The ability of AI to integrate data from various sources, as well as to predict results, contributes to more precise and personalized medicine"

Through the application of Artificial Intelligence (AI) in Clinical Research, it is possible to streamline the process of analyzing large medical data sets, allowing researchers to identify patterns, correlations and trends more efficiently. In addition, AI contributes to the personalization of medicine, thanks to the adaptation of treatments according to the individual characteristics of patients. In fact, new technologies not only optimize processes, but also open up new perspectives for addressing medical challenges and improving the quality of care.

For this reason, TECH Global University has created this program in which AI and biomedicine converge, providing professionals with a deep and practical understanding of the specific applications of this technology in the field of Clinical Research. Accordingly, the syllabus includes specialized modules such as computational simulation in biomedicine and advanced analysis of clinical data, which will enable graduates to acquire advanced skills in the application of AI in complex biomedical situations. In addition, the focus on ethics, regulations and legal considerations in the use of AI in the clinical setting will be addressed.

Likewise, the degree integrates cutting-edge technologies, such as genomic sequencing and biomedical image analysis, addressing emerging issues, such as sustainability in biomedical research and the management of large volumes of data. In this context, students will be equipped with the necessary skills to lead at the convergence of AI and Clinical Research.Master's Degree has designed a comprehensive program based on the innovative methodology of Relearning with the purpose of forging highly competent specialists in AI. This learning modality focuses on reiterating key concepts to consolidate an optimal understanding. Only an electronic device connected to the Internet will be required to access the contents at any time, eliminating the need for on-site attendance or to adhere to fixed schedules.

This program in Artificial Intelligence in Clinical Research is highly relevant on the current healthcare and technology scene”

This Master's Degree in Artificial Intelligence in Clinical Research contains the most complete and up-to-date scientific program on the market. Its most notable features are:

• The development of case studies presented by experts in Artificial Intelligence in Clinical Research
• The graphic, schematic and practical contents with which it is conceived provide scientific and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

You will explore the latest technologies and the most innovative applications of Artificial Intelligence in Clinical Research, using the best multimedia resources”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this specializing program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to prepare for real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

Thanks to this 100% online program, you will thoroughly analyze the essential principles of machine learning and its implementation in the analysis of clinical and biomedical data"

You will gain in-depth knowledge of the implementation of Big Data and machine learning techniques in Clinical Research. Enroll now!"

This program is carefully designed to merge the scientific rigor of clinical research with the disruptive innovations of Artificial Intelligence.  Its structure is based on specialized modules, from the interpretation of medical data to the development of predictive algorithms and the implementation of technological solutions in clinical environments. The content is a blend of theory and practice, covering the basics of AI and its specific application in the medical field. In this way, graduates will be prepared to lead advances in the personalization of treatments and the optimization of healthcare.

Benefit from a syllabus created by experts and top-quality content. Update your clinical practice thanks to TECH Euromed University!”

Module 1. Fundamentals of Artificial Intelligence

1.1. History of Artificial Intelligence

1.1.1. When Do We Start Talking About Artificial Intelligence?
1.1.2. References in Films
1.1.3. Importance of Artificial Intelligence
1.1.4. Technologies that Enable and Support Artificial Intelligence

1.2. Artificial Intelligence in Games

1.2.1. Game Theory
1.2.2. Minimax and Alpha-Beta Pruning
1.2.3. Simulation: Monte Carlo

1.3. Neural Networks

1.3.1. Biological Fundamentals
1.3.2. Computational Model
1.3.3. Supervised and Unsupervised Neural Networks
1.3.4. Simple Perceptron
1.3.5. Multilayer Perceptron

1.4. Genetic Algorithms

1.4.1. History
1.4.2. Biological Basis
1.4.3. Problem Coding
1.4.4. Generation of the Initial Population
1.4.5. Main Algorithm and Genetic Operators
1.4.6. Evaluation of Individuals: Fitness

1.5. Thesauri, Vocabularies, Taxonomies

1.5.1. Vocabulary
1.5.2. Taxonomy
1.5.3. Thesauri
1.5.4. Ontologies
1.5.5. Knowledge Representation Semantic Web

1.6. Semantic Web

1.6.1. Specifications RDF, RDFS and OWL
1.6.2. Inference/Reasoning
1.6.3. Linked Data

1.7. Expert Systems and DSS

1.7.1. Expert Systems
1.7.2. Decision Support Systems

1.8. Chatbots and Virtual Assistants

1.8.1. Types of Assistants: Voice and Text Assistants
1.8.2. Fundamental Parts for the Development of an Assistant: Intents, Entities and Dialog Flow
1.8.3. Integrations: Web, Slack, WhatsApp, Facebook
1.8.4. Assistant Development Tools: Dialog Flow, Watson Assistant

1.9. AI Implementation Strategy
1.10. Future of Artificial Intelligence

1.10.1. Understand How to Detect Emotions Using Algorithms
1.10.2. Creating a Personality: Language, Expressions and Content
1.10.3. Trends of Artificial Intelligence
1.10.4. Reflections

Module 2. Data Types and Life Cycle

2.1. Statistics

2.1.1. Statistics: Descriptive Statistics, Statistical Inferences
2.1.2. Population, Sample, Individual
2.1.3. Variables: Definition, Measurement Scales

2.2. Types of Data Statistics

2.2.1. According to Type

2.2.1.1. Quantitative: Continuous Data and Discrete Data
2.2.1.2. Qualitative. Binomial Data, Nominal Data and Ordinal Data

