Evidence map›Paper›PMID 39017712›Full record

ArticleBundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz2024

[Supporting medical and nursing activities with AI: recommendations for responsible design and use].

Tanja Bratan, Diana Schneider, Florian Funer, Nils B Heyen, Andrea Klausen, Wenke Liedtke, Myriam Lipprandt, Sabine Salloch, Martin Langanke

Abstract readEnglish Abstract
PubMed Full text at PMC Publisher
In one paragraph

Article in Bundesgesundheitsblatt, Gesundheitsforschung, Gesundheitsschutz, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

9 authors.

Tanja BratanCompetence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland. Tanja.Bratan@isi.fraunhofer.de.
Diana SchneiderCompetence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland.
Florian FunerInstitut für Ethik, Geschichte und Philosophie der Medizin, Medizinische Hochschule Hannover (MHH), Hannover, Deutschland.
Nils B HeyenCompetence Center Neue Technologien, Fraunhofer-Institut für System- und Innovationsforschung ISI, Breslauer Straße 48, 76139, Karlsruhe, Deutschland.
Andrea KlausenUniklinik RWTH Aachen, Institut für Medizinische Informatik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Deutschland.
Wenke LiedtkeTheologische Fakultät, Universität Greifswald, Greifswald, Deutschland.
Myriam LipprandtUniklinik RWTH Aachen, Institut für Medizinische Informatik, Rheinisch-Westfälische Technische Hochschule (RWTH) Aachen, Aachen, Deutschland.
Sabine SallochInstitut für Ethik, Geschichte und Philosophie der Medizin, Medizinische Hochschule Hannover (MHH), Hannover, Deutschland.
Martin LangankeAngewandte Ethik/Fachbereich Soziale Arbeit, Evangelische Hochschule Rheinland-Westfalen-Lippe, Bochum, Deutschland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical decision support systems (CDSS) based on artificial intelligence (AI) are complex socio-technical innovations and are increasingly being used in medicine and nursing to improve the overall quality and efficiency of care, while also addressing limited financial and human resources. However, in addition to such intended clinical and organisational effects, far-reaching ethical, social and legal implications of AI-based CDSS on patient care and nursing are to be expected. To date, these normative-social implications have not been sufficiently investigated. The BMBF-funded project DESIREE (DEcision Support In Routine and Emergency HEalth Care: Ethical and Social Implications) has developed recommendations for the responsible design and use of clinical decision support systems. This article focuses primarily on ethical and social aspects of AI-based CDSS that could have a negative impact on patient health. Our recommendations are intended as additions to existing recommendations and are divided into the following action fields with relevance across all stakeholder groups: development, clinical use, information and consent, education and training, and (accompanying) research.

Indexed as

Artificial IntelligenceDecision Support Systems, ClinicalGermanyHumansNursing CarePractice Guidelines as TopicSoftware DesignAI-based decision support systemsArtificial intelligenceEthicsMedicineNursing

Identifiers

PMID39017712
PMCPMC11349829

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.