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Artificial intelligence

Artificial intelligence (AI) and big data have the potential to transform health and medicine. They can support more accurate diagnoses, help clinicians make decisions and improve the overall quality of care. At the same time, it is essential to ensure these technologies promote fair and patient-centred care, and do not reinforce — and ideally help to reduce — existing biases and inequalities in genomics and healthcare. While the benefits can be significant, so can the risks. Many of these risks are difficult to measure or predict in advance. As big data and AI will inevitably shape the future of healthcare, their ethical and societal implications need to be discussed openly with the public.

AI in healthcare

In medicine, AI systems can, for example, detect patterns (such as tumours) in medical images or generate summaries of conversations between doctors and patients. These tools may support faster and more reliable diagnoses and help clinicians manage increasing amounts of information.

However, a major challenge is the so-called black box nature of many AI systems: it is often unclear how they arrive at their conclusions. This raises important questions about accuracy, transparency and accountability. If an AI-supported decision leads to an error, who is responsible — the developer, the clinician, or someone else?

To maintain trust in AI-supported healthcare, human oversight and professional judgement must remain essential.

Bias, fairness and accountability

In principle, big data and AI could make healthcare more efficient, cost-effective and accessible, including for underserved populations. In practice, however, AI systems can reflect existing biases if they are trained on incomplete or unrepresentative datasets (for example, if data from certain population groups are missing or underrepresented). As a result, AI outputs may be less accurate or less useful for these groups, potentially reinforcing existing inequalities. While there are ways to reduce bias and use AI to support fairer healthcare, doing so requires careful ethical reflection and ongoing societal debate.

These conversations are essential to ensure that big data and AI genuinely contribute to better health outcomes for everyone.