BIOMATHEMATICS AND ARTIFICIAL INTELLIGENCE: MATHEMATICAL MODELING OF COMPLEX BIOLOGICAL SYSTEMS IN THE DIGITAL ERA

Abstract

This research explores the use of biomathematics and artificial intelligence (AI) in modelling complex biological systems in the digital era. A descriptive quantitative approach was used to evaluate perceptions of mathematical modelling, AI, hybrid models, and research challenges in biology. The sample consisted of 150 participants with backgrounds in relevant fields, including biology, biotechnology, bioinformatics, mathematics, computer science, AI, and health. Data collection was conducted using a structured questionnaire with a five-point Likert scale, and data analysis was performed using descriptive statistics. Results showed that 68% of respondents had research experience, and 32% did not. Most respondents identified biomathematics as an important interdisciplinary area at the intersection of biology and statistics, and of computing and data analysis (mean score: 4.31). AI for analyzing huge amounts of biological data was seen as very useful (mean score: 4.36). Biomathematics and AI were found to improve model accuracy (mean score: 4.27), while data quality was the main challenge (mean score: 4.33). The future convergence of mathematics, AI and biology was seen as most important (mean score: 4.38). The findings highlight the influence of digitalization on modern biology and health care. The study found that mathematical-AI models have applications in prediction, interpretation, simulation, and decision-making in biological research. Still, their use will depend on high-quality data, model transparency, ethical considerations, and interdisciplinary training.