## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Ethical Technologies as Cornerstones in Healthcare AI  
Integrating blockchain, Federated Learning (FL), Homomorphic Encryption, and Differential Privacy into healthcare AI systems is a multifaceted endeavor that addresses the pressing need for ethical standards and the protection of patient rights in the rapidly evolving field of healthcare technology. Collectively, these technologies form a robust framework for managing the challenges associated with the privacy and security of sensitive healthcare data while fostering innovation and efficiency in healthcare delivery. Blockchain technology stands out for its unique attributes of decentralization, immutability, and transparency, which are particularly beneficial in the context of healthcare AI systems. By creating a secure and tamper-proof ledger for medical data transactions, blockchain technology ensures the integrity and traceability of patient data, which is paramount in clinical settings. For instance, blockchain can be used to securely manage electronic health records (EHRs), providing a reliable source of patient data for healthcare professionals and AI algorithms while preventing unauthorized access and data breaches [19]. Federated Learning (FL) represents a paradigm shift in data analysis and machine learning, particularly within the healthcare sector. By enabling the training of AI models on decentralized data sources, FL ensures that sensitive patient information remains within the confines of the local institution, thereby preserving privacy. This approach is crucial for predicting hospital readmission risks and screening for medical conditions using wearable device data. FL's ability to integrate data from various sources without compromising privacy is a testament to its potential to enhance the capabilities of healthcare AI systems. Homomorphic Encryption is a cryptographic technique that allows computations to be performed on encrypted data without the need to decrypt it first. Healthcare providers can analyze and process sensitive patient data while it remains encrypted, significantly reducing the risk of data exposure. Homomorphic Encryption in healthcare AI systems enables secure data utilization, ensuring patient confidentiality is maintained even during complex data analysis processes [15]. Differential Privacy is a technique that adds a certain amount of noise to data queries, making it difficult to identify individual records while allowing for accurate aggregate analysis. This approach is particularly relevant in healthcare, where patient data are susceptible. Differential Privacy ensures that the privacy of individual patients is not compromised when their data are used for research or to train AI models. By integrating Differential Privacy into healthcare AI systems, researchers and clinicians can gain valuable insights from large datasets without risking the exposure of personal health information, thus maintaining patients' trust and complying with stringent privacy regulations such as GDPR and HIPAA [21]. The Internet of Healthcare Things (IoHT) represents a significant advancement in healthcare data management, with devices such as smartwatches and wearable trackers collecting a wealth of health-related data. These data are invaluable for healthcare professionals to make informed decisions regarding disease treatment and management. However, the traditional centralized machine learning models face challenges related to data access restrictions, potential biases, and high resource demands. Integrating blockchain, FL, Homomorphic Encryption, and Differential Privacy within IoHT systems offers solutions to these challenges, ensuring that data are managed ethically and securely while enabling innovative healthcare solutions [15]. Despite the promise of FL, there are inherent chal lenges, such as the statistical diversity of data across clients and communication efficiency. Techniques like Agnostic Federated Learning and q-Fair Federated Learning have been proposed to address the issue of data heterogeneity.