2.2.2. According to their Shape

2.2.2.1. Numeric
2.2.2.2. Text
2.2.2.3. Logical

2.2.3. According to its Source

2.2.3.1. Primary
2.2.3.2. Secondary

2.3. Life Cycle of Data

2.3.1. Stages of the Cycle
2.3.2. Milestones of the Cycle
2.3.3. FAIR Principles

2.4. Initial Stages of the Cycle

2.4.1. Definition of Goals
2.4.2. Determination of Resource Requirements
2.4.3. Gantt Chart
2.4.4. Data Structure

2.5. Data Collection

2.5.1. Methodology of Data Collection
2.5.2. Data Collection Tools
2.5.3. Data Collection Channels

2.6. Data Cleaning

2.6.1. Phases of Data Cleansing
2.6.2. Data Quality
2.6.3. Data Manipulation (with R)

2.7. Data Analysis, Interpretation and Evaluation of Results

2.7.1. Statistical Measures
2.7.2. Relationship Indexes
2.7.3. Data Mining

2.8. Datawarehouse

2.8.1. Elements that Comprise it
2.8.2. Design
2.8.3. Aspects to Consider

2.9. Data Availability

2.9.1. Access
2.9.2. Uses
2.9.3. Security

2.10. Regulatory Framework

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Regulatory Aspects

Module 3. Data in Artificial Intelligence

3.1. Data Science

3.1.1. Data Science
3.1.2. Advanced Tools for Data Scientists

3.2. Data, Information and Knowledge

3.2.1. Data, Information and Knowledge
3.2.2. Types of Data
3.2.3. Data Sources

3.3. From Data to Information

3.3.1. Data Analysis
3.3.2. Types of Analysis
3.3.3. Extraction of Information from a Dataset

3.4. Extraction of Information Through Visualization

3.4.1. Visualization as an Analysis Tool
3.4.2. Visualization Methods
3.4.3. Visualization of a Data Set

3.5. Data Quality

3.5.1. Quality Data
3.5.2. Data Cleaning
3.5.3. Basic Data Preprocessing

3.6. Dataset

3.6.1. Dataset Enrichment
3.6.2. The Curse of Dimensionality
3.6.3. Modification of Our Data Set

3.7. Unbalance

3.7.1. Classes of Unbalance
3.7.2. Unbalance Mitigation Techniques
3.7.3. Balancing a Dataset

3.8. Unsupervised Models

3.8.1. Unsupervised Model
3.8.2. Methods
3.8.3. Classification with Unsupervised Models

3.9. Supervised Models

3.9.1. Supervised Model
3.9.2. Methods
3.9.3. Classification with Supervised Models

3.10. Tools and Good Practices

3.10.1. Good Practices for Data Scientists
3.10.2. The Best Model
3.10.3. Useful Tools

Module 4. Data Mining. Selection, Preprocessing and Transformation

4.1. Statistical Inference

4.1.1. Descriptive Statistics vs. Statistical Inference
4.1.2. Parametric Procedures
4.1.3. Non-Parametric Procedures

4.2. Exploratory Analysis

4.2.1. Descriptive Analysis
4.2.2. Visualization
4.2.3. Data Preparation

4.3. Data Preparation

4.3.1. Integration and Data Cleaning
4.3.2. Normalization of Data
4.3.3. Transforming Attributes

4.4. Missing Values

4.4.1. Treatment of Missing Values
4.4.2. Maximum Likelihood Imputation Methods
4.4.3. Missing Value Imputation Using Machine Learning

4.5. Noise in the Data

4.5.1. Noise Classes and Attributes
4.5.2. Noise Filtering
4.5.3. The Effect of Noise

4.6. The Curse of Dimensionality

4.6.1. Oversampling
4.6.2. Undersampling
4.6.3. Multidimensional Data Reduction

4.7. From Continuous to Discrete Attributes

4.7.1. Continuous Data Vs. Discreet Data
4.7.2. Discretization Process

4.8. The Data

4.8.1. Data Selection
4.8.2. Prospects and Selection Criteria
4.8.3. Selection Methods

4.9. Instance Selection

4.9.1. Methods for Instance Selection
4.9.2. Prototype Selection
4.9.3. Advanced Methods for Instance Selection

4.10. Data Preprocessing in Big Data Environments

Module 5. Algorithm and Complexity in Artificial Intelligence

5.1. Introduction to Algorithm Design Strategies

5.1.1. Recursion
5.1.2. Divide and Conquer
5.1.3. Other Strategies

5.2. Efficiency and Analysis of Algorithms

5.2.1. Efficiency Measures
5.2.2. Measuring the Size of the Input
5.2.3. Measuring Execution Time
5.2.4. Worst, Best and Average Case
5.2.5. Asymptotic Notation
5.2.6. Criteria for Mathematical Analysis of Non-Recursive Algorithms
5.2.7. Mathematical Analysis of Recursive Algorithms
5.2.8. Empirical Analysis of Algorithms

5.3. Sorting Algorithms

5.3.1. Concept of Sorting
5.3.2. Bubble Sorting
5.3.3. Sorting by Selection
5.3.4. Sorting by Insertion
5.3.5. Sorting by Mixing (Merge_Sort)
5.3.6. Quick Sort

5.4. Algorithms with Trees

5.4.1. Tree Concept
5.4.2. Binary Trees
5.4.3. Tree Paths
5.4.4. Representing Expressions
5.4.5. Ordered Binary Trees
5.4.6. Balanced Binary Trees

5.5. Algorithms Using Heaps

5.5.1. Heaps
5.5.2. The Heapsort Algorithm
5.5.3. Priority Queues

5.6. Graph Algorithms

5.6.1. Representation
5.6.2. Traversal in Width
5.6.3. Depth Travel
5.6.4. Topological Sorting

5.7. Greedy Algorithms

5.7.1. Greedy Strategy
5.7.2. Elements of the Greedy Strategy
5.7.3. Currency Exchange
5.7.4. Traveler’s Problem
5.7.5. Backpack Problem

5.8. Minimal Path Finding

5.8.1. The Minimum Path Problem
5.8.2. Negative Arcs and Cycles
5.8.3. Dijkstra's Algorithm

5.9. Greedy Algorithms on Graphs

5.9.1. The Minimum Covering Tree
5.9.2. Prim's Algorithm
5.9.3. Kruskal’s Algorithm
5.9.4. Complexity Analysis

5.10. Backtracking

5.10.1. Backtracking
5.10.2. Alternative Techniques

Module 6. Intelligent Systems

6.1. Agent Theory

6.1.1. Concept History
6.1.2. Agent Definition
6.1.3. Agents in Artificial Intelligence
6.1.4. Agents in Software Engineering

6.2. Agent Architectures

6.2.1. The Reasoning Process of an Agent
6.2.2. Reactive Agents
6.2.3. Deductive Agents
6.2.4. Hybrid Agents
6.2.5. Comparison

6.3. Information and Knowledge

6.3.1. Difference between Data, Information and Knowledge
6.3.2. Data Quality Assessment
6.3.3. Data Collection Methods
6.3.4. Information Acquisition Methods
6.3.5. Knowledge Acquisition Methods

6.4. Knowledge Representation

6.4.1. The Importance of Knowledge Representation
6.4.2. Definition of Knowledge Representation According to Roles
6.4.3. Knowledge Representation Features

6.5. Ontologies

6.5.1. Introduction to Metadata
6.5.2. Philosophical Concept of Ontology
6.5.3. Computing Concept of Ontology
6.5.4. Domain Ontologies and Higher-Level Ontologies
6.5.5. How to Build an Ontology?

