Evidence map›Paper›PMID 41963247›Full record

ArticleJournal of palliative medicine2026

How Hospice and Palliative Care Are Portrayed in Artificial Intelligence-Generated Images: A Content Analysis.

Anna Gaysynsky, Abigail Muro, Nicole Senft Everson, Irina Iles, Wen-Ying Sylvia Chou

Abstract read
In one paragraph

Article in Journal of palliative medicine, 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

5 authors.

Anna GaysynskyHealth Communication and Informatics Research Branch, National Cancer Institute, Rockville, Maryland, USA.ORCID 0000-0001-5612-5572
Abigail MuroHealth Communication and Informatics Research Branch, National Cancer Institute, Rockville, Maryland, USA.
Nicole Senft EversonHealth Communication and Informatics Research Branch, National Cancer Institute, Rockville, Maryland, USA.
Irina IlesHealth Communication and Informatics Research Branch, National Cancer Institute, Rockville, Maryland, USA.
Wen-Ying Sylvia ChouHealth Communication and Informatics Research Branch, National Cancer Institute, Rockville, Maryland, USA.

Funding

Intramural NIH HHS Z99 CA999999NCI NIH HHS HHSN261201800002BNCI NIH HHS HHSN261201800002C
6 · The paper itself

Abstract

backgroundAI-generated images can support or impede health communication efforts and influence perceptions of health-related topics, making it important to evaluate how these images portray topics like palliative care (PC) and hospice care (HC), which are characterized by limited public understanding (e.g., regarding where these services are delivered) and misperceptions (e.g., widespread conflation of these services).

objectivesTo examine characteristics of AI-generated PC and HC images.

designThree generative AI tools (ChatGPT, DreamStudio, and Midjourney) were used to generate 40 images each for the prompts "a photograph of palliative care" and "a photograph of hospice care" (

resultsPC images more often depicted medical settings (PC = 37.5%, HC = 27.5%) and medical equipment (PC = 67.5%, HC = 50.0%). DreamStudio images were the most medicalized. When included, patients were generally presented as White (PC = 85.0%, HC = 91.0%), female (PC = 81.0%, HC = 75.3%), and older (PC = 88.0%, HC = 94.4%) and rarely had negative affect (PC = 5.0%, HC = 2.2%) or looked ill (PC = 4.0%, HC = 2.2%). Providers were more racially diverse (PC = 41.1%, HC = 48.4% non-White), but nearly all were women (PC = 95.9%, HC = 96.9% female). Providers displayed supportive touch frequently (PC = 72.6%, HC = 71.9%) but were rarely shown engaging in medical tasks (PC = 12.3%, HC = 6.3%). Caregivers were infrequently included in either PC or HC images.

conclusionsThe few substantial differences identified between PC and HC images could reinforce conflation between these services. Some image features could also create additional misperceptions (e.g., regarding the role of caregivers).

Indexed as

Artificial IntelligenceHospice CarePalliative CarePhotographyAdultAgedFemaleGenerative Artificial IntelligenceHumansMaleMiddle AgedAI-generated imagescontent analysisgenerative artificial intelligencehospicepalliative carevisual communication

Identifiers

PMID41963247
PMCPMC13158862

What OpenQuestion holds

Textmetadata
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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.