Evidence map›Paper›PMID 42330509›Full record

ArticleJMIR AI2026

Patient Perceptions on the Use of Artificial Intelligence in Creating Clinical Research Documents: Survey Study.

Kimbra Edwards, Samuel Entwisle, Zack Fey, Hana Do, Art Gertel, Annick de Bruin, Kenneth Getz

Abstract read
In one paragraph

Article in JMIR AI, 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

7 authors.

Kimbra Edwards *Center for Information and Study on Clinical Research Participation, One Liberty Square, Suite 1100, Boston, MA, 02109, United States, 1 617-725-2750 ext 118.ORCID http://orcid.org/0000-0002-5742-7924
Samuel Entwisle *Center for Information and Study on Clinical Research Participation, One Liberty Square, Suite 1100, Boston, MA, 02109, United States, 1 617-725-2750 ext 118.ORCID http://orcid.org/0000-0001-8515-636X
Zack Fey *Center for Information and Study on Clinical Research Participation, One Liberty Square, Suite 1100, Boston, MA, 02109, United States, 1 617-725-2750 ext 118.ORCID http://orcid.org/0009-0004-0191-3394
Hana DoTufts Center for the Study of Drug Development, Tufts University School of Medicine, Boston, MA, United States.ORCID http://orcid.org/0009-0000-4663-2239
Art GertelMedSciCom, Lebanon, NJ, United States.ORCID http://orcid.org/0000-0002-1625-5003
Annick de BruinCenter for Information and Study on Clinical Research Participation, One Liberty Square, Suite 1100, Boston, MA, 02109, United States, 1 617-725-2750 ext 118.ORCID http://orcid.org/0000-0003-3279-8689
Kenneth GetzCenter for Information and Study on Clinical Research Participation, One Liberty Square, Suite 1100, Boston, MA, 02109, United States, 1 617-725-2750 ext 118.ORCID http://orcid.org/0000-0003-0568-2541

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The use of generative artificial intelligence (AI) by pharmaceutical companies and other organizations for preparing patient-facing documents reporting results of clinical research is becoming more common. This raises concerns about whether the accuracy and quality of these documents could be affected, as well as the potential impact on patient perceptions and trust. Accurate and trustworthy information is critical to health care decision-making. Little is known about patient perceptions of AI-generated content. Objective: This study aimed to better understand patient experience and familiarity with AI, their resulting confidence in the abilities of AI, and their trust in the use of AI by research organizations to generate clinical research documents. Methods: An online survey was conducted using an online health care panel of patients in Europe and the United States to assess familiarity with AI, trust in organizations reporting on research, and trust in the use of AI to prepare clinical research documents. The survey also asked directly about the importance of human involvement and of transparency in disclosing AI use. Results: A total of 1010 respondents completed the online survey. About half of respondents were from the United States and half from Europe. Survey results showed that 63.6% (642/1010) of respondents had used AI before with 74.9% (756/1010) reporting being "Somewhat" or "Very" familiar with AI. AI use was influenced by country, gender, education level, race/ethnicity, and clinical trial experience. Higher familiarity with AI was observed among younger participants. Respondents were generally confident in the capabilities of AI, as more than half believed AI use would reduce grammar and data errors. Trust in clinical trial documents generally increased with greater human oversight, as trust was lowest for documents created by AI with no human involvement (12.3% "A lot" of trust, 124/1010) and highest for documents created by humans without AI (39.1% "A lot" of trust, 395/1010). 95.0% (959/1010) of respondents considered human involvement in clinical trial document review as "Very important" or "Somewhat important." The majority (62.4%, 630/1010) of respondents felt it was "Very important" for pharmaceutical companies and academic institutions to be transparent about their use of AI in public-facing documents. Transparency was considered more important among respondents in the United States and the United Kingdom compared to those in the European Union. Conclusions: The survey results reveal high familiarity with, and confidence in the capabilities of, AI. Despite this confidence, respondents emphasized the need for human involvement in the creation of clinical trial documents and the importance of disclosing AI usage, underscoring the critical role of human oversight in maintaining patient trust. Transparent integration of AI with deliberate human involvement remains essential to ensure trust in patient-facing documents.

Indexed as

artificial intelligenceclinical researchhealth communicationpatient engagementplain language summary

Identifiers

PMID42330509
PMCPMC13286328

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.