Patient Privacy and Data Security

• Protecting patient privacy while utilizing data for AI development is a delicate balance
• Concerns include potential data breaches and unauthorized data access
• Data solidarity concept suggests patients may support data use for common good if done transparently
• European initiatives like GAIA-X cloud aim to create trusted environments for health AI development

Bias and Fairness

• AI systems can perpetuate or amplify biases present in training data
• A biased AI could lead to health disparities across demographic groups
• EU projects emphasize obtaining diverse, representative datasets for AI development
• The EU AI Act will require transparency about training data and bias mitigation strategies

Transparency and Informed Consent

• Patients have the right to know when algorithms are involved in their care
• EU guidelines advise informing patients when AI assists in diagnosis or treatment
• Informed consent processes are being updated to cover AI tools
• The principle of human oversight ensures AI doesn't make autonomous decisions

Liability and Accountability

• Unclear responsibility if AI causes errors creates accountability challenges
• Currently, the accountable person is usually the physician using the AI's advice
• The teamwork model promotes treating AI as a tool under clinical supervision
• Hospitals often have guidelines stating clinicians must validate AI outputs

Ethical Use of LLMs

• LLMs can produce incorrect but authoritative-sounding answers (hallucinations)
• Many hospitals establish ethics committees to evaluate LLM applications
• There's concern about balancing AI efficiency with maintaining human empathy
• European approach emphasizes AI augmenting rather than replacing human care