6.6. Ontology Languages and Ontology Creation Software

6.6.1. Triple RDF, Turtle and N
6.6.2. RDF Schema
6.6.3. OWL
6.6.4. SPARQL
6.6.5. Introduction to Ontology Creation Tools
6.6.6. Installing and Using Protégé

6.7. Semantic Web

6.7.1. Current and Future Status of the Semantic Web
6.7.2. Semantic Web Applications

6.8. Other Knowledge Representation Models

6.8.1. Vocabulary
6.8.2. Global Vision
6.8.3. Taxonomy
6.8.4. Thesauri
6.8.5. Folksonomy
6.8.6. Comparison
6.8.7. Mind Maps

6.9. Knowledge Representation Assessment and Integration

6.9.1. Zero-Order Logic
6.9.2. First-Order Logic
6.9.3. Descriptive Logic
6.9.4. Relationship between Different Types of Logic
6.9.5. Prolog: Programming Based on First-Order Logic

6.10. Semantic Reasoners, Knowledge-Based Systems and Expert Systems

6.10.1. Concept of Reasoner
6.10.2. Reasoner Applications
6.10.3. Knowledge-Based Systems
6.10.4. MYCIN: History of Expert Systems
6.10.5. Expert Systems Elements and Architecture
6.10.6. Creating Expert Systems

Module 7. Machine Learning and Data Mining

7.1. Introduction to Knowledge Discovery Processes and Basic Concepts of Machine Learning

7.1.1. Key Concepts of Knowledge Discovery Processes
7.1.2. Historical Perspective of Knowledge Discovery Processes
7.1.3. Stages of the Knowledge Discovery Processes
7.1.4. Techniques Used in Knowledge Discovery Processes
7.1.5. Characteristics of Good Machine Learning Models
7.1.6. Types of Machine Learning Information
7.1.7. Basic Learning Concepts
7.1.8. Basic Concepts of Unsupervised Learning

7.2. Data Exploration and Preprocessing

7.2.1. Data Processing
7.2.2. Data Processing in the Data Analysis Flow
7.2.3. Types of Data
7.2.4. Data Transformations
7.2.5. Visualization and Exploration of Continuous Variables
7.2.6. Visualization and Exploration of Categorical Variables
7.2.7. Correlation Measures
7.2.8. Most Common Graphic Representations
7.2.9. Introduction to Multivariate Analysis and Dimensionality Reduction

7.3. Decision Trees

7.3.1. ID Algorithm
7.3.2. C Algorithm
7.3.3. Overtraining and Pruning
7.3.4. Result Analysis

7.4. Evaluation of Classifiers

7.4.1. Confusion Matrixes
7.4.2. Numerical Evaluation Matrixes
7.4.3. Kappa Statistic
7.4.4. ROC Curves

7.5. Classification Rules

7.5.1. Rule Evaluation Measures
7.5.2. Introduction to Graphic Representation
7.5.3. Sequential Overlay Algorithm

7.6. Neural Networks

7.6.1. Basic Concepts
7.6.2. Simple Neural Networks
7.6.3. Backpropagation Algorithm
7.6.4. Introduction to Recurrent Neural Networks

7.7. Bayesian Methods

7.7.1. Basic Probability Concepts
7.7.2. Bayes' Theorem
7.7.3. Naive Bayes
7.7.4. Introduction to Bayesian Networks

7.8. Regression and Continuous Response Models

7.8.1. Simple Linear Regression
7.8.2. Multiple Linear Regression
7.8.3. Logistic Regression
7.8.4. Regression Trees
7.8.5. Introduction to Support Vector Machines (SVM)
7.8.6. Goodness-of-Fit Measures

7.9. Clustering

7.9.1. Basic Concepts
7.9.2. Hierarchical Clustering
7.9.3. Probabilistic Methods
7.9.4. EM Algorithm
7.9.5. B-Cubed Method
7.9.6. Implicit Methods

7.10. Text Mining and Natural Language Processing (NLP)

7.10.1. Basic Concepts
7.10.2. Corpus Creation
7.10.3. Descriptive Analysis
7.10.4. Introduction to Feelings Analysis

Module 8. Neural networks, the basis of Deep Learning

8.1. Deep Learning

8.1.1. Types of Deep Learning
8.1.2. Applications of Deep Learning
8.1.3. Advantages and Disadvantages of Deep Learning

8.2. Surgery

8.2.1. Sum
8.2.2. Product
8.2.3. Transfer

8.3. Layers

8.3.1. Input Layer
8.3.2. Cloak
8.3.3. Output Layer

8.4. Layer Bonding and Operations

8.4.1. Architecture Design
8.4.2. Connection between Layers
8.4.3. Forward Propagation

8.5. Construction of the first neural network

8.5.1. Network Design
8.5.2. Establish the Weights
8.5.3. Network Training

8.6. Trainer and Optimizer

8.6.1. Optimizer Selection
8.6.2. Establishment of a Loss Function
8.6.3. Establishing a Metric

8.7. Application of the Principles of Neural Networks

8.7.1. Activation Functions
8.7.2. Backward Propagation
8.7.3. Parameter Adjustment

8.8. From Biological to Artificial Neurons

8.8.1. Functioning of a Biological Neuron
8.8.2. Transfer of Knowledge to Artificial Neurons
8.8.3. Establish Relations Between the Two

