Evidence map›Paper›PMID 40435166›Full record

ArticlePloS one2025

Physician awareness of, interest in, and current use of artificial intelligence large language model-based virtual assistants.

Rachel L Solmonovich, Insaf Kouba, Ji Y Lee, Kristen Demertzis, Matthew J Blitz

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
  2. Article
  3. 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

5 authors.

Rachel L SolmonovichNorthwell, New Hyde Park, New York, United States of America.ORCID https://orcid.org/0009-0007-5063-5345
Insaf KoubaNorthwell, New Hyde Park, New York, United States of America.ORCID https://orcid.org/0000-0001-9245-0081
Ji Y LeeBiostatistics Unit, Office of Academic Affairs, Northwell Health, New Hyde Park, New York, United States of America.
Kristen DemertzisNorthwell, New Hyde Park, New York, United States of America.
Matthew J BlitzNorthwell, New Hyde Park, New York, United States of America.ORCID https://orcid.org/0000-0002-5534-6928

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There is increasing medical interest and research regarding the potential of large language model-based virtual assistants in healthcare. It is important to understand physicians' interest in implementing these tools into clinical practice, so preceding education could be implemented to ensure appropriate and ethical use. We aimed to assess physician 1) awareness of, 2) interest in, and 3) current use of large language model-based virtual assistants for clinical practice and professional development and determine the specific applications of interest and use. Additionally, we wanted to determine associations with age, gender, and role. We conducted a cross-sectional study between 11/08-12/2023 via an anonymous web-based survey that was disseminated among physicians at a large NY healthcare network using snowball sampling. Descriptive and basic inferential statistics were performed. There were 562 respondents, largely males (55.7%), attending physicians (68.5%), and from nonsurgical specialties (67.4%). Most were aware of large language model chatbots (89.7%) and expressed interest (97.2%). Only a minority incorporated it into their practice (21%). Highest levels of interest were for journal review, patient education, and documentation/dictation (88.1-89.5%). The most frequently employed uses were medical information and education and study/research design. Females showed higher interest than males (99.2% vs. 95.5%, p = 0.011). Attendings were more aware of large language models (92.2% vs. 84.2%, p = 0.004), while trainees had increased rates of use (28.8% vs. 17.4%, p = 0.002). Use varied across age brackets, highest among 20-30 year olds (29.1% vs. 13.5%-23.4%, p = 0.018), except for documentation/dictation, where highest use was among the 41-50 year old group (10.5% vs. 2.6%-8.7%, p = 0.047). We concluded that physicians are interested in large language model-based virtual assistants, a minority are implementing it into their practice, and gender-, role-, and age-based disparities exist. As physicians continue to integrate large language models into their patient care and professional development, there is opportunity for research, education, and guidance to ensure an inclusive, responsible, and safe adoption.

Indexed as

Artificial IntelligenceLanguagePhysiciansAdultCross-Sectional StudiesFemaleHumansLarge Language ModelsMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40435166
PMCPMC12118853

What OpenQuestion 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 OpenQuestion graph.