What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals
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Abstract
OBJECTIVE: This work explores the perceptions of obstetrical clinicians about artificial intelligence (AI) in order to bridge the gap in uptake of AI between research and medical practice. Identifying potential areas where AI can contribute to clinical practice, enables AI research to align with the needs of clinicians and ultimately patients. DESIGN: Qualitative interview study. SETTING: A national study conducted in the Netherlands between November 2022 and February 2023. PARTICIPANTS: Dutch clinicians working in obstetrics with varying relevant work experience, gender and age. ANALYSIS: Thematic analysis of qualitative interview transcripts. RESULTS: Thirteen gynaecologists were interviewed about hypothetical scenarios of an implemented AI model. Thematic analysis identified two major themes: perceived usefulness and trust. Usefulness involved AI extending human brain capacity in complex pattern recognition and information processing, reducing contextual influence and saving time. Trust required validation, explainability and successful personal experience. This result shows two paradoxes: first, AI is expected to provide added value by surpassing human capabilities, yet also a need to understand the parameters and their influence on predictions for trust and adoption was expressed. Second, participants recognised the value of incorporating numerous parameters into a model, but they also believed that certain contextual factors should only be considered by humans, as it would be undesirable for AI models to use that information. CONCLUSIONS: Obstetricians' opinions on the potential value of AI highlight the need for clinician-AI researcher collaboration. Trust can be built through conventional means like randomised controlled trials and guidelines. Holistic impact metrics, such as changes in workflow, not just clinical outcomes, should guide AI model development. Further research is needed for evaluating evolving AI systems beyond traditional validation methods.
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Five studies reported facilitating factors at the individual level that encourage greater knowledge about AI by healthcare professionals, including being familiar with AI, having time to trial the AI application, receiving education and training programs, and conferences and scientific networks.
“Five studies reported on the facilitating factors at the indivudual level that encourage greater knowledge about AI by healthcare professionals, including being familiar with AI [34, 37, 38], having time to trial the AI application in their practice [39] and receiving education and training programs [39] as well as conferences and scientific networks [40] as opportunities to learn about them.”
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 [34]
In Results > Successful personal experience (page unavailable), participants say they need time working with an AI model to gain experience and trust, supporting the time-to-try-it portion attributed to this source. The passage does not describe education or training programs as an opportunity to learn about AI. The other factors in the sentence have separate citations and are not assessed here. (LLM, conf=0.84)
Experimental — accuracy not yet measuredOverall system-wide benefits for patients are seen in improved access to healthcare services, for instance by establishing remote support for patients, and by improving population health indicators by supporting measures of disease prevention and health promotion.
“Overall system-wide benefits for patients are seen in an improved access to healthcare services [32, 59, 60, 71], for instance by establishing remote support for patients [34], and by improving population health indicators by supporting measures of disease prevention and health promotion [79].”
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 [34]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredA handful of studies could be clearly assigned as natural language processing models or rule-based expert systems.
“A handful of studies could be clearly assigned as “natural language processing models” [29–32] or “rule-based expert systems” [33–36].”
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 [34]
The Results (Explainability; page unavailable) discuss clinicians’ views of AI-model parameters, and the Discussion (Interpretation; page unavailable) compares deep learning with traditional AI models. Neither section classifies a handful of studies as natural language processing models or rule-based expert systems; searches for both terms found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
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- Authors
- Fischer, A., Rietveld, A., Teunissen, P., Hoogendoorn, M., & Bakker, P.
- Affiliations
- Amsterdam Reproduction Development (NL)
- Maastricht University Medical Centre (NL)
- Vrije Universiteit Amsterdam (NL)
- Corresponding author
- Fischer, Anne
- Venue
- BMJ Open
- Publisher
- BMJ
- Published
- 2023-10
- Volume
- 13
- Issue
- 10
- Pages
- e076017
- ISSN
- 2044-6055 (ISSN-L)
- PMC
- PMC10603416
- OpenAlex
- W4387940598
- Cited by
- 26
- References
- 41
- Citations by year
- 2026: 13 · 2025: 10 · 2024: 3
- FWCI
- 1.03
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Pure Amsterdam UMC (publishedVersion, cc-by)
- Research Publications (Maastricht University) (submittedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- HighWire Press Open Archive (submittedVersion)
- Amsterdam UMC (VU Amsterdam) - Institutional Repository (submittedVersion, other-oa)
- VU Research Portal (submittedVersion, cc-by-nc)
- Funders
- VU
- Bloom
- Health~Holland (2007651)
- PPP (2007651)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- clinician perceptions, trust in AI, perceived usefulness, explainability, artificial intelligence adoption, clinician-AI collaboration, clinical workflow, gynecologists, research-practice gap
- MeSH terms
- Obstetricians, Artificial Intelligence, Delivery of Health Care, Female, Health Personnel, Humans, Obstetrics, Pregnancy
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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