Digital Biology
When Trusted AI enters life sciences.
Life science is one of the most complex, most rigorous, lowest-error-tolerance fields in the world. Here, AI must not only understand data — it must be explainable, auditable and governable.
If AI can be trusted in life science, the same capability can serve enterprise, government, cities and other high-trust industries.
Why digital biology matters
Living systems have vast numbers of variables, complex relationships and very high decision costs — any wrong judgment can have serious consequences.
So digital biology needs AI that not only understands data but is explainable, auditable and governable. That is exactly the value of Trusted AI.
- Where did the data come from?
- Why did the AI reach this conclusion?
- Can it be reviewed and verified?
- Who is accountable for the final decision?
Entrust's four capabilities, brought into life science
Understand complex biological signals — find patterns in complex data via plasma multi-omics and explainable AI.
- Biomarker identification
- Risk-scoring models
- Disease-signature discovery
- Multi-omics correlation
Accumulate scientific memory — every experiment, sample and result becomes the basis for continuously improving models.
- Multi-omics database
- Research knowledge accrual
- Long-term sample accumulation
- Continuous model improvement
Keep the medical boundary — AI advises, clinicians decide.
- Clinicians always in the decision loop
- Human review on high-risk
- Traceable decision process
- Results can be verified
Safe deployment — via an AI Appliance and in-hospital deployment, AI moves from the lab to real scenarios.
- On-device inference
- LIS / HIS integration
- Private deployment
- Data stays in the hospital
AI supports clinicians.
AI does not replace clinicians.
Decision provenance in healthcare
In life science, the result alone isn't enough. Organizations must know: where the data came from, why the AI advised it, how the clinician reviewed it, and how the final decision was formed.
With Decision Provenance, key decision points can be recorded, reviewed and traced — a foundation for future governance and compliance.
Clinical validation
3000+
Lung-nodule samples
92%
Sensitivity
90%
Specificity
82%
Nodule differentiation
These figures are reported by BAIRI clinical validation projects, shown only to illustrate digital-biology research and validation, and do not constitute medical claims or diagnostic/treatment advice by Entrust BCT.
Future research directions
Virtual Cell
Computable virtual-cell models built with AI.
Digital Twin
Digital twins of disease and human systems.
Longevity Intelligence
Exploring healthspan and longevity science.
Computational Biology
Deepening the fusion of AI and life science.
The above are research directions and do not constitute clinical product claims.
Why this matters for Entrust
Enterprise AI, government AI and city AI are already complex; life science is more so.
If Trusted AI can be validated in digital biology, the same capability system can serve many more high-trust industries.
Digital Biology isn't just one industry direction — it's proof of the depth and portability of Entrust's Trusted-AI capability.