Ogundare Oluwatosin, Owadokun Tolu, Ogundare Temitope, Ekpo Promise, Nguyen Ha Linh, Bello Stephen
Department of Information and Decision Sciences, California State University, San Bernardino, CA, USA.
SAINTPHAREUX Research Group, Houston, TX, USA.
Sci Rep. 2025 Jul 1;15(1):21879. doi: 10.1038/s41598-025-07581-7.
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.
医疗保健领域存在许多人工智能（AI）可以改善或有时会放大的问题。尽管如此，人工智能正在改变我们寻求解决方案的方式，特别是在公共卫生应用的前沿领域，自主和辅助人工智能集成系统现在正迅速被临床和非临床环境中的主流应用所采用。在这方面，我们对影响人工智能集成医疗系统中患者的主题问题进行实证分析，以及护理体验如何受到人工智能集成程度的影响。此外，我们提出了一个相当严谨的数学模型，并采用机器学习（ML）中的主流技术来开发模型，这些模型利用患者的一般信息和对调查的回答来预测将使他们的护理体验最大化的人工智能集成程度。我们将患者的护理体验建模为开区间（[公式：见文本]）上的连续随机变量，并将其称为人工智能亲和度得分，该得分概括了患者在所选医疗系统中偏好的人工智能集成程度。我们展示了调查回复在关键人口统计学变量（即年龄、性别、教育水平）上的分布的描述性统计数据，以及这些类别中对人工智能集成医疗保健的感知态度的总结。我们进一步展示了为确定人工智能亲和度得分在已识别组上的分布方差是否具有统计学意义而进行的统计测试结果，并使用贝叶斯非参数模型进一步评估人工智能亲和度得分的任何独立分布的行为。
