Evidence map›Paper›PMID 42147757›Full record

ArticleProceedings of the SIGCHI conference on human factors in computing systems. CHI Conference2026

Intelligent Reasoning Cues: A Framework and Case Study of the Roles of AI Information in Complex Decisions.

Venkatesh Sivaraman, Eric Mason, Mengfan Li, Jessica Tong, Andrew J King, Jeremy M Kahn, Adam Perer

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

7 authors.

Venkatesh SivaramanCarnegie Mellon University, Pittsburgh, PA, USA.ORCID 0000-0002-6965-3961
Eric MasonCarnegie Mellon University, Pittsburgh, PA, USA.
Mengfan LiCarnegie Mellon University, Pittsburgh, PA, USA.
Jessica TongPomona College, Claremont, CA, USA.
Andrew J KingUniversity of Pittsburgh, Pittsburgh, PA, USA.
Jeremy M KahnUniversity of Pittsburgh, Pittsburgh, PA, USA.
Adam PererCarnegie Mellon University, Pittsburgh, PA, USA.

Funding

Organizational strategies for improving evidence-uptake in intensive careR35HL144804 · NHLBI · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KAHN, JEREMY M · 2019 to 2025
$6.6M
NHLBI NIH HHS R35 HL144804
6 · The paper itself

Abstract

Artificial intelligence (AI)-based decision support systems can be highly accurate yet still fail to support users or improve decisions. Existing theories of AI-assisted decision-making focus on calibrating reliance on AI advice, leaving it unclear how different system designs might influence the reasoning processes underneath. We address this gap by reconsidering AI interfaces as collections of

Indexed as

AI-Assisted Decision-MakingClinical ReasoningDecision SupportExplainable AI

Identifiers

PMID42147757
PMCPMC13175582

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.