Evidence map›Paper›PMID 40067301›Full record

ArticleJAMA network open2025

Ethics in Patient Preferences for Artificial Intelligence-Drafted Responses to Electronic Messages.

Joanna S Cavalier, Benjamin A Goldstein, Vardit Ravitsky, Jean-Christophe Bélisle-Pipon, Armando Bedoya, Jennifer Maddocks, Sam Klotman, Matthew Roman, Jessica Sperling, Chun Xu and 2 more

Abstract read
In one paragraph

Article in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 1 pooled it
–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

19 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. The use of generative AI tools among individuals with cancer and their caregivers: a cross-sectional study of awareness, attitudes, and experiences.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
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  15. What patients want from healthcare chatbots: insights from a mixed-methods study.Journal of the American Medical Informatics Association : JAMIA · 2025
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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

12 authors.

Joanna S CavalierDepartment of Medicine, Duke University School of Medicine, Durham, North Carolina.
Benjamin A GoldsteinDepartment of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Vardit RavitskyCenter for Bioethics, Harvard Medical School, Boston, Massachusetts.
Jean-Christophe Bélisle-PiponFaculty of Health Sciences, Simon Fraser University, Burnaby, British Columbia, Canada.
Armando BedoyaDepartment of Medicine, Duke University School of Medicine, Durham, North Carolina.
Jennifer MaddocksDepartment of Health Technology Solutions, Duke University Health System, Durham, North Carolina.
Sam KlotmanDepartment of Health Technology Solutions, Duke University Health System, Durham, North Carolina.
Matthew RomanDepartment of Health Technology Solutions, Duke University Health System, Durham, North Carolina.
Jessica SperlingDuke Clinical & Translational Science Institute, Duke University, Durham, North Carolina.
Chun XuDepartment of Biostatistics & Bioinformatics, Duke University School of Medicine, Durham, North Carolina.
Eric G PoonDepartment of Health Technology Solutions, Duke University Health System, Durham, North Carolina.
Anand ChowdhuryDepartment of Medicine, Duke University School of Medicine, Durham, North Carolina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Importance: The rise of patient messages sent to clinicians via a patient portal has directly led to physician burnout and dissatisfaction, prompting uptake of artificial intelligence (AI) to alleviate this burden. It is important to understand patient preferences around AI in patient-clinician communication as ethical guidelines on appropriate use and disclosure (patient notification of AI use) are developed. Objective: To analyze patient preferences regarding use of AI in electronic messages. Design, Setting, and Participants: A survey study was conducted within the Duke University Health System's patient advisory committee, consisting of individuals 18 years or older who participate in periodic surveys to inform health system patient care practices. Multiple surveys were administered to test the impact of different factors, including response author, disclosure (AI, human, or none), and seriousness of the topic. A follow-up survey assessed preferred disclosure verbiage. Surveys were administered from October 31 to December 11, 2023. Exposure: Multiple surveys. Main Outcomes and Measures: Participants rated their overall satisfaction, usefulness of the information, and perceived level of care on a 5-point Likert scale. Results: Of the 2511 members surveyed, 1455 (57.9%) responded, with respondents being older (median age, 57 [IQR, 49-70] vs 53 [IQR, 41-62] years), more educated (872 of 1083 [80.5%] vs 319 of 440 [72.5%] with a college or graduate degree), and predominantly female (921 [63.3%]). Participants preferred AI- compared with human-drafted responses, with a mean difference for satisfaction of -0.30 (95% CI, -0.37 to -0.23) points, usefulness of -0.28 (95% CI, -0.34 to -0.22) points, and perception they were cared for of -0.43 (95% CI, -0.50 to -0.37) points. Participants tended to have higher satisfaction with a human disclosure over AI disclosure, with a mean difference of 0.13 (95% CI, 0.05-0.22) points, and with no disclosure over AI authorship disclosure, with a mean difference of 0.09 (95% CI, 0.01-0.17) points. Regardless of author or disclosure type, more than 75% of respondents were satisfied (agree or strongly agree) with the response. Conclusions and Relevance: In this survey study, participants expressed a mild preference for messages written by AI but had a slightly decreased satisfaction when told AI was involved. Patient experience must be considered along with ethical implementation of AI. Although AI disclosure may slightly reduce satisfaction, disclosure should be maintained to uphold patient autonomy and empowerment.

Indexed as

Artificial IntelligenceElectronic MailPatient PreferencePhysician-Patient RelationsAdultAgedDisclosureFemaleHumansMaleMiddle AgedPatient SatisfactionSurveys and Questionnaires

Identifiers

PMID40067301
PMCPMC11897835

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.