Evidence map›Paper›PMID 41469701›Full record

ArticleSystematic reviews2025

Artificial intelligence in the workplace: a living systematic review protocol on worker safety, health, and well-being implications.

Arif Jetha, Meghan Crouch, Karina Vold, Susan Elizabeth Peters, Jay Vietas, Abi Sriharan, Emma Irvin

Abstract read
In one paragraph

Article in Systematic reviews, 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

7 authors.

Arif JethaInstitute for Work & Health, Toronto, ON, Canada. AJetha@iwh.on.ca.ORCID 0000-0003-0322-7027
Meghan CrouchInstitute for Work & Health, Toronto, ON, Canada.
Karina VoldInstitute for the History and Philosophy of Science and Technology, University of Toronto, Toronto, ON, Canada.
Susan Elizabeth PetersCenter for Work, Health and Well-Being, Harvard T.H. Chan School of Public Health, Harvard University, Cambridge, MA, USA.
Jay VietasNational Safety Council, Itasca, Illinois, USA.
Abi SriharanKrembil Centre for Health Management and Leadership, Schulich School of Business, York University, Toronto, ON, Canada.
Emma IrvinInstitute for Work & Health, Toronto, ON, Canada.

Funding

NIOSH CDC HHS U19 OH008861
6 · The paper itself

Abstract

backgroundAdvancements in artificial intelligence (AI) are transforming employment and working conditions in ways that shape the safety, health, and well-being of workers. We describe a protocol for a living systematic review (LSR) that will examine the interrelationship between AI systems, employment and working conditions, and worker safety, health, and well-being. Research questions are: 1. What types of AI systems are being used within workplaces and how do their design and adoption impact worker safety, health, and well-being? 2. How do a worker's employment and working conditions affect the relationship between the adoption of AI systems and worker safety, health, and well-being? 3. How does a worker's social position (e.g., age, gender, race, disability) shape the interrelationship between AI systems at work, employment and working conditions, and their safety, health, and well-being?

methodsA comprehensive search of primary qualitative and quantitative research will be conducted. MEDLINE, Embase (OVID), PsycINFO (OVID), and Web of Science will be searched every six to twelve months using database-specific terms and keywords. Title/abstract and full-text screening will be completed independently by two reviewers. Relevant articles will be quality appraised using a mixed method assessment tool adapted for studies of AI. Medium and high-quality studies will be synthesized using a best evidence synthesis approach. To ensure relevancy, applied workplace and AI stakeholders will provide feedback at all stages of the LSR process through dissemination excluding quality appraisal. Annually, we will evaluate the appropriateness of the review process (e.g., frequency of searches, requirement to refine research questions, utility of continuing LSR). Any amendments to protocols will be documented. DISCUSSION: This LSR will provide timely and evolving evidence on the implications of AI in the workplace that will be disseminated through a publicly available living review dashboard. We will capture the emerging impact AI has on workers. Findings can be used to develop strategies to minimize AI's potential workplace harms while amplifying its potential benefits, address emerging worker inequities, and inform ongoing discussions regarding responsible and safe AI adoption. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42024625501.

Indexed as

Artificial IntelligenceOccupational HealthWorkplaceEmploymentHumansSystematic Reviews as TopicArtificial intelligenceEmploymentOccupational healthSystematic reviewsWorking conditions

Identifiers

PMID41469701
PMCPMC12754963

What Socratic 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 Socratic graph.