8.9. Implementation of MLP (Multilayer Perceptron) with Keras

8.9.1. Definition of the Network Structure
8.9.2. Model Compilation
8.9.3. Model Training

8.10. Fine Tuning Hyperparameters of Neural Networks

8.10.1. Selection of the Activation Function
8.10.2. Set the Learning Rate
8.10.3. Adjustment of Weights

Module 9. Deep Neural Networks Training

9.1. Gradient Problems

9.1.1. Gradient Optimization Techniques
9.1.2. Stochastic Gradients
9.1.3. Weight Initialization Techniques

9.2. Reuse of Pre-Trained Layers

9.2.1. Learning Transfer Training
9.2.2. Feature Extraction
9.2.3. Deep Learning

9.3. Optimizers

9.3.1. Stochastic Gradient Descent Optimizers
9.3.2. Optimizers Adam and RMSprop
9.3.3. Moment Optimizers

9.4. Learning Rate Programming

9.4.1. Automatic Learning Rate Control
9.4.2. Learning Cycles
9.4.3. Smoothing Terms

9.5. Overfitting

9.5.1. Cross Validation
9.5.2. Regularization
9.5.3. Evaluation Metrics

9.6. Practical Guidelines

9.6.1. Model Design
9.6.2. Selection of Metrics and Evaluation Parameters
9.6.3. Hypothesis Testing

9.7. Transfer Learning

9.7.1. Learning Transfer Training
9.7.2. Feature Extraction
9.7.3. Deep Learning

9.8. Data Augmentation

9.8.1. Image Transformations
9.8.2. Synthetic Data Generation
9.8.3. Text Transformation

9.9. Practical Application of Transfer Learning

9.9.1. Learning Transfer Training
9.9.2. Feature Extraction
9.9.3. Deep Learning

9.10. Regularization

9.10.1. L and L
9.10.2. Regularization by Maximum Entropy
9.10.3. Dropout

Module 10. Model Customization and training with TensorFlow

10.1. TensorFlow

10.1.1. Use of the TensorFlow Library
10.1.2. Model Training with TensorFlow
10.1.3. Operations with Graphs in TensorFlow

10.2. TensorFlow and NumPy

10.2.1. NumPy Computing Environment for TensorFlow
10.2.2. Using NumPy Arrays with TensorFlow
10.2.3. NumPy Operations for TensorFlow Graphs

10.3. Model Customization and Training Algorithms

10.3.1. Building Custom Models with TensorFlow
10.3.2. Management of Training Parameters
10.3.3. Use of Optimization Techniques for Training

10.4. TensorFlow Features and Graphs

10.4.1. Functions with TensorFlow
10.4.2. Use of Graphs for Model Training
10.4.3. Grap Optimization with TensorFlow Operations

10.5. Loading and Preprocessing Data with TensorFlow

10.5.1. Loading Data Sets with TensorFlow
10.5.2. Preprocessing Data with TensorFlow
10.5.3. Using TensorFlow Tools for Data Manipulation

10.6. The tf.data API

10.6.1. Using the tf.dataAPI for Data Processing
10.6.2. Construction of Data Streams with tf.data
10.6.3. Using the tf.data API for Model Training

10.7. The TFRecord Format

10.7.1. Using the TFRecord API for Data Serialization
10.7.2. TFRecord File Upload with TensorFlow
10.7.3. Using TFRecord Files for Model Training

10.8. Keras Preprocessing Layers

10.8.1. Using the Keras Preprocessing API
10.8.2. Construction of preprocessing Pipelined with Keras
10.8.3. Using the Keras Preprocessing API for Model Training

10.9. The TensorFlow Datasets Project

10.9.1. Using TensorFlow Datasets  for Data Loading
10.9.2. Preprocessing Data with TensorFlow Datasets
10.9.3. Using TensorFlow Datasets  for Model Training

10.10. Building a Deep Learning App with TensorFlow

10.10.1. Practical Applications
10.10.2. Building a Deep Learning App with TensorFlow
10.10.3. Model Training with TensorFlow
10.10.4. Use of the Application for the Prediction of Results

Module 11. Deep Computer Vision with Convolutional Neural Networks

11.1. The Visual Cortex Architecture

11.1.1. Functions of the Visual Cortex
11.1.2. Theories of Computational Vision
11.1.3. Models of Image Processing

11.2. Convolutional Layers

11.2.1. Reuse of Weights in Convolution
11.2.2. Convolution D
11.2.3. Activation Functions

11.3. Grouping Layers and Implementation of Grouping Layers with Keras

11.3.1. Pooling and Striding
11.3.2. Flattening
11.3.3. Types of Pooling

11.4. CNN Architecture

11.4.1. VGG Architecture
11.4.2. AlexNet Architecture
11.4.3. ResNet Architecture

11.5. Implementing a CNN ResNet using Keras

11.5.1. Weight Initialization
11.5.2. Input Layer Definition
11.5.3. Output Definition

11.6. Use of Pre-trained Keras Models

11.6.1. Characteristics of Pre-trained Models
11.6.2. Uses of Pre-trained Models
11.6.3. Advantages of Pre-trained Models

11.7. Pre-trained Models for Transfer Learning

11.7.1. Learning by Transfer
11.7.2. Transfer Learning Process
11.7.3. Advantages of Transfer Learning

11.8. Deep Computer Vision Classification and Localization

11.8.1. Image Classification
11.8.2. Localization of Objects in Images
11.8.3. Object Detection

11.9. Object Detection and Object Tracking

11.9.1. Object Detection Methods
11.9.2. Object Tracking Algorithms
11.9.3. Tracking and Localization Techniques

11.10. Semantic Segmentation

11.10.1. Deep Learning for Semantic Segmentation
11.10.2. Edge Detection
11.10.3. Rule-based Segmentation Methods

Module 12. Natural Language Processing (NLP) with Recurrent Neural Networks (RNN) and Attention

12.1. Text Generation using RNN

12.1.1. Training an RNN for Text Generation
12.1.2. Natural Language Generation with RNN
12.1.3. Text Generation Applications with RNN

12.2. Training Data Set Creation

12.2.1. Preparation of the Data for Training an RNN
12.2.2. Storage of the Training Dataset
12.2.3. Data Cleaning and Transformation
12.2.4. Sentiment Analysis

12.3. Classification of Opinions with RNN

12.3.1. Detection of Themes in Comments
12.3.2. Sentiment Analysis with Deep Learning Algorithms

12.4. Encoder-Decoder Network for Neural Machine Translation

12.4.1. Training an RNN for Machine Translation
12.4.2. Use of an Encoder-Decoder Network for Machine Translation
12.4.3. Improving the Accuracy of Machine Translation with RNNs

12.5. Attention Mechanisms

12.5.1. Application of Care Mechanisms in RNN
12.5.2. Use of Care Mechanisms to Improve the Accuracy of the Models
12.5.3. Advantages of Attention Mechanisms in Neural Networks

12.6. Transformer Models

12.6.1. Use of Transformers models for Natural Language Processing
12.6.2. Application of Transformers Models for Vision
12.6.3. Advantages of Transformers Models

12.7. Transformers for Vision

12.7.1. Use of Transformers Models for Vision
12.7.2. Image Data Preprocessing
12.7.3. Training a TransformersModel for Vision

