## 5 Conclusions, Key Issues, and Concerns
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Area** & **Studies in This Area** & **Issue** & **Concern** \\ \hline Data privacy and security & [10; 21; 34; 35; 36] & Generative AI often requires access to sensitive patient data. Ensuring the privacy and security of this data is challenging, especially given the stringent regulations in healthcare (e.g., HIPAA). & Unauthorized access, data breaches, and the potential for re-identification of anonymized data pose serious privacy risks. \\ \hline Ethical regulations & [2; 22; 26; 29] & The ethical implications of Gen AI, including biases in training data and decision-making processes, raise concerns about fairness and equitable treatment of diverse patient populations. & Ethical considerations regarding informed consent, algorithmic transparency, and the potential for unintended consequences in patient care. \\ \hline Model interpretability & [2; 9; 23; 29] & Many Gen AI models, especially complex deep learning architectures, lack interpretability. Understanding how the model arrives at a decision is challenging, impacting the trust healthcare professionals and patients place in the technology. \\ \hline Bias and fairness & [37; 38; 39; 40; 41; 42] & Generative AI models can inherit biases present in training data, leading to biased outcomes in healthcare decision-making. & Biases may disproportionately affect certain demographic groups, potentially exacerbating existing healthcare disparities. \\ \hline End-user (patient) trust and confidence & [41] & Patients may be skeptical or hesitant to trust AI-driven technologies with their healthcare data and decisions, particularly if they perceive a lack of transparency or understanding of how the technology works. \\ \hline Clinical workflow integration & [1; 16; 27; 29; 30; 32; 42] & Integrating Gen AI models into existing clinical workflows is challenging. Healthcare professionals may face disruptions in their routines, and the technology may not seamlessly align with established practices. \\ \hline Resources and tools & [26; 29; 41] & Developing and maintaining Gen AI models, especially those that require large datasets and computational resources, can be resource-intensive. & Limited access to resources may hinder the implementation of Gen AI in healthcare, particularly in smaller or resource-constrained healthcare settings. \\ \hline \end{tabular}
\end{table} 
Table 3: Summarizing key issues and concerns related to the use of Gen AI in personalized patient care. Bias and fairness in Gen AI models are of key interest and applicable to the wider and broader application beyond health and well-being. Gen AI tools can audit datasets and models to detect and correct biases related to gender, race, socioeconomic status, or other demographic factors. These AI tools can also be used to continuously improve fairness in healthcare decisions. Interpretability of the model is critical, ensuring that healthcare professionals understand how AI-driven decisions are made, fostering trust and accountability [3; 17; 18; 31]. This research received no external funding. Authors [PERSON] and [PERSON] were employed by the company Orion Health. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.