Table 2.

How artificial intelligence (AI) can integrate digital twins (DTs) with biological twins (BTs).

Complex Data Analysis	AI Function
In vitro models, such as cell cultures or organoids, generate vast amounts of data (e.g., images, genomic, proteomic, and metabolomic data).	AI can process these data in real time, identifying complex patterns and relationships that may not be evident through traditional analyses.
Personalized Predictions	AI Function
The digital twin is fed by multimodal data, allowing for simulations of disease progression and predictions of how the patient will respond to various treatments.	AI can combine data from in vitro models with patient clinical data, such as blood tests, diagnostic images, and genomic information.
Treatment Optimization	AI Function
Integrating information from in vitro models with simulations in the digital twin reduces risks and improves treatment effectiveness.	AI can virtually test different therapies and dosages before applying them to the patient.
Dynamic Updates	AI Function
Continuous updates on the patient’s health status are provided as well as personalized management of treatment, such as adjusting therapies.	AI enables the digital twin to continuously update itself with new data from both the patient and in vitro models.
Accelerate Discovery	AI Function
The discovery of therapeutic targets goes hand in hand with the new knowledge gained on solid tumors thanks to the sophisticated technologies available today.	AI can speed up drug discovery processes and treatment optimization by running large-scale simulations and rapidly integrating data from in vitro models.