12.8. Hugging Face’s Transformers Library

12.8.1. Using the Hugging Face's Transformers Library
12.8.2. Hugging Face´s Transformers Library App
12.8.3. Advantages of Hugging Face´s Transformers Library

12.9. Other Transformers Libraries. Comparison

12.9.1. Comparison Between Different Transformers Libraries
12.9.2. Use of the Other Transformers Libraries
12.9.3. Advantages of the Other Transformers Libraries

12.10. Development of an NLP Application with RNN and Attention. Practical Applications

12.10.1. Development of a Natural Language Processing Application with RNN and Attention.
12.10.2. Use of RNN, Attention Mechanisms and Transformers Models in the Application
12.10.3. Evaluation of the Practical Application

Module 13. Autoencoders, GANs and Diffusion Models

13.1. Representation of Efficient Data

13.1.1. Dimensionality Reduction
13.1.2. Deep Learning
13.1.3. Compact Representations

13.2. PCA Realization with an Incomplete Linear Automatic Encoder

13.2.1. Training Process
13.2.2. Implementation in Python
13.2.3. Use of Test Data

13.3. Stacked Automatic Encoders

13.3.1. Deep Neural Networks
13.3.2. Construction of Coding Architectures
13.3.3. Use of Regularization

13.4. Convolutional Autoencoders

13.4.1. Design of Convolutional Models
13.4.2. Convolutional Model Training
13.4.3. Results Evaluation

13.5. Noise Suppression of Autoencoders

13.5.1. Filter Application
13.5.2. Design of Coding Models
13.5.3. Use of Regularization Techniques

13.6. Sparse Automatic Encoders

13.6.1. Increasing Coding Efficiency
13.6.2. Minimizing the Number of Parameters
13.6.3. Using Regularization Techniques

13.7. Variational Automatic Encoders

13.7.1. Use of Variational Optimization
13.7.2. Unsupervised Deep Learning
13.7.3. Deep Latent Representations

13.8. Generation of Fashion MNIST Images

13.8.1. Pattern Recognition
13.8.2. Image Generation
13.8.3. Deep Neural Networks Training

13.9. Generative Adversarial Networks and Diffusion Models

13.9.1. Content Generation from Images
13.9.2. Modeling of Data Distributions
13.9.3. Use of Adversarial Networks

13.10. Implementation of the Models

13.10.1. Practical Application
13.10.2. Implementation of the Models
13.10.3. Use of Real Data
13.10.4. Results Evaluation

Module 14. Bio-Inspired Computing

14.1. Introduction to Bio-Inspired Computing

14.1.1. Introduction to Bio-Inspired Computing

14.2. Social Adaptation Algorithms

14.2.1. Bio-Inspired Computation Based on Ant Colonies
14.2.2. Variants of Ant Colony Algorithms
14.2.3. Particle Cloud Computing

14.3. Genetic Algorithms

14.3.1. General Structure
14.3.2. Implementations of the Major Operators

14.4. Space Exploration-Exploitation Strategies for Genetic Algorithms

14.4.1. CHC Algorithm
14.4.2. Multimodal Problems

14.5. Evolutionary Computing Models (I)

14.5.1. Evolutionary Strategies
14.5.2. Evolutionary Programming
14.5.3. Algorithms Based on Differential Evolution

14.6. Evolutionary Computation Models (II)

14.6.1. Evolutionary Models Based on Estimation of Distributions (EDA)
14.6.2. Genetic Programming

14.7. Evolutionary Programming Applied to Learning Problems

14.7.1. Rules-Based Learning
14.7.2. Evolutionary Methods in Instance Selection Problems

14.8. Multi-Objective Problems

14.8.1. Concept of Dominance
14.8.2. Application of Evolutionary Algorithms to Multi-Objective Problems

14.9. Neural Networks (I)

14.9.1. Introduction to Neural Networks
14.9.2. Practical Example with Neural Networks

14.10. Neural Networks (II)

14.10.1. Use Cases of Neural Networks in Medical Research
14.10.2. Use Cases of Neural Networks in Economics
14.10.3. Use Cases of Neural Networks in Artificial Vision

Module 15. Artificial Intelligence: Strategies and Applications

15.1. Financial Services

15.1.1. The Implications of Artificial Intelligence (AI) in Financial Services  Opportunities and Challenges
15.1.2. Case Uses
15.1.3. Potential Risks Related to the Use of AI
15.1.4. Potential Future Developments/Uses of AI

15.2. Implications of Artificial Intelligence in the Healthcare Service

15.2.1. Implications of AI in the Healthcare Sector. Opportunities and Challenges
15.2.2. Case Uses

15.3. Risks Related to the Use of AI in the Health Service

15.3.1. Potential Risks Related to the Use of AI
15.3.2. Potential Future Developments/Uses of AI

15.4. Retail

15.4.1. Implications of AI in Retail. Opportunities and Challenges
15.4.2. Case Uses
15.4.3. Potential Risks Related to the Use of AI
15.4.4. Potential Future Developments/Uses of AI

15.5. Industry

15.5.1. Implications of AI in Industry. Opportunities and Challenges
15.5.2. Case Uses

15.6. Potential Risks Related to the Use of AI in Industry

15.6.1. Case Uses
15.6.2. Potential Risks Related to the Use of AI
15.6.3. Potential Future Developments/Uses of AI

15.7. Public Administration

15.7.1. AI Implications for Public Administration. Opportunities and Challenges
15.7.2. Case Uses
15.7.3. Potential Risks Related to the Use of AI
15.7.4. Potential Future Developments/Uses of AI

15.8. Education

15.8.1. AI Implications for Education. Opportunities and Challenges
15.8.2. Case Uses
15.8.3. Potential Risks Related to the Use of AI
15.8.4. Potential Future Developments/Uses of AI

15.9. Forestry and Agriculture

15.9.1. Implications of AI in Forestry and Agriculture. Opportunities and Challenges
15.9.2. Case Uses
15.9.3. Potential Risks Related to the Use of AI
15.9.4. Potential Future Developments/Uses of AI

15.10. Human Resources

15.10.1. Implications of AI for Human Resources. Opportunities and Challenges
15.10.2. Case Uses
15.10.3. Potential Risks Related to the Use of AI
15.10.4. Potential Future Developments/Uses of AI

