Evidence map›Paper›PMID 40103749›Full record

ArticleFrontiers in artificial intelligence2025

Augmented intelligence with voice assistance and automated machine learning in Industry 5.0.

Alexandros Bousdekis, Mina Foosherian, Mattheos Fikardos, Stefan Wellsandt, Katerina Lepenioti, Enrica Bosani, Gregoris Mentzas, Klaus-Dieter Thoben

Abstract read
In one paragraph

Article in Frontiers in artificial intelligence, 2025. 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

8 authors.

Alexandros BousdekisInformation Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece.
Mina FoosherianBIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany.
Mattheos FikardosInformation Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece.
Stefan WellsandtBIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany.
Katerina LepeniotiInformation Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece.
Enrica BosaniBeko Europe, Varese, Italy.
Gregoris MentzasInformation Management Unit (IMU), Institute of Communication and Computer Systems (ICCS), National Technical University of Athens (NTUA), Athens, Greece.
Klaus-Dieter ThobenBIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Germany.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope. In this paper, we propose the integration of voice assistance technology with Automated Machine Learning (AutoML) in order to enable the realization of the augmented intelligence paradigm in the context of Industry 5.0. In this way, the user is able to interact with the assistant through Speech-To-Text (STT) and Text-To-Speech (TTS) technologies, and consequently with the Machine Learning (ML) pipelines that are automatically created with AutoML, through voice in order to receive immediate insights while performing their task. The proposed approach was evaluated in a real manufacturing environment. We followed a structured evaluation methodology, and we analyzed the results, which demonstrates the effectiveness of our proposed approach.

Indexed as

artificial intelligenceautomated machine learningdigital intelligent assistanthuman-AI collaborationsmart manufacturingvoice assistance

Identifiers

PMID40103749
PMCPMC11913813

What OpenQuestion holds

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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.