arXiv:2606.05925v1 Announce Type: new Abstract: A central goal of biomedicine is to understand, predict and ultimately control the dynamic mechanisms by which biological systems respond to perturbations, disease progression and therapeutic intervention. Although foundation models and large language models have accelerated biomedical data interpretation, most current systems remain focused on static pattern recognition rather than prospective simulation of biological futures. Here we propose biomedical world models as a paradigm for AI-driven discovery. These models learn latent representations
The proliferation of foundation models and large language models in biomedicine is naturally leading to aspirations for more predictive, dynamic AI systems capable of simulating biological outcomes.
This represents a significant conceptual leap for AI in biomedicine, moving from static data interpretation to prospective simulation, which could accelerate drug discovery and personalized medicine.
The focus of biomedical AI development is shifting from pattern recognition towards building dynamic, predictive 'world models' that can simulate biological responses and disease progression.
- · AI-driven drug discovery platforms
- · Biomedical research institutions
- · Pharmaceutical companies
- · AI model developers
- · Traditional drug development methodologies
- · Biotech companies solely focused on static data analysis
AI systems will become capable of simulating biological systems and predicting disease trajectories with unprecedented accuracy.
This will drastically reduce the time and cost of drug discovery, leading to a new era of personalized and preventative medicine.
The ability to model biological futures could also lead to advancements in synthetic biology and bio-engineering beyond current capabilities.
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Read at arXiv cs.AI