Evidence map›Paper›PMID 42330487›Full record

ArticleJournal of medical Internet research2026

Solidarity or Segregation? ChatGPT Health and US Health Care Disparities.

Andrew J Barnhart, Giuseppe Comerci, Barbara Prainsack, Matthias Braun

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. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Andrew J BarnhartDepartment of Social Ethics, University of Bonn, Am Hofgarten 8, Bonn, North Rhine-Westphalia, 53113, Germany.ORCID http://orcid.org/0000-0002-7935-253X
Giuseppe ComerciDepartment of Social Ethics, University of Bonn, Am Hofgarten 8, Bonn, North Rhine-Westphalia, 53113, Germany.ORCID http://orcid.org/0009-0009-2520-082X
Barbara PrainsackDepartment of Political Science, University of Vienna, Vienna, Austria.ORCID http://orcid.org/0000-0002-6335-1532
Matthias BraunDepartment of Social Ethics, University of Bonn, Am Hofgarten 8, Bonn, North Rhine-Westphalia, 53113, Germany.ORCID http://orcid.org/0000-0002-6687-6027

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unlabelled: ChatGPT Health, an artificial intelligence feature launched by OpenAI in January 2026, integrates personal medical records and consumer health data into a large language model-based chatbot. It positions itself as a digital health tool to help users navigate a fragmented US health care system marked by inaccessible insurance, medical deserts, and workforce shortages. We argue that, rather than closing these gaps, ChatGPT Health risks widening what we call the solidarity gap in health care. By appearing to address unmet needs, it may obscure and entrench the structural conditions that produce health care disparities. We identify specific risks to patient safety, such as the legitimization of dangerous self-rationing and self-medication behaviors, inaccurate triage of clinical emergencies, erosion of effective health communication, and the reinforcement of confirmation and anchoring bias. These risks are particularly acute for individuals facing structural barriers to care. To prevent ChatGPT Health and similar large language model-based health tools from deepening inequities, we propose solidarity-based policy interventions such as upstream reforms that rebuild the institutional foundations of access and health equity and downstream safeguards that embed artificial intelligence tools within accountable, equitable health care infrastructures as complements to rather than substitutes for human care.

Indexed as

Healthcare DisparitiesDigital HealthGenerative Artificial IntelligenceHumansLarge Language ModelsUnited StatesAIartificial intelligencedigital health policyhealth care inequitylarge language modelssolidarity

Identifiers

PMID42330487
PMCPMC13286327

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.