###### Abstract  
The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows. generative artificial intelligence; Gen AI; patient care; personalized patient care; large language models (LLMs); opportunities and challenges of using generative AI; clinical decision support +
Footnote †: journal: Applied sciences  
0000-0001-9993-3227]M. M. M.  
leveraging advanced algorithms to create tailored solutions for individual patient needs. This innovative approach enables healthcare providers to analyze vast datasets, including patient health records, genetic information, and treatment histories, to generate highly customized care plans. By synthesizing this data, Gen AI can identify optimal treatment options, dosage regimens, and therapeutic interventions tailored to each patient's unique characteristics and medical history [5; 6; 7]. The rapid evolution of AI has fundamentally reshaped numerous industries, with healthcare being one of the most significant beneficiaries. AI-driven innovations are revolutionizing how patient data is processed, analyzed, and applied, enabling unprecedented capabilities in disease diagnosis, prognosis, and personalized care planning. A particular focus has emerged on personalized patient care, which involves tailoring medical interventions to individual patient profiles based on a comprehensive analysis of genetic, phenotypic, environmental, and lifestyle data. This patient-centric approach is at the forefront of precision medicine and has been enhanced by recent advances in AI technologies [8]. Generative AI, a subset of AI, has shown extraordinary promise in transforming healthcare through its ability to create new, meaningful data representations. Unlike traditional AI models, which primarily focus on classification or prediction tasks, generative AI is designed to generate new content or simulate realistic data. The two primary architectures underpinning Gen AI in healthcare are generative adversarial networks (GANs) and transformer-based models, such as GPTs (generative pre-trained transformers). These models have unique characteristics and applications that make them suitable for enhancing personalized patient care [9]. Generative AI offers a significant advantage in creating personalized treatment care plans by simultaneously analyzing a wide range of factors, including patient demographics, existing comorbidities, treatment preferences, and responses to previous therapies.