ReviewJMIR AI2025
Exploring Primary Care Patients' Perspectives on Artificial Intelligence: Systematic Literature Review and Qualitative Meta-Synthesis.
Review in JMIR AI, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Artificial intelligence to support clinical practice across the inpatient heart failure care pathway: a systematic review.European heart journal. Digital health · 2026Review
- Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The introduction of artificial intelligence (AI) in health care holds great promise, offering the potential to alleviate physicians' workloads and allocate more time for patient interactions. After the emergence of large language models (LLMs), interest in AI has surged in the health care sector, including within primary care. However, patients have expressed concerns about the ethical implications and use of AI in primary care. Understanding patients' perspectives on using AI in primary care is crucial for its effective integration. Despite this, few studies have addressed patients' perspectives on using AI in primary care. Objective: This study aimed to synthesize qualitative research on primary care patients' perspectives regarding the use of AI, including LLMs, in primary care. Methods: A qualitative systematic review, using thematic analysis, was performed in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Databases, including PubMed, Scopus, Web of Science, CINAHL, and PsycINFO, were searched from inception to February 5, 2024. Eligible studies (1) used a qualitative interview research design, (2) explored primary care patients' perspectives on the use of AI in primary care, (3) were written in English, and (4) were published in peer-reviewed scientific journals. Quantitative studies, gray literature, surveys, and studies lacking depth in qualitative analysis were excluded. The Critical Appraisal Skills Program (CASP) checklist was used for quality assessment. Results: Of 1004 studies screened, 6 were included, comprising 170 patients aged 13-91 years from 3 countries. Three themes emerged: "The Relationship with and Actions of AI Systems," "Implementing AI responsibly," and "Training Physicians and Artificial Minds." Patients acknowledged AI's potential benefits but advocated for clinician oversight, safety frameworks, and the preservation of patient autonomy. Conclusions: This systematic review provides an understanding of patients' perspectives on AI in primary care. We identified heterogeneity in AI definitions across studies. Further research is needed on patients' perspectives across different countries. Notably, our synthesis revealed a significant research gap, as none of the included studies particularly explored patients' perspectives on LLMs, highlighting an important area for future research.
Indexed as
Identifiers
What Socratic holds
Registered trials
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 Socratic graph.