Applications of human-machine collaborative decision-making: A review of research with recent developments.

Yun Luo, Yanpuze Hao, Haochen Gong, Bo Li

Abstract readReview

In one paragraph

Review in iScience, 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

4 authors.

Yun LuoSchool of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Yanpuze HaoSchool of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Haochen GongSchool of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Bo LiSchool of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study comprehensively reviews human-machine collaborative decision-making (HMCD) methods and applications across management science, the military, healthcare, and manufacturing. We propose a dual-layer analytical framework. The first layer decomposes HMCD into four sequential stages, namely attribute determination, weight assignment, information aggregation, and decision-making. The second layer identifies four cross-cutting collaboration mechanisms, namely role configuration, interaction and deliberation, trust and explanation, and authority and responsibility migration. A feedback loop connects decision outcomes to earlier stages, capturing iterative adaptation between agents. Using this framework as an analytical lens, we synthesize methods and applications, examine stage-specific collaboration patterns across domains, and trace how failures propagate through mechanism dependencies. This review provides a structured map of HMCD research and guidance for collaborative system design.

Indexed as

Applied sciencesArtificial intelligenceComputer scienceComputing methodologyDecision scienceLinguisticsSocial sciences

Identifiers

PMID42231950

DOI10.1016/j.isci.2026.116056

PMCPMC13224006

What OpenQuestion holds

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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.