Skip to content
DraconiticResearch
Search Sources API
223,421 studies31 sourcesUpdated 43 min ago
Peer-reviewedCC BYImplementation science communications

Towards clinical implementation of artificial intelligence in cancer care: concept mapping analysis of provincial workshop findings.

  • Hong Hao Xu1,2
  • Chhavi Nayyar3
  • Daniel Hilbers2
  • Jonathan Avery4
  • Srinivas Raman5
  • Alan T Bates4,6
  • Cristina Conati7
  • Ahmad Fayaz-Bakhsh8,9
  • John-Jose Nunez10,11
11 affiliations
  1. 1University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
  2. 2Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
  3. 3Faculty of Science, University of British Columbia, Vancouver, BC, Canada.
  4. 4Supportive Care Service, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada.
  5. 5Division of Radiation Oncology, Department of Surgery, Faculty of Medicine, University of British Columbia, BC Cancer - Vancouver, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada.
  6. 6Department of Psychiatry, Faculty of Medicine, University of British Columbia, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada.
  7. 7Department of Computer Science, Faculty of Science, University of British Columbia, 2366 Main Mall, Vancouver, BC, V6T 1Z4, Canada.
  8. 8School of Public Health, Tehran University of Medical Sciences, Tehran, Iran. [email protected].
  9. 9Centre for Health Services and Policy Research, University of British Columbia, Vancouver, BC, Canada. [email protected].
  10. 10Supportive Care Service, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada. [email protected].
  11. 11Department of Psychiatry, Faculty of Medicine, University of British Columbia, BC Cancer, 600 West 10th Avenue, Vancouver, BC, V5Z 4E6, Canada. [email protected].
Read full textPDFDOICite

Abstract

Background

Artificial intelligence (AI) has rapidly garnered interest in healthcare. Cancer care's multidisciplinary nature and high coordination demands are well positioned to benefit from AI. While attitudes toward implementation of AI in medicine have been explored generally, literature remains scarce with specific regards to AI in cancer care. This study sought to understand perspectives of both patients and professionals in guiding responsible, effective implementation of evidence-based AI in cancer care.Methods

We conducted a workshop at a provincial Cancer Summit (Vancouver, Canada). Discussions addressed concerns, benefits, and priorities for AI in cancer care. Responses from 48 workshop participants underwent structured conceptualization by concept mapping. Sorting and rating of the resulting statements were performed by a purposively sampled 13-member expert panel. Multidimensional scaling, hierarchical cluster and subcluster analysis produced visual and quantitative maps of findings.Results

A total of 265 statements on perceived benefits, concerns, and priorities related to the implementation of AI in cancer care were generated; a deduplicated and consolidated final set of 100 statements underwent concept mapping. Two main clusters identified pertained to "Challenges and Safeguards for AI Implementation " (Cluster 1) and "Clinical Benefits and Efficiency Gains" (Cluster 2). Subcluster analysis distinguished 8 thematic subclusters (4 per cluster). Mean importance and feasibility ratings were higher for Cluster 2, with large effect sizes for both importance (Cohen's d = 0.94) and feasibility (Cohen's d = 1.59). Ratings by clinical and nonclinical professionals were similar across comparisons except for Cluster 2 feasibility, rated higher by clinical participants (P = 0.029, Hedges' g = 0.465). Further go-zone analysis classified statements according to their relative superiority/inferiority in importance and feasibility from overall average.Conclusions

Expert panel ratings were higher for statements describing clinical benefits and efficiency gains than for those describing challenges and safeguards for AI implementation in cancer care. Concept mapping analysis distinguished between workflow-aligned AI applications, perceived as ready for implementation, and system-level governance requirements requiring longer-term investment. Present findings offer initial, context-specific structuring of stakeholder perspectives that may inform the prioritization and sequencing of AI implementation efforts in cancer care, providing a foundation for validation in other settings.