Module 16. Artificial Intelligence Methods and Tools for Clinical Research 

16.1. AI Technologies and Tools in Clinical Research 

16.1.1. Using Machine Learning to Identify Patterns in Clinical Data 
16.1.2. Development of Predictive Algorithms for Clinical Trials 
16.1.3. Implementation of AI Systems to Improve Patient Recruitment 
16.1.4. AI Tools for Real-Time Analysis of Research Data with Tableau

16.2. Statistical Methods and Algorithms in Clinical Trials 

16.2.1. Application of Advanced Statistical Techniques for Clinical Data Analysis 
16.2.2. Use of Algorithms for the Validation and Verification of Trial Results 
16.2.3. Implementation of Regression and Classification Models in Clinical Studies 
16.2.4. Analysis of Large Data Sets using Computational Statistical Methods 

16.3. Design of Experiments and Analysis of Results 

16.3.1. Strategies for the Efficient Design of Clinical Trials Using AI with IBM Watson Health
16.3.2. AI Techniques for Analysis and Interpretation of Experimental Data 
16.3.3. Optimization of Research Protocols Using AI Simulations 
16.3.4. Evaluation of Efficacy and Safety of Treatments Using AI Models 

16.4. Interpretation of Medical Images Using AI in Research Using Aidoc

16.4.1. Development of AI Systems for the Automatic Detection of Diseases in Images 
16.4.2. Use of Deep Learning for Classification and Segmentation in Medical Images 
16.4.3. AI Tools to Improve Accuracy in Image Diagnostics 
16.4.4. Analysis of Radiological and Magnetic Resonance Imaging Using AI 

16.5. Clinical Analysis and Biomedical Data Analysis 

16.5.1. AI in the Processing and Analysis of Genomic and Proteomic Data DeepGenomics
16.5.2. Tools for the Integrated Analysis of Clinical and Biomedical Data 
16.5.3. Use of AI to Identify Biomarkers in Clinical Research 
16.5.4. Predictive Analysis of Clinical Outcomes Based on Biomedical Data 

16.6. Advanced Data Visualization in Clinical Research 

16.6.1. Development of Interactive Visualization Tools for Clinical Data 
16.6.2. Use of AI in the Creation of Graphical Representations of Complex Data in Microsoft Power BI
16.6.3. Visualization Techniques for Easy Interpretation of Research Results 
16.6.4. Augmented and Virtual Reality Tools for Visualization of Biomedical Data 

16.7. Natural Language Processing in Scientific and Clinical Documentation 

16.7.1. Application of NLP for the Analysis of Scientific Literature and Clinical Records with Linguamatics
16.7.2. AI Tools for the Extraction of Relevant Information from Medical Texts 
16.7.3. AI Systems for Summarizing and Categorizing Scientific Publications 
16.7.4. Use of NLP to Identify Trends and Patterns in Clinical Documentation 

16.8. Processing Heterogeneous Data in Clinical Research with Google Cloud Healthcare API and IBM Watson Health

16.8.1. AI Techniques for Integrating and Analyzing Data from Diverse Clinical Sources 
16.8.2. Tools for the Management of Unstructured Clinical Data 
16.8.3. AI Systems for Correlating Clinical and Demographic Data 
16.8.4. Analysis of Multidimensional Data for Clinical Insights 

16.9. Applications of Neural Networks in Biomedical Research 

16.9.1. Use of Neural Networks for Disease Modeling and Treatment Prediction 
16.9.2. Implementation of Neural Networks in Genetic Disease Classification 
16.9.3. Development of Diagnostic Systems Based on Neural Networks 
16.9.4. Application of Neural Networks in the Personalization of Medical Treatments 

16.10. Predictive Modeling and its Impact on Clinical Research 

16.10.1. Development of Predictive Models for the Anticipation of Clinical Outcomes 
16.10.2. Use of AI in the Prediction of Side Effects and Adverse Reactions 
16.10.3. Implementation of Predictive Models in the Optimization of Clinical Trials 
16.10.4. Risk Analysis in Medical Treatments Using Predictive Modeling 

Module 17. Biomedical Research with AI 

17.1. Design and Implementation of Observational Studies with AI 

17.1.1. Implementation of AI for the Selection and Segmentation of Populations in Studies 
17.1.2. Use of Algorithms for Real-Time Monitoring of Observational Study Data 
17.1.3. AI Tools for Identifying Patterns and Correlations in Observational Studies with Flatiron Health
17.1.4. Automation of the Data Collection and Analysis Process in Observational Studies 

17.2. Validation and Calibration of Models in Clinical Research 

17.2.1. AI Techniques to Ensure the Accuracy and Reliability of Clinical Models 
17.2.2. Use of AI in the Calibration of Predictive Models in Clinical Research 
17.2.3. Cross-Validation Methods Applied to Clinical Models Using AI with KNIME Analytics Platform
17.2.4. AI Tools for the Evaluation of Generalization of Clinical Models 

17.3. Methods for Integration of Heterogeneous Data in Clinical Research 

17.3.1. AI Techniques for Combining Clinical, Genomic and Environmental Data with DeepGenomics
17.3.2. Use of Algorithms to Manage and Analyze Unstructured Clinical Data 
17.3.3. AI Tools for the Normalization and Standardization of Clinical Data with Informatica's Healthcare Data Management
17.3.4. AI Systems for Correlation of Different Types of Data in Research 

17.4. Integration of Multidisciplinary Biomedical Data Using Flatiron Health's OncologyCloud and AutoML

17.4.1. AI Systems to Combine Data from Different Biomedical Disciplines 
17.4.2. Algorithms for Integrated Analysis of Laboratory and Clinical Data 
17.4.3. AI Tools for Visualization of Complex Biomedical Data 
17.4.4. Use of AI in the Creation of Holistic Health Models from Multidisciplinary Data 

17.5. Deep Learning Algorithms in Biomedical Data Analysis 

17.5.1. Implementation of Neural Networks in the Analysis of Genetic and Proteomic Data 
17.5.2. Use of Deep Learning for Pattern Identification in Biomedical Data 
17.5.3. Development of Predictive Models in Precision Medicine with Deep Learning 
17.5.4. Application of AI in the Advanced Analysis of Biomedical Images Using Aidoc

17.6. Optimization of Research Processes with Automation 

17.6.1. Automation of Laboratory Routines Using AI Systems with Beckman Coulter
17.6.2. Use of AI for Efficient Management of Resources and Time in Research 
17.6.3. AI Tools for Optimization of Workflows in Clinical Research 
17.6.4. Automated Systems for Tracking and Reporting Progress in Research 

