TY - JOUR
T1 - What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals
AU - Fischer, Anne
AU - Rietveld, Anna
AU - Teunissen, Pim
AU - Hoogendoorn, Mark
AU - Bakker, Petra
N1 - Funding Information: AF, AR and PB are funded by a grant of public–private partnerships (PPP) from Amsterdam UMC (grant identifier 2007651). The collaboration project is co-funded by the PPP Allowance made available by Health~Holland, Top Sector Life Sciences & Health, to stimulate PPP. This study has been performed in the context of the COCOON (Combining cord-free uterine electrohysterography and standard clinical measurements for refining the detection of premature birth) study, a cooperation of Stichting VUmc, Stichting VU, Health~Holland and Bloom Technologies NV. All funding bodies played no role in the creation of this paper. Publisher Copyright: © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
PY - 2023/10/24
Y1 - 2023/10/24
N2 - 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.
AB - 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.
KW - gynaecology
KW - obstetrics
KW - qualitative research
UR - http://www.scopus.com/inward/record.url?scp=85175217591&partnerID=8YFLogxK
U2 - https://doi.org/10.1136/bmjopen-2023-076017
DO - https://doi.org/10.1136/bmjopen-2023-076017
M3 - Article
C2 - 37879682
SN - 2044-6055
VL - 13
SP - e076017
JO - BMJ Open
JF - BMJ Open
IS - 10
M1 - e076017
ER -