Digital twins and artificial intelligence are reshaping the future of individualised healthcare. Each offers distinct advantages: digital twins rely on mechanistic models grounded in physics, while AI provides speed and flexibility through data-driven learning. However, their limitations—digital twins being computationally intensive and AI models often lacking transparency—have led to the emergence of a new hybrid approach. Termed “Big AI”, this integration enhances clinical decision-making and drug discovery by fusing the interpretability of physics-based models with the adaptability of AI. The result is a more accurate, scalable and trustworthy foundation for predictive medicine.
Digital Twins and the Role of Physics-Based Models
Digital twins offer a virtual representation of a person’s physiology, capturing data across multiple scales—from DNA to organs. These models simulate how an individual’s body responds to various scenarios, enabling precise predictions about health outcomes. Rooted in physical laws, digital twins use equations to reflect biological processes like fluid dynamics and chemical interactions. Their predictions are not derived from population averages but from individualised inputs, making them inherently personalised.
The strength of digital twins lies in their capacity for rigorous validation. Through uncertainty quantification, these models provide measurable confidence levels for each prediction. This scientific robustness makes them suitable for high-stakes applications, such as simulating cardiac or immune system responses. However, their reliance on comprehensive biological knowledge and significant computing power can restrict speed and scalability. Despite these constraints, digital twins represent a foundational step toward highly individualised healthcare.
AI Models: Speed, Scale and Limitations
AI models, particularly those based on machine learning, detect patterns in large datasets to make fast predictions. Their capacity to generate results without needing full mechanistic understanding makes them well suited for scenarios where information is incomplete or noisy. This quality has supported the adoption of AI across clinical settings, especially in diagnostics, decision support and patient monitoring.
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Yet AI models present important limitations. Their predictions often lack explainability and transparency. In many cases, clinicians cannot trace how an AI system arrived at a decision, which raises questions about accountability and trust. Moreover, AI models are vulnerable to data bias. If the training data is skewed—by geography, ethnicity or clinical setting—predictions may not generalise well to broader populations. The models also struggle with extrapolation, and efforts to quantify prediction uncertainty remain largely immature. Without mechanistic grounding, AI systems may offer output without understanding, which risks undermining scientific reliability in healthcare contexts.
Big AI: Integrating Strengths for Predictive Care
Big AI seeks to address these challenges by combining the rigour of digital twins with the efficiency of AI. This hybrid model leverages physics-based simulations for interpretability and accuracy, while incorporating machine learning to enhance speed and adaptability. Big AI maintains the explanatory power of physical models while allowing AI to fill gaps in knowledge or data. In doing so, it supports more dynamic, robust and personalised healthcare applications.
In cardiac safety testing, Big AI trains machine learning models on 3D simulations of drug effects on virtual human populations. This not only accelerates drug screening but improves reliability. Other use cases include modelling physiology, guiding neurosurgical planning and predicting cardiovascular disease. By creating a loop where AI proposes candidates and physics-based models assess their viability, Big AI improves both suggestion quality and scientific validation. This synergy is especially valuable in drug discovery, where traditional pipelines are slow and costly. AI can rapidly scan chemical libraries, while physics-based models verify molecular interactions, significantly reducing development timelines.
The integration of Big AI aligns with evolving regulatory frameworks. Authorities such as the US FDA and European Medicines Agency have acknowledged the promise of computational models in drug and device development. These endorsements support broader adoption and signal a growing recognition of the role hybrid models can play in improving both innovation and safety.
The future of individualised healthcare depends on models that are both scientifically rigorous and operationally scalable. Digital twins offer accuracy and explainability, while AI brings speed and flexibility. On their own, each presents constraints. Together, they form Big AI—a hybrid framework capable of transforming diagnostics, therapeutics and preventive care. By uniting mechanistic modelling with machine learning, Big AI lays the groundwork for predictive medicine that is truly personalised, accountable and adaptive. Its applications in cardiac modelling, neurosurgery and drug discovery demonstrate the tangible benefits of this convergence, driving a new era in healthcare delivery.
Source: npj digital medicine
Image Credit: iStock