17.7. Simulation and Computational Modeling in Medicine with AI 

17.7.1. Development of Computational Models to Simulate Clinical Scenarios 
17.7.2. Using AI to Simulate Molecular and Cellular Interactions with Schrödinger 
17.7.3. AI Tools in the Creation of Predictive Disease Models with GNS Healthcare
17.7.4. Application of AI in the Simulation of Drug and Treatment Effects 

17.8. Use of Virtual and Augmented Reality in Clinical Studies with Surgical Theater

17.8.1. Implementation of Virtual Reality for Training and Simulation in Medicine 
17.8.2. Use of Augmented Reality in Surgical and Diagnostic Procedures 
17.8.3. Virtual Reality Tools for Behavioral and Psychological Studies 
17.8.4. Application of Immersive Technologies in Rehabilitation and Therapy 

17.9. Data Mining Tools Applied to Biomedical Research 

17.9.1. Use of Data Mining Techniques to Extract Knowledge from Biomedical Databases 
17.9.2. Implementation of AI Algorithms to Discover Patterns in Clinical Data 
17.9.3. AI Tools for Trend Identification in Large Data Sets with Tableau
17.9.4. Application of Data Mining in the Generation of Research Hypotheses  

17.10. Development and Validation of Biomarkers with Artificial Intelligence 

17.10.1. Use of AI for the Identification and Characterization of Novel Biomarkers 
17.10.2. Implementation of AI Models for the Validation of Biomarkers in Clinical Studies 
17.10.3.  AI Tools in the Correlation of Biomarkers with Clinical Outcomes
17.10.4. Application of AI in Biomarker Analysis for Personalized Medicine

Module 18. Practical Application of Artificial Intelligence in Clinical Research 

18.1. Genome Sequencing Technologies and Data Analysis with AI with DeepGenomics

18.1.1. Use of AI for Rapid and Accurate Analysis of Genetic Sequences 
18.1.2. Implementation of Machine Learning Algorithms in the Interpretation of Genomic Data 
18.1.3. AI Tools for Identification of Genetic Variants and Mutations 
18.1.4. Development of AI Systems for Anomaly Detection in Medical Images 

18.2. AI in the Analysis of Biomedical Images with Aidoc

18.2.1. Development of AI Systems for the Detection of Anomalies in Medical Images 
18.2.2. Use of Deep Learning in the Interpretation of X-rays, MRI and CT Scans 
18.2.3. AI Tools to Improve Accuracy in Diagnostic Imaging 
18.2.4. Implementation of AI in Biomedical Image Classification and Segmentation 

18.3. Robotics and Automation in Clinical Laboratories 

18.3.1. Use of Robots for the Automation of Tests and Processes in Laboratories 
18.3.2. Implementation of Automatic Systems for the Management of Biological Samples 
18.3.3. Development of Robotic Technologies to Improve Efficiency and Accuracy in Clinical Analysis 
18.3.4. Application of AI in the Optimization of Laboratory Workflows with Optum

18.4. AI in the Personalization of Therapies and Precision Medicine 

18.4.1. Development of AI Models for the Personalization of Medical Treatments 
18.4.2. Use of Predictive Algorithms in the Selection of Therapies based on Genetic Profiling 
18.4.3. AI Tools in the Adaptation of Drug Doses and Combinations with PharmGKB
18.4.4. Application of AI in the Identification of Effective Treatments for Specific Groups  

18.5. Innovations in AI-assisted Diagnosis using ChatGPT and Amazon Comprehend Medical

18.5.1. Implementation of AI Systems for Rapid and Accurate Diagnostics 
18.5.2. Use of AI in Early Identification of Diseases through Data Analysis 
18.5.3. Development of AI Tools for Clinical Test Interpretation 
18.5.4. Application of AI in Combining Clinical and Biomedical Data for Comprehensive Diagnostics 

18.6. AI Applications in Microbiome and Microbiology Studies with Metabiomics

18.6.1. Use of AI in the Analysis and Mapping of the Human Microbiome 
18.6.2. Implementation of Algorithms to Study the Relation between Microbiome and Diseases 
18.6.3. AI Tools in the Identification of Patterns in Microbiological Studies 
18.6.4. Application of AI in Microbiome-Based Therapeutics Research 

18.7. Wearables and Remote Monitoring in Clinical Trials 

18.7.1. Development of Wearable Devices with AI for Continuous Health Monitoring with FitBit
18.7.2. Use of AI in the Interpretation of Data Collected by Wearables 
18.7.3. Implementation of Remote Monitoring Systems in Clinical Trials 
18.7.4. Application of AI in the Prediction of Clinical Events through Wearable Data 

18.8. AI in the Management of Clinical Trials with Oracle Health Sciences

18.8.1. Use of AI Systems for Optimization of Clinical Trial Management 
18.8.2. Implementation of AI in the Selection and Monitoring of Participants 
18.8.3. AI Tools for Analysis of Clinical Trial Data and Results 
18.8.4. Application of AI to Improve Trial Efficiency and Reduce Trial Costs 

18.9. Development of AI-Assisted Vaccines and Treatments with Benevolent AI

18.9.1. Use of AI to Accelerate Vaccine Development 
18.9.2. Implementation of Predictive Models in the Identification of Potential Treatments 
18.9.3. AI Tools to Simulate Responses to Vaccines and Drugs 
18.9.4. Application of AI in the Personalization of Vaccines and Therapies 

18.10. AI Applications in Immunology and Immune Response Studies 

18.10.1. Development of AI Models to Understand Immunological Mechanisms with Immuneering
18.10.2. Use of AI in the Identification of Patterns in Immune Responses 
18.10.3. Implementation of AI in Autoimmune Disorders Research 
18.10.4. Application of AI in the Design of Personalized Immunotherapies 

Module 19. Big Data Analytics and Machine Learning in Clinical Research 

19.1. Big Data in Clinical Research: Concepts and Tools 

19.1.1. The Explosion of Data in the Field of Clinical Research 
19.1.2. Concept of Big Data and Main Tools 
19.1.3. Applications of Big Data in Clinical Research 

19.2. Data Mining in Clinical and Biomedical Records with KNIME and Python

19.2.1. Main Methodologies for Data Mining 
19.2.2. Data Integration of Clinical and Biomedical Registry Data 
19.2.3. Detection of Patterns and Anomalies in Clinical and Biomedical Records 

