Evidence map›Paper›PMID 42519990›Full record

ArticleJournal of medical Internet research2026

Shadow AI in Swedish Health Care: Qualitative Analysis of Physicians' Free-Text Answers.

Lena Petersson, Luís Irgang, Ingela Mauritzon, Magnus Holmén

Abstract read
In one paragraph

Article in Journal of medical Internet research, 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.

Lena PeterssonThe Department of Health and Sport, School of Health and Welfare, Halmstad University, Box 823, Halmstad, Halland, 301 18, Sweden, 46 702055024.ORCID http://orcid.org/0000-0001-7874-7970
Luís IrgangThe Department of Health and Sport, School of Health and Welfare, Halmstad University, Box 823, Halmstad, Halland, 301 18, Sweden, 46 702055024.ORCID http://orcid.org/0000-0001-9033-3957
Ingela MauritzonSchool of Business, Innovation, and Sustainability, Halmstad University, Halmstad, Halland, Sweden.ORCID http://orcid.org/0009-0000-5499-5822
Magnus HolménSchool of Business, Innovation, and Sustainability, Halmstad University, Halmstad, Halland, Sweden.ORCID http://orcid.org/0000-0002-0560-7392

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The rapid emergence of artificial intelligence (AI) has outpaced its formal adoption in health care organizations, contributing to the emergence of Shadow AI, defined here as the use of unauthorized AI tools by medical professionals. Under the European Union Medical Device Regulation, AI tools used for clinical purposes must undergo conformity assessment before use; general-purpose tools such as ChatGPT have not done so, rendering their clinical application unauthorized at the regulatory level. While Shadow AI offers potential efficiency gains and higher performance, it poses significant risks to data privacy, clinical safety, and regulatory compliance. Despite its growing prevalence, empirical research on the purposes for which physicians use Shadow AI remains scarce. Objective: This study explores the purposes for which physicians describe using Shadow AI in their work. Methods: We conducted a cross-sectional survey of physicians employed in Swedish health care organizations (N=357; response rate~64%). Data were collected between December 2023 and January 2024 via a verified online panel. We conducted a qualitative content analysis of free-text responses on the use of unauthorized AI tools. We applied theoretical lenses from the sociology of professions and paradox theory to interpret the empirical findings. Results: Physicians use Shadow AI for several purposes, which we grouped into 4 categories: clinical work and decision-making, administrative work, research and professional development, and technological interest and curiosity. More specifically, Shadow AI is used as a colleague and second opinion for clinical decision support (eg, differential diagnoses and rare cases), administrative tasks such as patient communication and documentation, and research aimed at staying up to date and exploring developments in generative AI. Physicians described using these tools compensated for perceived gaps in institutional systems, reducing workload, and accessing knowledge considered difficult to obtain through conventional channels. The findings reveal a tension between physicians' drive to improve their practice and the regulatory and organizational constraints that render such use unauthorized. Conclusions: Shadow AI used by physicians presents both opportunities and risks for health care professionals and organizations. Shadow AI indicates gaps where formal hospital systems may fail to meet health care professionals' needs and signals a way for physicians to strengthen their experience-based knowledge. It represents a renegotiation of professional boundaries, as physicians bypass institutional constraints to maintain professional efficacy. The findings highlight a paradox in which the same tools that pose regulatory and safety risks also address real gaps in clinical and administrative support, suggesting that governance approaches must account for this tension rather than relying on prohibition alone.

Indexed as

Artificial IntelligenceDelivery of Health CarePhysiciansCross-Sectional StudiesGenerative Artificial IntelligenceHumansSurveys and QuestionnairesSwedenartificial intelligenceChatGPTgenerative AIhealth carephysiciansqualitativeShadow AI

Identifiers

PMID42519990
PMCPMC13411434

What OpenQuestion holds

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Registered trials

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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.