TY - JOUR
AU - O. Ogundare
AU - T. Owadokun
AU - T. Ogundare
AU - P. Ekpo
AU - H. L. Nguyen
AU - S. Bello
A1 - 
AB - Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometimes amplify. Regardless, AI is changing the way we reason towards solutions, especially at the frontier of public health applications where autonomous and co-pilot AI integrated systems are now rapidly adopted for mainstream use in both clinical and non-clinical settings. In this regard, we present empirical analysis of thematic concerns that affect patients within AI integrated healthcare systems and how the experience of care may be influenced by the degree of AI integration. Furthermore, we present a fairly rigorous mathematical model and adopt prevailing techniques in Machine Learning (ML) to develop models that utilize a patient's general information and responses to a survey to predict the degree of AI integration that will maximize their experience of care. We model the patient's experience of care as a continuous random variable on the open interval ([Formula: see text]) and refer to it as the AI Affinity Score which encapsulates the degree of AI integration that the patient prefers within a chosen healthcare system. We present descriptive statistics of the distribution of the survey responses over key demographic variables viz. Age, Gender, Level of Education as well as a summary of perceived attitudes towards AI integrated healthcare in these categories. We further present the results of statistical tests conducted to determine if the variance across distributions of AI Affinity Scores over the identified groups are statistically significant and further assess the behavior of any independent distribution of AI Affinity Scores using a Bayesian nonparametric model.
AD - Department of Information and Decision Sciences, California State University, San Bernardino, CA, USA. oluwatosin.ogundare@csusb.edu.; SAINTPHAREUX Research Group, Houston, TX, USA. oluwatosin.ogundare@csusb.edu.; SAINTPHAREUX Research Group, Houston, TX, USA.; Department of Psychiatry, Boston University School of Medicine, Boston, MA, USA.; Department of Computer Science, Cornell University, Ithaca, NY, USA.; National Economics University, Hanoi, Vietnam.
AN - 40595089
BT - Sci Rep
C5 - HIT & Telehealth
CP - 1
DA - Jul 1
DO - 10.1038/s41598-025-07581-7
DP - NLM
ET - 20250701
IS - 1
JF - Sci Rep
LA - eng
N2 - Healthcare is plagued with many problems that Artificial Intelligence (AI) can ameliorate or sometimes amplify. Regardless, AI is changing the way we reason towards solutions, especially at the frontier of public health applications where autonomous and co-pilot AI integrated systems are now rapidly adopted for mainstream use in both clinical and non-clinical settings. In this regard, we present empirical analysis of thematic concerns that affect patients within AI integrated healthcare systems and how the experience of care may be influenced by the degree of AI integration. Furthermore, we present a fairly rigorous mathematical model and adopt prevailing techniques in Machine Learning (ML) to develop models that utilize a patient's general information and responses to a survey to predict the degree of AI integration that will maximize their experience of care. We model the patient's experience of care as a continuous random variable on the open interval ([Formula: see text]) and refer to it as the AI Affinity Score which encapsulates the degree of AI integration that the patient prefers within a chosen healthcare system. We present descriptive statistics of the distribution of the survey responses over key demographic variables viz. Age, Gender, Level of Education as well as a summary of perceived attitudes towards AI integrated healthcare in these categories. We further present the results of statistical tests conducted to determine if the variance across distributions of AI Affinity Scores over the identified groups are statistically significant and further assess the behavior of any independent distribution of AI Affinity Scores using a Bayesian nonparametric model.
PY - 2025
SN - 2045-2322
SP - 21879
ST - Integrated artificial intelligence in healthcare and the patient's experience of care
T1 - Integrated artificial intelligence in healthcare and the patient's experience of care
T2 - Sci Rep
TI - Integrated artificial intelligence in healthcare and the patient's experience of care
U1 - HIT & Telehealth
U3 - 10.1038/s41598-025-07581-7
VL - 15
VO - 2045-2322
Y1 - 2025
ER -