Related

  1. Artificial intelligence-enabled mental health applications for adolescents: a scoping review of safety, effectiveness and clinical implementation
    Meita Dhamayanti, Susaldi Susaldi, Fanni Hanifa +4
    BMJ OpenPeer-reviewedCC BY-NC 4.0Full textGeneral medicineCrossref
  2. Digital, Mobile and Artificial Intelligence‐Supported Interventions for Pressure Injury Prevention and Management: A Systematic Review of Clinical, Workflow and Implementation Outcomes
    Tuba Sengul, Fadime Koyuncu, Seda Sarıköse +2
    International wound journalPeer-reviewedCC BY-NC-NDFull textGeneral medicineEurope PMC
  3. Evaluation Practices and Characteristics of Implementation for Clinical Artificial Intelligence in Mental Healthcare: A Rapid Scoping Review
    Jessica Kemp, Charlotte Pape, Masooma Hassan +7
    PsyArXivPreprintCC0Full textPsychiatry & mental healthOSF
  4. Artificial Intelligence in Obstetrics and Prenatal Medicine: Current Evidence, Clinical Validation and Implementation Künstliche Intelligenz in der Geburtshilfe und Pränatalmedizin: aktuelle Evidenz, klinische Validierung und Implementierung
    Florian Recker
    Geburtshilfe und FrauenheilkundePeer-reviewedCC BY-NC-NDFull textEpidemiology & public healthEurope PMCEurope PMCPMC
  5. Artificial Intelligence in Neurology: A Critical Narrative Review of Clinical Evidence, Translation, and Responsible Implementation
    Anwar Zahran, Raya Fuqha, Aya Milhem +13
    Brain and BehaviorPeer-reviewedCC BY 4.0Full textEpidemiology & public healthPMC

Cite

BibTeXRIS
BibTeX
@article{xu2026towards,
  title = {{Towards clinical implementation of artificial intelligence in cancer care: concept mapping analysis of provincial workshop findings.}},
  author = {Hong Hao Xu and Chhavi Nayyar and Daniel Hilbers and Jonathan Avery and Srinivas Raman and Alan T Bates and Cristina Conati and Ahmad Fayaz-Bakhsh and John-Jose Nunez},
  journal = {Implementation science communications},
  year = {2026},
  month = aug,
  doi = {10.1186/s43058-026-01075-x},
  url = {https://europepmc.org/article/MED/43010090},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Towards clinical implementation of artificial intelligence in cancer care: concept mapping analysis of provincial workshop findings.
AU  - Hong Hao Xu
AU  - Chhavi Nayyar
AU  - Daniel Hilbers
AU  - Jonathan Avery
AU  - Srinivas Raman
AU  - Alan T Bates
AU  - Cristina Conati
AU  - Ahmad Fayaz-Bakhsh
AU  - John-Jose Nunez
PY  - 2026
DA  - 2026/08/14
JO  - Implementation science communications
DO  - 10.1186/s43058-026-01075-x
UR  - https://europepmc.org/article/MED/43010090
AB  - Background  Artificial intelligence (AI) has rapidly garnered interest in healthcare. Cancer care's multidisciplinary nature and high coordination demands are well positioned to benefit from AI. While attitudes toward implementation of AI in medicine have been explored generally, literature remains scarce with specific regards to AI in cancer care. This study sought to understand perspectives of both patients and professionals in guiding responsible, effective implementation of evidence-based AI in cancer care.Methods  We conducted a workshop at a provincial Cancer Summit (Vancouver, Canada). Discussions addressed concerns, benefits, and priorities for AI in cancer care. Responses from 48 workshop participants underwent structured conceptualization by concept mapping. Sorting and rating of the resulting statements were performed by a purposively sampled 13-member expert panel. Multidimensional scaling, hierarchical cluster and subcluster analysis produced visual and quantitative maps of findings.Results  A total of 265 statements on perceived benefits, concerns, and priorities related to the implementation of AI in cancer care were generated; a deduplicated and consolidated final set of 100 statements underwent concept mapping. Two main clusters identified pertained to "Challenges and Safeguards for AI Implementation " (Cluster 1) and "Clinical Benefits and Efficiency Gains" (Cluster 2). Subcluster analysis distinguished 8 thematic subclusters (4 per cluster). Mean importance and feasibility ratings were higher for Cluster 2, with large effect sizes for both importance (Cohen's d = 0.94) and feasibility (Cohen's d = 1.59). Ratings by clinical and nonclinical professionals were similar across comparisons except for Cluster 2 feasibility, rated higher by clinical participants (P = 0.029, Hedges' g = 0.465). Further go-zone analysis classified statements according to their relative superiority/inferiority in importance and feasibility from overall average.Conclusions  Expert panel ratings were higher for statements describing clinical benefits and efficiency gains than for those describing challenges and safeguards for AI implementation in cancer care. Concept mapping analysis distinguished between workflow-aligned AI applications, perceived as ready for implementation, and system-level governance requirements requiring longer-term investment. Present findings offer initial, context-specific structuring of stakeholder perspectives that may inform the prioritization and sequencing of AI implementation efforts in cancer care, providing a foundation for validation in other settings.
SN  - 2662-2211
ER  -