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Cited by 297Artificial Intelligence in Healthcare and EducationAI in cancer detectionArtificial Intelligence Applications

Physician perspectives on integration of artificial intelligence into diagnostic pathology

Open access
Sarwar, Shihab, Dent, Anglin, Faust, Kevin, Richer, Maxime, Djuric, Ugljesa, Van Ommeren, Randy, Diamandis, Phedias·2019·npj Digital Medicine
DOI 10.1038/s41746-019-0106-0PMC PMC6550202
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Abstract

Advancements in computer vision and artificial intelligence (AI) carry the potential to make significant contributions to health care, particularly in diagnostic specialties such as radiology and pathology. The impact of these technologies on physician stakeholders is the subject of significant speculation. There is however a dearth of information regarding the opinions, enthusiasm, and concerns of the pathology community at large. Here, we report results from a survey of 487 pathologist-respondents practicing in 54 countries, conducted to examine perspectives on AI implementation in clinical practice. Despite limitations, including difficulty with quantifying response bias and verifying identity of respondents to this anonymous and voluntary survey, several interesting findings were uncovered. Overall, respondents carried generally positive attitudes towards AI, with nearly 75% reporting interest or excitement in AI as a diagnostic tool to facilitate improvements in workflow efficiency and quality assurance in pathology. Importantly, even within the more optimistic cohort, a significant number of respondents endorsed concerns about AI, including the potential for job displacement and replacement. Overall, around 80% of respondents predicted the introduction of AI technology in the pathology laboratory within the coming decade. Attempts to identify statistically significant demographic characteristics (e.g., age, sex, type/place of practice) predictive of attitudes towards AI using Kolmogorov–Smirnov (KS) testing revealed several associations. Important themes which were commented on by respondents included the need for increasing efforts towards physician training and resolving medical-legal implications prior to the generalized implementation of AI in pathology.

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  • Previous studies exploring the implementation of these models have focused on surveying clinician perspectives on future rollout.

    “Previous studies exploring the implementation of these models have focused on surveying clinician perspectives on future rollout [42,49-51].”

    From Integrating a Machine Learning System Into Clinical Workflows: Qualitative Study (2020) · cites this paper as [42]

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Cited by

Sorted by citations

Authors
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P.
Affiliations
  • University of Toronto (CA)
  • University Health Network (CA)
Corresponding author
Diamandis, Phedias
ORCID
Venue
npj Digital Medicine
Publisher
Springer Science and Business Media LLC
Published
2019-04-26
Volume
2
Issue
1
Pages
28-28
ISSN
2398-6352 (ISSN-L)
DOI
10.1038/s41746-019-0106-0
PMC
PMC6550202
OpenAlex
W2940900531
Cited by
297
References
7
Citations by year
2026: 30 · 2025: 53 · 2024: 57 · 2023: 44 · 2022: 51 · 2021: 34 · 2020: 21 · 2019: 7
FWCI
8.99
Type
journal article
Language
en
Open access
Licence
Locations
  • npj Digital Medicine (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
American Society of Clinical Oncology Conquer Cancer Foundation The Brain Tumour Foundation of Canada
Topics
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
Keywords
artificial intelligence, diagnostic pathology, clinical implementation of AI, workflow efficiency, quality assurance, physician training, medical-legal implications, computer vision

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