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Cited by 53Artificial Intelligence in Healthcare and EducationAI in cancer detectionArtificial Intelligence Applications

Integrating artificial intelligence in pathology: a qualitative interview study of users' experiences and expectations

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Drogt, Jojanneke, Milota, Megan, Vos, Shoko, Bredenoord, Annelien, Jongsma, Karin·2022·Modern Pathology
DOI 10.1038/s41379-022-01123-6PMC PMC9596368
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Abstract

Recent progress in the development of artificial intelligence (AI) has sparked enthusiasm for its potential use in pathology. As pathology labs are currently starting to shift their focus towards AI implementation, a better understanding how AI tools can be optimally aligned with the medical and social context of pathology daily practice is urgently needed. Strikingly, studies often fail to mention the ways in which AI tools should be integrated in the decision-making processes of pathologists, nor do they address how this can be achieved in an ethically sound way. Moreover, the perspectives of pathologists and other professionals within pathology concerning the integration of AI within pathology remains an underreported topic. This article aims to fill this gap in the literature and presents the first in-depth interview study in which professionals' perspectives on the possibilities, conditions and prerequisites of AI integration in pathology are explicated. The results of this study have led to the formulation of three concrete recommendations to support AI integration, namely: (1) foster a pragmatic attitude toward AI development, (2) provide task-sensitive information and training to health care professionals working in pathology departments and (3) take time to reflect upon users' changing roles and responsibilities.

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  • Ten studies describe factors relating to AI's impact on clinical tasks, such as an overall decreased time spent on repetitive medical and non-medical tasks and the possibility to delegate other tasks to AI, which could allow more time and focus on direct interaction with patients or other perceived critical tasks.

    “Ten studies describe factors relating to AI’s impact on their clinical tasks, such as an overall decreased time spent on repetitive medical and non-medical tasks [30, 40, 46, 52–55] and the possibility to delegate other tasks to AI [47], which in turn could allow them to focus more time and focus on direct interaction with their patients or other perceived critical tasks [45, 56].”

    From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [54]

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  • Ethical facilitators are seen insofar that AI systems would not discriminate against patients while also providing equal care to all which is evidence-based, ensuring that all patients receive fair and equitable treatment.

    “Ethical facilitators are seen insofar that AI systems would not discriminate against patients [44, 86] while also providing equal care to all which is evidence-based, ensuring that all patients receive fair and equitable treatment [54, 65].”

    From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [54]

    The Results (‘AI in the daily workflow’; page unavailable) say respondents hoped AI would assess images more consistently and support a more evidence-based approach. Recommendation 3 calls for ongoing attention to algorithmic bias. Neither passage says AI systems would not discriminate against patients or ensure equal, equitable care for all. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The main quality of care benefits from AI integration is seen as an overall improvement in clinical outcomes by increasing patient safety and enhancing medical standards.

    “The main quality of care benefits from AI integration is seen as an overall improvement in clinical outcomes [33, 54, 55, 79, 80, 84] by increasing patient safety [32, 46, 49, 72] and enhancing medical standards [85].”

    From Healthcare professionals’ perspectives on artificial intelligence in patient care: a systematic review of hindering and facilitating factors on different levels (2025) · cites this paper as [54]

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Cited by

Sorted by citations

Authors
Drogt, J., Milota, M., Vos, S., Bredenoord, A., & Jongsma, K.
Affiliations
  • University Medical Center Utrecht (NL)
  • Radboud University Medical Center (NL)
Corresponding author
Drogt, Jojanneke
ORCID
Venue
Modern Pathology
Publisher
Elsevier BV
Published
2022-11
Volume
35
Issue
11
Pages
1540-1550
ISSN
0893-3952 (ISSN-L)
DOI
10.1038/s41379-022-01123-6
PMC
PMC9596368
OpenAlex
W4289731231
Cited by
53
References
6
Citations by year
2026: 10 · 2025: 16 · 2024: 14 · 2023: 11 · 2022: 2
FWCI
3.24
Type
journal article
Language
en
Update notices
erratum (2022-11-01)
Open access
Licence
Locations
  • Modern Pathology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Utrecht University Repository (Utrecht University) (submittedVersion, other-oa)
  • Radboud Repository (Radboud University) (submittedVersion)
  • Radboud Repository (Radboud University) (submittedVersion, other-oa)
Funders
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (406.DI.19.089)
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 in pathology, artificial intelligence integration, clinical decision-making, AI ethics, changing professional roles, health professionals' experiences
MeSH terms
Artificial Intelligence, Humans, Motivation, Qualitative Research

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