# Artificial Intelligence to Reshape the Healthcare Ecosystem
## 3 AI Penetration in Baseline Healthcare Services  
Section 2 includes many bibliographic references that show several advances in the context of healthcare. The aim of this section is to show the versatility of the techniques presented and the potential of their joint use. For this purpose, we consider some baseline activities that characterize the healthcare and highlight the existing contributions to them by all the techniques presented in Section 2. The considered baseline activities are _diagnosis, patient treatment and management, clinical care and decision support, research and development, administration and management_, and _prevention and wellness_. For each baseline, Table 2 reports some selected examples of existing contributions in order to show the flexibility and adaptability of the AI techniques presented. The selected examples in the identified baselines are often overlapping, and some contributions can be considered in common between them. Furthermore, a good part of the contributions are characterized by a technology readiness level that is still insufficient \begin{table}
\begin{tabular}{p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**AI Method** & **Best-Ssuited Healthcare Tasks** & **Critical Limitations** & **Essential Requirements** & **Input Data** & **Available Technologies** & **Readiness Level** \\ \hline
**GNN** & Drug discovery, protein affinity prediction, modeling complex relationships in EHRs & Scalability issues, difficult interpretability of results, complex data preprocessing preprocessing & Need for graph-structured data, high-performance computing infrastructure, graph data & Molecular structures, Temporal Flow, Deep Graph Library, Graph Library, Graph Library, Graph data & TensorFlow, Deep Graph Library, Spektral, StarStellarGraph & Low to medium with rapid growth rate \\ \hline
**RNN** & Time-series analysis of health data (e.g., EHR), continuous patient monitoring, telemedicine, protein affinity prediction & Vanishing gradient, long training times, highly variable health status & Data preprocessing and alignment, high-quality data, significant computational resources & Time-series data, EHRs, Supervised Learning Systems, Keras & TensorFlow, Python, Keras & Medium \\ \hline
**Generative AI** & Clinical documentation, conversational agents, synthetic medical data generation & Possible misleading data, sequential concerns, patient concerns, ethical concerns & Reliable validation of results, high-quality training data, ethical & Clinical images, EHRs, omics data & BERT, GPT, StyleGAN, BioGPT, GPT-4 & Medium to high, with growing usage for synthetic data for synthetic data for training purposes \\ \hline
**Diffusion Models** & Image reconstruction, denoising of medical images, generation of high-resolution medical images, medical images, medical images, high-resolution medical images, learning data & High computational resources, quality leaks for initialization, learning data & Medical images (MRI, CT scans), noisy or incomplete training images & PyTorch, TensorFlow, Diffusers, NVIDIA Clara & Low, with rapid growth rate \\ \hline
**RL** & Personalized treatments, robotic surgery, support for clinical decision making & Slow training, complex experimental setup, ethical concerns, safety concerns, safety & Emulated environments, real-world feedback data, ethical compliance and safety assessment & Patient data for state model, wearable sensor data, treatment outcomes & TensorFlow, PyTorch, High \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._for clinical practice. However, they are sufficient to demonstrate how AI is penetrating and reshaping related activities. \begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Area** & **Deep Learning** & **GNN** & **RNN** & **Generative AI** & **Diffusion Models** & **Reinforcement Learning** \\ \hline
**Diagnosis** & Analysis of radiological images, such as X-rays or CT scans [35, 118, 119, 120, 121, 122].