19.3.  Machine Learning Algorithms in Biomedical Research with KNIME and Python

19.3.1. Classification Techniques in Biomedical Research 
19.3.2. Regression Techniques in Biomedical Research 
19.3.4. Unsupervised Techniques in Biomedical Research 

19.4. Predictive Analytics in Clinical Research with KNIME and Python 

19.4.1. Classification Techniques in Clinical Research 
19.4.2. Regression Techniques in Clinical Research 
19.4.3. Deep Learning in Clinical Research 

19.5. AI Models in Epidemiology and Public Health with KNIME and Python

19.5.1. Classification Techniques for Epidemiology and Public Health 
19.5.2. Regression Techniques for Epidemiology and Public Health 
19.5.3. Unsupervised Techniques for Epidemiology and Public Health 

19.6. Analysis of Biological Networks and Disease Patterns with KNIME and Python

19.6.1. Exploration of Interactions in Biological Networks for the Identification of Disease Patterns 
19.6.2. Integration of Omics Data in Network Analysis to Characterize Biological Complexities 
19.6.3. Application of Machine Learning Algorithms for the Discovery of Disease Patterns 

19.7. Development of Tools for Clinical Prognosis with Workflow-type Platforms and Python

19.7.1. Creation of Innovative Clinical Prognostic Tools based on Multidimensional Data 
19.7.2. Integration of Clinical and Molecular Variables in the Development of Prognostic Tools 
19.7.3. Evaluating the Effectiveness of Prognostic Tools in Diverse Clinical Contexts 

19.8. Advanced Visualization and Communication of Complex Data with Tools like PowerBI and Python

19.8.1. Use of Advanced Visualization Techniques to Represent Complex Biomedical Data 
19.8.2. Development of Effective Communication Strategies to Present Results of Complex Analyses 
19.8.3. Implementation of Interactivity Tools in Visualizations to Enhance Understanding 

19.9. Data Security and Challenges in Big Data Management 

19.9.1. Addressing Data Security Challenges in the Context of Biomedical Big Data 
19.9.2. Strategies for Privacy Protection in the Management of Large Biomedical Datasets 
19.9.3. Implementation of Security Measures to Mitigate Risks in the Handling of Sensitive Data 

19.10. Practical Applications and Case Studies on Biomedical Big Data 

19.10.1. Exploration of Successful Cases in the Implementation of Biomedical Big Data in Clinical Research 
19.10.2. Development of Practical Strategies for the Application of Big Data in Clinical Decision Making 
19.10.3. Evaluation of Impact and Lessons Learned through Case Studies in the Biomedical Field 

Module 20. Ethical, Legal and Future Aspects of Artificial Intelligence in Clinical Research 

20.1. Ethics in the Application of AI in Clinical Research 

20.1.1. Ethical Analysis of AI-Assisted Decision Making in Clinical Research Settings 
20.1.2. Ethics in the Use of AI Algorithms for Participant Selection in Clinical Trials 
20.1.3. Ethical Considerations in the Interpretation of Results Generated by AI Systems in Clinical Research 

20.2. Legal and Regulatory Considerations in Biomedical AI 

20.2.1. Analysis of Legal Regulations in the Development and Application of AI Technologies in the Biomedical Field 
20.2.2. Assessment of Compliance with Specific Regulations to Ensure the Safety and Efficacy of AI-Based Solutions 
20.2.3. Addressing Emerging Regulatory Challenges Associated with the Use of AI in Biomedical Research 

20.3. Informed Consent and Ethical Aspects in the Use of Clinical Data

20.3.1. Development of Strategies to Ensure Effective Informed Consent in AI Projects 
20.3.2. Ethics in the Collection and Use of Sensitive Clinical Data in the Context of AI-Driven Research 
20.3.3. Addressing Ethical Issues Related to Ownership and Access to Clinical Data in Research Projects 

20.4. AI and Liability in Clinical Research 

20.4.1. Evaluation of Ethical and Legal Accountability in the Implementation of AI Systems in Clinical Research Protocols 
20.4.2. Development of Strategies to Address Potential Adverse Consequences of the Application of AI in Biomedical Research 
20.4.3. Ethical Considerations in the Active Participation of AI in Clinical Research Decision Making 

20.5. Impact of AI on Equity and Access to Health Care 

20.5.1. Evaluation of the Impact of AI Solutions on Equity in Clinical Trial Participation 
20.5.2. Development of Strategies to Improve Access to AI Technologies in Diverse Clinical Settings 
20.5.3. Ethics in the Distribution of Benefits and Risks Associated with the Application of AI in Health Care 

20.6. Privacy and Data Protection in Research Projects 

20.6.1. Ensuring the Privacy of Participants in Research Projects Involving the Use of AI 
20.6.2. Development of Policies and Practices for Data Protection in Biomedical Research 
20.6.3. Addressing Specific Privacy and Security Challenges in the Handling of Sensitive Data in the Clinical Environment 

20.7. AI and Sustainability in Biomedical Research 

20.7.1. Assessment of the Environmental Impact and Resources Associated with the Implementation of AI in Biomedical Research 
20.7.2. Development of Sustainable Practices in the Integration of AI Technologies into Clinical Research Projects 
20.7.3. Ethics in Resource Management and Sustainability in the Adoption of AI in Biomedical Research 

20.8. Auditing and Explainability of AI Models in the Clinical Setting

20.8.1. Development of Audit Protocols for Assessing the Reliability and Accuracy of AI Models in Clinical Research 
20.8.2. Ethics in Explainability of Algorithms to Ensure Understanding of Decisions Made by AI Systems in Clinical Contexts 
20.8.3. Addressing Ethical Challenges in the Interpretation of AI Model Results in Biomedical Research 

20.9. Innovation and Entrepreneurship in the Field of Clinical AI 

20.9.1. Responsible Innovation Ethics in Developing AI Solutions for Clinical Applications 
20.9.2. Development of Ethical Business Strategies in the Field of Clinical AI 
20.9.3. Ethical Considerations in the Commercialization and Adoption of AI Solutions in the Clinical Sector 

20.10. Ethical Considerations in International Collaboration in Clinical Research 

20.10.1. Development of Ethical and Legal Arrangements for International Collaboration in AI-Driven Research Projects 
20.10.2. Ethics in Multi-Institutional and Multi-Country Involvement in Clinical Research using AI Technologies 
20.10.3. Addressing Emerging Ethical Challenges Associated with Global Collaboration in Biomedical Research 

The program includes the analysis of ethical, legal and regulatory aspects, committing to responsibility and awareness of contemporary challenges"