Evidence map›Paper›PMID 40199292›Full record

ReviewYearbook of medical informatics2024

Searching for Value Sensitive Design in Applied Health AI: A Narrative Review.

Yufei Long, Laurie Novak, Colin G Walsh

Abstract readReview
In one paragraph

Review in Yearbook of medical informatics, 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. Article
  2. Review
  3. Digital Health for Precision Prevention.Yearbook of medical informatics · 2024
    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

3 authors.

Yufei LongDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
Laurie NovakDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.
Colin G WalshDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveRecent advances in the implementation of healthcare artificial intelligence (AI) have drawn attention toward design methods to address the impacts on workflow. Lesser known than human-centered design, Value Sensitive Design (VSD) is an established framework integrating values into conceptual, technical, and empirical investigations of technology. We sought to study the current state of the literature intersecting elements of VSD with practical applications of healthcare AI.

methodsUsing a modified VSD framework attentive to AI-specific values, we conducted a narrative review informed by PRISMA guidelines and assessed VSD elements across design and implementation case studies.

resultsOur search produced 819 articles that went through multiple rounds of review. Nine studies qualified for full-text review. Most of the studies focused on values for the individual or professional practice such as trust and autonomy. Attention to organizational (e.g., stewardship, employee well-being) and societal (e.g., equity, justice) values was lacking. Studies were primarily from the U.S. and Western Europe.

conclusionFuture design studies might better incorporate components of VSD by considering larger domains, organizational and societal, in value identification and to bridge to design processes that are not just human-centered but value sensitive. The small number of heterogeneous studies underlines the importance of broader studies of elements of VSD to inform healthcare AI in practice.

Indexed as

Artificial IntelligenceDelivery of Health CareHumans

Identifiers

PMID40199292
PMCPMC12020519

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.