## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### In-Depth Analysis of Data Integrity in AI Healthcare Applications  
The importance of maintaining data integrity and security in AI healthcare applications cannot be overstated. This is because it directly impacts patient safety and the effectiveness of healthcare delivery. Highlighting this [5] underscores the crucial nature of AI's impact on healthcare. Additionally, the authors of [7,8] emphasize the essential role of data integrity in ensuring reliable healthcare outcomes. They advocate for rigorous verification and validation mechanisms, which gain particular significance in precision-critical fields like ophthalmology, as noted in [30]. The importance of robust data management practices is further stressed by [31]. These practices include continuous monitoring for anomalies to uphold the integrity of health data. The seamless transition from robust data management to addressing patient data access and control challenges is crucial. These challenges, especially when data are private, underscore the need for robust regulatory frameworks and systemic oversight. This transition highlights the interdependence of data integrity and privacy, emphasizing how effective data management addresses access and control challenges. Moreover, the risks of reidentification in anonymized data require enhanced privacy and security measures to protect patient data against computational strategies capable of reidentifying individuals. This concern naturally leads us to the broader implications for patient trust and the ethical application of AI in healthcare, as argued by the authors [1,9,10].  
The evolving nature of large language models (LLMs) like GPT-4 underscores the importance of addressing the scale and complexity inherent in these systems to maintain data integrity. The reliance of these models on extensive datasets and their potential for complex output generation necessitate enhanced data privacy and security measures, as well as robust verification and validation mechanisms. This point relates to the need for transparent and responsible AI usage in healthcare, which is a crucial factor in maintaining public trust. Lastly, the importance of technologically facilitated recurrent informed consent is underscored, which also mentions the dynamic regulatory approach as essential for ongoing data integrity and security in healthcare AI applications. This approach is critical in adapting to the ever-evolving landscape of AI technology, ensuring that as AI systems become more complex, the mechanisms to safeguard data integrity and privacy evolve alongside them [1,27].  
The authors of [9] focus on the practicalities of ensuring data integrity in AI healthcare. It stresses the importance of maintaining the confidentiality and integrity of patient data, which is fundamental for developing reliable healthcare AI systems. The significant barrier to secure data access and usage at the commercial level is acknowledged. Privacy-Preserving Machine Learning (PPML) emerges as a key solution, enhancing data and machine learning security--a vital step in overcoming these barriers. Particularly in sensitive datasets, which are integral to developing life-saving treatments and making critical decisions, PPML methods allow for collaborative training of ML models without compromising private information's unique structure. This method safeguards patient data and facilitates the responsible development and application of AI in healthcare settings.