Evidence map›Paper›PMID 37721797›Full record

ArticleJournal of medical Internet research2023

Patients' Views on AI for Risk Prediction in Shared Decision-Making for Knee Replacement Surgery: Qualitative Interview Study.

Daniel J Gould, Michelle M Dowsey, Marion Glanville-Hearst, Tim Spelman, James A Bailey, Peter F M Choong, Samantha Bunzli

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
0.9field-weighted citation impact, top 19% 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

12 citing papers in PubMed, 24 citations in OpenAlex.

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  9. Patient perspectives on AI-based decision support in surgery.BMJ surgery, interventions, & health technologies · 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

7 authors at 2 institutions in 1 country.

Daniel J GouldSt Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-0423-5822
Michelle M DowseySt Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-9708-5308
Marion Glanville-HearstSt Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-6711-6436
Tim SpelmanSt Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia.ORCID 0000-0001-9204-3216
James A BaileySchool of Computing and Information Systems, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-3769-3811
Peter F M ChoongSt Vincent's Hospital, Department of Surgery, University of Melbourne, Melbourne, Australia.ORCID 0000-0002-3522-7374
Samantha BunzliSchool of Health Sciences and Social Work, Griffith University, Brisbane, Australia.ORCID 0000-0002-5747-9361
The University of Melbourne · AUGriffith University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe use of artificial intelligence (AI) in decision-making around knee replacement surgery is increasing, and this technology holds promise to improve the prediction of patient outcomes. Ambiguity surrounds the definition of AI, and there are mixed views on its application in clinical settings.

objectiveIn this study, we aimed to explore the understanding and attitudes of patients who underwent knee replacement surgery regarding AI in the context of risk prediction for shared clinical decision-making.

methodsThis qualitative study involved patients who underwent knee replacement surgery at a tertiary referral center for joint replacement surgery. The participants were selected based on their age and sex. Semistructured interviews explored the participants' understanding of AI and their opinions on its use in shared clinical decision-making. Data collection and reflexive thematic analyses were conducted concurrently. Recruitment continued until thematic saturation was achieved.

resultsThematic saturation was achieved with 19 interviews and confirmed with 1 additional interview, resulting in 20 participants being interviewed (female participants: n=11, 55%; male participants: n=9, 45%; median age: 66 years). A total of 11 (55%) participants had a substantial postoperative complication. Three themes captured the participants' understanding of AI and their perceptions of its use in shared clinical decision-making. The theme Expectations captured the participants' views of themselves as individuals with the right to self-determination as they sought therapeutic solutions tailored to their circumstances, needs, and desires, including whether to use AI at all. The theme Empowerment highlighted the potential of AI to enable patients to develop realistic expectations and equip them with personalized risk information to discuss in shared decision-making conversations with the surgeon. The theme Partnership captured the importance of symbiosis between AI and clinicians because AI has varied levels of interpretability and understanding of human emotions and empathy.

conclusionsPatients who underwent knee replacement surgery in this study had varied levels of familiarity with AI and diverse conceptualizations of its definitions and capabilities. Educating patients about AI through nontechnical explanations and illustrative scenarios could help inform their decision to use it for risk prediction in the shared decision-making process with their surgeon. These findings could be used in the process of developing a questionnaire to ascertain the views of patients undergoing knee replacement surgery on the acceptability of AI in shared clinical decision-making. Future work could investigate the accuracy of this patient group's understanding of AI, beyond their familiarity with it, and how this influences their acceptance of its use. Surgeons may play a key role in finding a place for AI in the clinical setting as the uptake of this technology in health care continues to grow.

Indexed as

Arthroplasty, Replacement, KneeOrthopedic ProceduresAgedArtificial IntelligenceClinical Decision-MakingCommunicationFemaleHumansMaleartificial intelligenceknee replacementpatient perceptionpatient perspectivepatient preferencepatient understandingqualitative researchrisk predictionsemistructured interviews

Identifiers

PMID37721797
PMCPMC10546266
OpenAlexW4386038886

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.