1. Data Privacy and Security: It is crucial that patient information and records are protected from leaking to the wrong people. These include necessary measures such as strong encryption layers, access controls, compliance with existing and applicable legislation and laws, including the Health Insurance Portability and Accountability Act (HIPAA), the General Regulation on data protection (GDPR) and others.
2. Bias and fairness: Machine learning models are known to include the fairness or political biases of the individuals who provided the models with their training data. To address this risk, organizations should incorporate fairness-aware algorithms where appropriate and seek to conduct regular bias assessments.
3. Interpretability: Referrals must be made by the health professionals who have a basic level of understanding of how the AI system works and a level of confidence in the decisions it makes. Make ML models fully interpretable in order to provide understandable explanations for the predictions necessary to accept the model’s results.
4. Regulatory Compliance: Adhere to legal guidelines and best practices for the deployment and use of artificial intelligence in the healthcare industry. Before deploying models, it is important to ensure that they are properly verified and validated by the relevant authorities.
