Artificial intelligence promises to revolutionise healthcare, from faster diagnostics to more precise treatment plans. But with great power comes great responsibility. AI systems in clinical settings must be fair, transparent, and worthy of trust. Otherwise, they risk perpetuating bias, undermining user confidence, or even placing lives at risk.

Why bias matters in healthcare AI

AI systems can encode and amplify inequities present in training data or algorithms. For example, pulse oximeters have been shown to overestimate oxygen saturation in patients with darker skin tones, putting such individuals at risk of missed hypoxia diagnosis. Studies also show that AI sepsis prediction models often fail when applied across different hospitals, as institutional and demographic differences induce bias, and broad adopters may overlook this risk. A recent study stresses the need for site-specific bias mitigation strategies, not just one-size-fits-all models.

Building trust through transparency and explainability

Opaque “black-box” models leave clinicians unsure whether they can rely on AI. This distrust has real consequences: in a UK study, healthcare professionals voiced concerns over accountability and bias when using AI-assisted decision-making tools.

Explainable AI (XAI) techniques like SHAP and LIME help by breaking down how decisions are made. These tools help clinicians understand what features influenced the AI’s prediction, promoting trust and enabling human oversight .

Some methodologies, like permutation-based feature importance algorithms, can go further by explaining how features contribute to fairness, not just accuracy. This level of insight is crucial in high-stakes areas like sepsis mortality prediction.

Regulatory landscape: The EU AI Act and beyond

Regulation is catching up. The EU AI Act, effective from 2024 and with most obligations fully enforced by mid-2025, classes most healthcare AI as high-risk, mandating transparency, documentation, and human oversight.

Yet, gaps remain. Experts note that the Act currently lacks enforceable transparency obligations for private healthcare providers and recommend mandatory Fundamental Rights Impact Assessments (FRIAs) for all high-risk AI systems.

Strategies to mitigate bias and promote fairness

In clinical contexts, bias mitigation must be baked into every stage, from dataset design to model deployment. This includes careful dataset auditing, demographic parity assessment, or leveraging threshold adjustment strategies to balance false-positive rates across groups.

For sepsis models, local calibration and “silent trial” deployments, where predictions are run in the background without affecting patient care, can identify bias gaps before a full-scale rollout.

Designing ethical clinical AI

AI in healthcare can dramatically improve outcomes, but only when developed ethically. Fairness, transparency, and compliance are not optional extras, they’re essential foundations for trustworthy and effective clinical AI.