Evidence map›Paper›PMID 37884940›Full record

ArticleBMC health services research2023

Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care.

Samira Adus, Jillian Macklin, Andrew Pinto

Open access · goldAbstract read
In one paragraph

Article in BMC health services research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
42citing papers in PubMed, 3 pooled it
22.3field-weighted citation impact, top 1% of its field
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

42 citing papers in PubMed, 3 syntheses or guidelines pooled it, 77 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. The potential of artificial intelligence in clinical trials.European journal of clinical investigation · 2026
    Review
  12. Artificial intelligence in rural health care: A framework for responsible adoption, readiness, and access.The Journal of rural health : official journal of the American Rural Health Association and the National Rural Health Care Association · 2026
    Article
  13. The patient matters: a roundtable discussion on pathology in the era of digitization and AI.Virchows Archiv : an international journal of pathology · 2026
    Article
  14. Article
  15. Article
  16. Article
  17. Review
  18. Engaging patients in AI governance.BMJ digital health & AI · 2026
    Article
  19. Article
  20. 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

3 authors at 2 institutions in 1 country.

Samira AdusFaculty of Medicine, University of Toronto, Toronto, ON, Canada. samira.adus@mail.utoronto.ca.
Jillian MacklinFaculty of Medicine, University of Toronto, Toronto, ON, Canada.
Andrew PintoFaculty of Medicine, University of Toronto, Toronto, ON, Canada.
University of Toronto · CAUnity Health Toronto

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is a rapidly evolving field which will have implications on both individual patient care and the health care system. There are many benefits to the integration of AI into health care, such as predicting acute conditions and enhancing diagnostic capabilities. Despite these benefits potential harms include algorithmic bias, inadequate consent processes, and implications on the patient-provider relationship. One tool to address patients' needs and prevent the negative implications of AI is through patient engagement. As it currently stands, patients have infrequently been involved in AI application development for patient care delivery. Furthermore, we are unaware of any frameworks or recommendations specifically addressing patient engagement within the field of AI in health care.

methodsWe conducted four virtual focus groups with thirty patient participants to understand of how patients can and should be meaningfully engaged within the field of AI development in health care. Participants completed an educational module on the fundamentals of AI prior to participating in this study. Focus groups were analyzed using qualitative content analysis.

resultsWe found that participants in our study wanted to be engaged at the problem-identification stages using multiple methods such as surveys and interviews. Participants preferred that recruitment methodologies for patient engagement included both in-person and social media-based approaches with an emphasis on varying language modalities of recruitment to reflect diverse demographics. Patients prioritized the inclusion of underrepresented participant populations, longitudinal relationship building, accessibility, and interdisciplinary involvement of other stakeholders in AI development. We found that AI education is a critical step to enable meaningful patient engagement within this field. We have curated recommendations into a framework for the field to learn from and implement in future development.

conclusionGiven the novelty and speed at which AI innovation is progressing in health care, patient engagement should be the gold standard for application development. Our proposed recommendations seek to enable patient-centered AI application development in health care. Future research must be conducted to evaluate the effectiveness of patient engagement in AI application development to ensure that both AI application development and patient engagement are done rigorously, efficiently, and meaningfully.

Indexed as

Artificial IntelligenceHealth FacilitiesEducational StatusFocus GroupsHumansProfessional-Patient RelationsApplication developmentArtificial intelligencePatient engagementPatient perspectiveQualitative research

Identifiers

PMID37884940
PMCPMC10605984
OpenAlexW4387957120

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.