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Advances in artificial intelligence (AI) are transforming the landscape of mathematical modelling in areas including physics, biology, and chemistry. Research suggests that ChatGPT, Gemini, and other AI tools can change the way researchers use simulation and modeling for complex phenomena by helping to produce models faster with less computational complexity and real-time insights. Here, we introduce a novel framework for building mathematical models of life sciences using AI tools for applications in disease dynamics and ecological systems. The approach integrates AI tools into the process for a hybrid model that combines initial model formulations based on AI-assisted discussions and refinements based on expert validation of AI-generated output. To give an example, if we are interested in modelling disease outbreaks, AI platforms such as ChatGPT or Gemini can instantly build a simple susceptible-infectious-recovered (SIR) model. This also helps with high dataset processing and making parameter suggestions based on real-time data, which in turn helps in the dynamic adaptation of models to changing data (e.g. transmission rates or intervention strategies). Likewise, in ecological modelling, AI tools can aid in the generation of predator-prey models that consider these complex interactions, such as habitat fragmentation or reserved zones and then suggest parameter sensitivities based on observed trends. These abilities make the future of AI-based mathematical modelling especially exciting, as they will further decrease the time that is traditionally spent by researchers on manually defining models and allow them to focus on result interpretation and strategic decision-making. With the rapidly changing advances in AI tools, incorporating some new capabilities and developments in the mathematical modelling procedure may allow for unprecedented improvements in predictive performance, model flexibility and interdisciplinary investigations. Further research and real-world efforts with this approach are needed to determine if AI tools can improve the cost-effectiveness and affordability of mathematical modelling in many fields of science.
Abosaooda et al. (Thu,) studied this question.