Gallagher et al. studied the personalized management of atrial fibrillation (AF) using conversational artificial intelligence. In the primary outcome analysis, 66 participants used the intervention and 19 patients were the control. Automated AI phone calls recognized speech and natural language, and this software was used to interact with patients, but an online survey was sent to patients if they could not be reached after three phone calls. Once concerning health outcomes were detected, patients were directed to clinical care. While the Atrial Fibrillation Effect on Quality-of-Life (AFEQT) questionnaire depicted no change between groups six months into testing, only the intervention group using the automated phone calls depicted an increase in AFEQT score. The theory behind this study promotes the research into making conversational AI more mainstream to support patients that need more follow ups due to chronic illness, potentially decreasing the incidence of degenerative conditions as well. Because evaluation length had no impact on overall health outcomes, future studies could also explore other modes of communication to best reach patients.