Growing AI adoption in life sciences is raising ethical and governance questions the sector can no longer postpone. With the European AI Act in force and increasingly stringent global regulations, companies must develop robust, proactive governance frameworks.
The AI Act and Life Sciences
The European AI Act classifies many life sciences AI applications as "high-risk": medical diagnosis systems, AI-based medical devices, and clinical decision support systems. This entails specific obligations: detailed technical documentation, risk management, rigorous data governance, human oversight, and end-user transparency. Non-compliant companies face fines up to 6% of global revenue.
Bias and fairness in clinical data
Historical clinical data reflects systemic inequalities: ethnic minority underrepresentation in trials, gender differences in diagnosis, and socioeconomic biases in care access. AI models trained on this data risk amplifying these inequalities. Effective governance must include regular bias audits and fairness-aware machine learning.
Explainability and transparency
In life sciences, AI model explainability isn't a nice-to-have but a regulatory and ethical requirement. Doctors must understand AI recommendations, patients have the right to know how decisions are made, and regulators must be able to audit decision processes.
Privacy and sensitive data
Health data is among the most sensitive. The governance framework must ensure GDPR compliance, implement privacy-preserving AI techniques (federated learning, differential privacy, synthetic data), and define clear informed consent processes.
Adalot's AI governance framework for Life Sciences
Adalot has developed an AI governance framework specific to life sciences, based on five principles: beneficence, non-maleficence, autonomy, justice, and accountability. This framework guides our feasibility studies and technology assessments, ensuring life sciences AI innovation is not only effective but also ethical and sustainable.