## 3 Review of Generative Artificial Intelligence in Patient Care Applications  
### Recent Advancements in Generative AI in Patient Care  
Gen AI, as a specific subset or application of AI, focuses on generating new information or data that are similar to its training examples. Gen AI techniques often involve training \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Year of Publication** & & & \\
**(2021–2024)** & **Reference(s)** & **Area of Application** & **The Ratio (\%)** \\ \hline
2024 & [18] & Clinical decision support & 1 (7.7\%) \\  & & Personalized treatment plans, & \\
2023 & [2; 3; 4; 7; 8; 17; 19; 20; 21; 22; 23] & digital pathology, natural language processing, clinical decision support & 11 (84.6\%) \\
2021 & [16] & Clinical decision support & 1 (7.7\%) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Categorization of the selected articles. Figure 1: The article selection process. deep neurula network models on large datasets and then generating new content, such as medical notes, images, or sound, based on the patterns and structures learned from the training data. While AI models cover a broader field of applications by incorporating various ML techniques for simulating human intelligence, the Gen AI models can create new, original content by extrapolating from the patterns they have learned, sometimes producing outputs that are novel and different from human perception. Therefore, Gen AI holds considerable potential to transform patient care across various healthcare domains. Studies [8; 26; 27; 28; 29; 30; 31] included in this review underscore several healthcare domains where the role of Gen AI in enhancing patient care has garnered significant interest and demonstrated promising results, as outlined below:  
1. Personalized treatment plans or precision medicine: Gen AI can analyze a patient's medical history, genetic information, and other relevant data to generate personalized treatment plans. This ensures treatments are tailored to individual patient characteristics, improving efficacy and minimizing side effects. A study by [PERSON] et al. [2] examined how Gen AI produces factually precise and contextually significant structured-discussion feedback, showing that Gen AI algorithms can be deployed to support laboratory diagnostics, public health outbreak management, and clinical decision support tools [2; 3; 4];
2. Automated tasks and documentation: Natural language processing capabilities of the Gen AI models can help analyze unstructured clinical notes and medical literature to extract valuable insights. This helps healthcare professionals stay informed about the latest research and make evidence-based decisions, thus releasing clinician time from these trivial tasks and letting the clinicians focus on higher-level tasks such as decision-making or direct patient interactions [5; 6; 7; 8; 9];
3. Early disease detection using wearable and IoT devices: Generative AI can analyze diverse data sources, including electronic health records, wearable device data, and patient-reported symptoms, in order to identify patterns indicative of early disease onset. This enables early intervention and improved outcomes. The Internet of Things (or Internet-connected things) has attracted many applications and sectors for producing results efficiently, and these devices are generating a lot of data (vast data) which can be used for further prediction/forecast by Gen AI models [10; 11; 12]. Moreover, safeguarding the 'false positives' through the robust validation process is also key for enabling PPCPs;
4. Clinical decision support (CDS): Generative AI can assist healthcare professionals by analyzing complex patient data and recommending optimal treatment options based on the latest medical research and patient-specific factors. This supports more informed decision-making, using NLP, contextual understanding, and free-text analysis [5; 10; 11; 12].