Individual innovativeness levels and levels of medical artificial intelligence readiness among nursing students: a cross-sectional and correlational study

Aim This study, aimed to determine the individual innovativeness levels of nursing students and their readiness levels for medical artificial intelligence and the relationship between these two variables. Background A healthcare team with innovative personality traits is essential for the effective use of artificial intelligence in healthcare. It is important to determine the perspectives of nursing students, who are among the most crowded members of the team and whom we define as the nurses of the future, in this direction. Design The research was designed as descriptive and correlational. Method The research data were collected by the researcher between March 1 and May 1, 2023. The study included 1st, 2nd, 3rd and 4th year nursing students studying at two universities in Anatolia. Data were collected with a reliable online survey method. The sample selection procedure was based on the sampling of the known population method. The study was conducted with 781 nursing students using a cross-sectional and correlational study method. The data were collected using the Personal Information Form, Individual Innovativeness Scale and Medical Artificial Intelligence Readiness Scale. The conformity of the data to the normal distribution was made by using skewness and kurtosis values and the range of + 2 and − 2 was taken as reference. Significance level p

Machine learning in biological research: key algorithms, applications, and future directions

Machine learning is a robust framework to analyze questions using complex data in a variety of fields. We present definitions and recent applications of four key machine learning methods and discuss their advantages and challenges in biological research. Through a set of systematically selected case studies, we highlight how machine learning models have been used in a range of applications, including phylogenomics, disease prediction, and host taxonomy prediction. We identify additional potential areas of integration of machine learning into questions with biological relevance. This intersection can be further enhanced through collaboration and innovation on parallelization, interpretability, and preprocessing.