Evidence map›Paper›PMID 41355848›Full record

ArticleBig data & society

Information, collaboration, regulation: Physician and AI researcher views on ethical considerations in clinical AI integration.

Katie Ryan, Max Kasun, Laura W Roberts, Jane Paik Kim

Abstract read
In one paragraph

Article in Big data & society. 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. Article
  3. Article
  4. Review
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

4 authors.

Katie RyanDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-1437-3126
Max KasunDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-6364-6234
Laura W RobertsDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0003-4270-253X
Jane Paik KimDepartment of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.ORCID 0000-0002-1475-9822

Funding

Stakeholder Guidance to Anticipate and Address Ethical Challenges in Applications of Machine Learning and Artificial Intelligence in Algorithmic Medicine: a Novel Empirical ApproachR01TR003505 · NCATS · STANFORD UNIVERSITY · PI KIM, JANE PAIK · 2020 to 2023
$1.8M
NCATS NIH HHS R01 TR003505
6 · The paper itself

Abstract

Due to the potential scope and impact of artificial intelligence's (AI's) adoption in medicine, a comprehensive assessment of the potential ethical considerations arising during clinical integration is needed. Existing ethical frameworks and principles have aided in the identification of ethical considerations that may arise during the clinical integration of AI, however, our understanding of these considerations remains preliminary to the extent that it is not yet robustly informed by empirical research on key stakeholders' experiences and perspectives. Utilizing a qualitative descriptive approach, we completed in-depth semi-structured interviews with physicians (

Indexed as

artificial intelligencedevelopersEthicsmedicinephysiciansqualitative research

Identifiers

PMID41355848
PMCPMC12680088

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.