A medical field that is increasingly benefiting from Artificial Intelligence applications is Gynecology and Obstetrics. In previous work, we exposed that Artificial Intelligence (AI) technology and obstetric control by physicians can enhance pregnancy health, leading to better pregnancy outcomes and overall better experience, also reducing any possible long-term effects that can be produced by complications. This work presents a data collection methodology for responsible AI in Health and a case study in the pregnancy domain. It is a qualitative descriptive study on the preferences and expectations expressed by pregnant women regarding responsible AI and affective computing. A 41-items structured interview was distributed among 150 pregnant pa- tients attending prenatal care at Hospital Virgen del Rocío and the Clinic Victoria Rey (Seville, Spain) during the months of October and November 2020. A substantial interest in intelligent pregnancy solutions among pregnant women has been revealed in this study. Participants with a lower level of interest reported privacy concerns and lack of trust towards AI solutions. Re- garding affective computing based intelligent solutions specifically, most participants reported positively and no significant difference was found between women having a healthy or a high risk pregnancy on this matter. Our findings also suggest that a high demand of personalized intelligent solutions exists among participants. On the topic of sharing pregnancy data with the healthcare provider in favor of scientific research, pregnant women assisting public health- care services were found to be more likely to share their data when the provider was a public healthcare system rather than a private entity. Pregnant women who are interested in using an AI pregnancy application share a strong idea that it needs to be responsible, trustworthy, useful, and safe. Likewise, we found that pregnant women would change their mind about their concerns and they would feel more confident if the intelligent solution gives explanations about the system decisions and recommendations, as XAI approach promotes.