# Artificial Intelligence Applications in Smart Healthcare: A Survey
## 5 Challenges and Existing Solutions
### Ethical AI Design
#### 5.7.2 Existing Solutions  
To address accountability issues, clear frameworks for assigning responsibility in AI-driven healthcare systems need to be developed. This includes defining the roles and responsibilities of all stakeholders involved, such as developers, healthcare providers, and users. Developing comprehensive accountability guidelines can help in establishing who should be held responsible for errors or system failures, and mechanisms should be put in place to address such issues effectively [138].  
Several strategies can be employed to mitigate AI biases. First, using diverse and representative datasets during AI development is crucial. For example, incorporating datasets with images from various skin types and demographics in diagnosing skin diseases helps prevent bias and improves accuracy. The MelanomaAI project demonstrates how diverse datasets enhance diagnostic performance and reduce disparities. Second, conducting regular audits to assess AI fairness, accuracy, and performance is essential [141]. Tools like IBM's AI Fairness 360 provide frameworks for evaluating AI models and addressingidentified biases. Third, validating AI models across different patient populations and conditions is important for confirming their effectiveness. For instance, the HealthMap project shows how validating AI models in various regions and conditions maintains robustness and accuracy [142]. Lastly, educating clinicians and patients about inherent AI biases through training programs and workshops helps manage AI tools effectively and recognize potential biases.  
Several strategies can be employed to effectively address AI biases, each with pros and cons. First, using diverse and representative datasets is crucial for reducing bias and improving accuracy, though this approach can be resource-intensive and it can be challenging to ensure comprehensiveness. Second, conducting regular audits helps identify and address biases, ensuring fairness; however, audits can be costly and require continuous updates to remain relevant. Third, validating AI models across different populations confirms their effectiveness in various scenarios, but this process can be complex and time-consuming. Finally, educating clinicians and patients about AI biases raises awareness and improves the management of AI tools, though it requires significant investment in training and education [143]. In conclusion, while each method contributes valuable insights for mitigating AI bias, it also presents challenges that need to be managed. Combining these methods' strengths while addressing their limitations can provide a comprehensive approach to designing ethical AI systems.  
To protect sensitive information, practical measures include encrypting and anonymizing data. Implementing end-to-end encryption in health data systems and using techniques like anonymization and differential privacy in research data are effective methods. For instance, Google Health uses advanced encryption methods to safeguard patient data while deriving valuable insights. Internal and external audits should track data sourcing, access, and usage. Techniques such as federated machine learning, data aggregation, and privacy-preserving methods like differential privacy help maintain transparency and security. Federated learning allows researchers to collaborate on medical AI models while keeping patient data decentralized and secure, ensuring compliance with privacy regulations [144].