students of the University of Luebeck and the University Hospital of Tuebingen. Using standardized quantitative questionnaires and qualitative analysis of group discussions, the attitudes of medical students toward AI and chatbots in medicine were investigated. From this, relevant requirements for the future integration of AI into the medical curriculum could be identified. The aim was to establish a basic understanding of the opportunities, limitations, and risks, as well as potential areas of application of the technology. The participants (

Indexed as

Students, MedicalArtificial IntelligenceCommunicationCurriculumHumansKnowledgeapplications in education;artificial intelligencechatbothuman-computer interfaceMedical studentsteaching/learning strategies

Identifiers

PMID36855245
PMCPMC9979998

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.