Cardiotocography (CTG) is widely used for assessing fetal well-being, with the aim to reduce the risk of unnecessary and invasive obstetric interventions and to improve maternal-fetal outcomes. Artificial intelligence technologies could improve the efficacy of CTG; however, the acceptance of such technologies by clinicians is a prerequisite for their sustained adoption in clinical practice. In this chapter, we present a study aiming to investigate the factors associated with the acceptance of an AI-based tool aimed at interpreting the CTG by a group of midwives. An online survey was conducted with 302 midwives. The mean age of the respondents was 34.21 years (SD=9.71), and they reported an average practice of 10.08 years (DS=9.86). The questionnaire included some hypothesized predictors of acceptance, such as personal responsibility, self-efficacy at work, perceived utility, and easiness of use. The results showed that perceived usefulness and perceived easiness of use showed positive and significant effects on the intention to use the AI technology. Moreover, in the final model, personal responsibility showed a higher effect on perceived utility, while self-efficacy showed a higher effect on the easiness of use. These results confirmed some previous models of technology acceptance also for an AI-based tool aimed at interpreting the CTG. Moreover, it adds to the previous literature, emphasizing the role of clinicians’ perceived responsibility and self-efficacy in explaining the technology acceptance process.

The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms / D. Mazzoni, M.M. Pagin, R. Amadori, D. Surico, S. Triberti, C.I. Aquino, G. Pravettoni - In: Artificial Intelligence for Medicine : An Applied Reference for Methods and Applications / [a cura di] S. Ben- David, G. Curigliano, D. Koff, B. A. Jereczek-Fossa, D. La Torre, G. Pravettoni. - [s.l] : Elsevier, 2024. - ISBN 9780443136719. - pp. 73-80 [10.1016/B978-0-443-13671-9.00002-8]

The dual path of the technology acceptance model: An application of machine learning cardiotocography in delivery rooms

D. Mazzoni
Primo
;
S. Triberti;G. Pravettoni
Ultimo
2024

Abstract

Cardiotocography (CTG) is widely used for assessing fetal well-being, with the aim to reduce the risk of unnecessary and invasive obstetric interventions and to improve maternal-fetal outcomes. Artificial intelligence technologies could improve the efficacy of CTG; however, the acceptance of such technologies by clinicians is a prerequisite for their sustained adoption in clinical practice. In this chapter, we present a study aiming to investigate the factors associated with the acceptance of an AI-based tool aimed at interpreting the CTG by a group of midwives. An online survey was conducted with 302 midwives. The mean age of the respondents was 34.21 years (SD=9.71), and they reported an average practice of 10.08 years (DS=9.86). The questionnaire included some hypothesized predictors of acceptance, such as personal responsibility, self-efficacy at work, perceived utility, and easiness of use. The results showed that perceived usefulness and perceived easiness of use showed positive and significant effects on the intention to use the AI technology. Moreover, in the final model, personal responsibility showed a higher effect on perceived utility, while self-efficacy showed a higher effect on the easiness of use. These results confirmed some previous models of technology acceptance also for an AI-based tool aimed at interpreting the CTG. Moreover, it adds to the previous literature, emphasizing the role of clinicians’ perceived responsibility and self-efficacy in explaining the technology acceptance process.
Cardiotocography; Fetal well-being; Midwives; Responsibility; Technology acceptance
Settore PSIC-03/A - Psicologia sociale
Settore MEDS-21/A - Ginecologia e ostetricia
2024
Book Part (author)
File in questo prodotto:
Non ci sono file associati a questo prodotto.
Pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1119955
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 1
  • ???jsp.display-item.citation.isi??? ND
social impact