Evidence map›Paper›PMID 40354225›Full record

ArticleAJOB empirical bioethics

Should Physicians Take the Rap? Normative Analysis of Clinician Perspectives on Responsible Use of 'Black Box' AI Tools.

Ben H Lang, Kristin Kostick-Quenet, Jared N Smith, Meghan Hurley, Rita Dexter, Jennifer Blumenthal-Barby

Abstract read
In one paragraph

Article in AJOB empirical bioethics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Ben H LangDepartment of Philosophy, Oxford University, Oxford, UK.ORCID 0000-0002-8474-2890
Kristin Kostick-QuenetCenter for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.ORCID 0000-0003-2510-0174
Jared N SmithCenter for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.ORCID 0000-0002-2787-3798
Meghan HurleyCenter for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.ORCID 0000-0002-0418-8691
Rita DexterCenter for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.
Jennifer Blumenthal-BarbyCenter for Medical Ethics and Health Policy, Baylor College of Medicine, Houston, Texas, USA.

Funding

AHRQ HHS R01 HS027784
6 · The paper itself

Abstract

backgroundIncreasing interest in deploying artificial intelligence tools in clinical contexts has raised several ethical questions of both normative and empirical interest. One such question in the literature is whether "responsibility gaps" (r-gaps) are created when clinicians utilize or rely on such tools for providing care, and if so, what to do about them. These gaps are particularly likely to arise when using opaque, "black box" AI tools. Compared to normative and legal analysis of AI-generated responsibility gaps in health care, little is known, empirically, about health care providers views on this issue. The present study examines clinician perspectives on this issue in the context of black box AI decisional support systems (BBAI-DSS) in advanced heart failure.

methodsSemi-structured interviews were conducted with 20 clinicians (14 cardiologists and 6 LVAD nurse coordinators). Interviews were transcribed, coded, and thematically analyzed for salient themes. All study procedures were approved by local IRB.

resultsWe found that

conclusionClinicians varied in their view about the existence of r-gaps but were united in their ascriptions of physician responsibility for the use of BBAI-DSS in clinical care. It was unclear at times whether these were descriptive or normative judgments (i.e., is it merely inevitable physicians will be responsible, or is it morally appropriate that they be held responsible?) or both. We discuss the likely normative inadequacy of such a conception of physician responsibility for BBAI tool use.

Indexed as

Artificial IntelligenceAttitude of Health PersonnelDecision Support Systems, ClinicalHeart FailurePhysiciansFemaleHumansInterviews as TopicMaleQualitative ResearchArtificial intelligencecardiologyheart failureresponsibilityrisk prediction

Identifiers

PMID40354225
PMCPMC12353763

What OpenQuestion holds

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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.