## 3 Transformative Impact and Ethical Implications of AI in Healthcare
### Algorithmic Fairness, Transparency, and Trust in AI-Driven Healthcare  
In AI-driven healthcare, a multifaceted approach is vital to ensure algorithmic fairness, transparency, and trust, which are pivotal for equitable and ethical healthcare delivery. Mitigating algorithmic bias is crucial for equitable patient treatment and involves analyzing and adjusting AI models to reflect diverse patient demographics. Including diverse datasets in AI model training is essential for minimizing biases and promoting equitable health outcomes [8; 22]. Additionally, AI fairness and bias detection tools are essential for analyzing and readjusting algorithms to ensure fair treatment across patient demographics. Developing transparent and understandable AI systems is critical to building trust among healthcare stakeholders, addressing the nature of AI algorithms' black box' nature, and improving the understanding of AI processes [2; 23]. Explainable AI (XAI) plays a foundational role in making AI decisions and processes more comprehensible, thus enhancing transparency and fostering trust. This comprehensive methodology ensures that AI applications cater effectively to the diverse needs of patient populations and establishes a trust-based relationship between AI applications and healthcare users, ensuring AI's role remains informative and supportive [13]. Incorporating fairness and explanation in AI-informed healthcare decision-making, as noted by [24], is paramount to ensuring ethical AI integration. AI systems must refrain from perpetuating or introducing new forms of discrimination, especially given the diversity of patient populations. Detecting and mitigating biases in AI models is essential for equitable treatment across all patient groups. The development of AI models that are both fair and explainable allows healthcare providers and patients to understand and trust AI decisions, reinforcing patient autonomy and agency. Moreover, integrating AI into healthcare demands attention to patient dignity and respect. The authors of [25] explore how AI perceptions in healthcare are tied to how these systems uphold human dignity, highlighting the need for AI applications to respect patient values. This aligns with broader ethical considerations in healthcare AI, particularly concerning patient autonomy, data privacy, and algorithmic bias. In the broader context of AI decision-making, [26] points out the risk of AI simplifying patient narratives, emphasizing the need for AI systems to augment personalized care while maintaining essential human elements for adequate healthcare. The authors of [27] emphasize patient engagement, advocating for informed and active involvement in AI-related healthcare decisions. Addressing algorithmic discrimination, enhancing transparency, and fostering trust in AI applications are crucial for ethical and patient-centered healthcare. Adopting frameworks that emphasize fairness, transparency, and explanation, alongside a focus on human dignity and patient engagement, AI in healthcare can achieve more equitable and trustworthy outcomes, aligning technological innovation with ethical considerations. Integrating the AI-driven Internet of Things (AIIoT) in healthcare presents unique challenges and opportunities for ensuring algorithmic fairness, transparency, and trust. Mitigating bias in AI algorithms is paramount, especially considering the diverse nature of patient populations and the varied nature of diseases and conditions. The research by [21] highlights the rapid adoption of AIIoT in healthcare, significantly enhancing the quality and effectiveness of healthcare services. This adoption is particularly valuable for chronic conditions, elderly care, and continuous monitoring. Incorporating diverse data sets into AI model training minimizes biases and promotes equitable health outcomes. The use of AIIoT, including remote monitoring and communication, significantly enhances patient treatments.