AI in Clinical Practice promises to improve the quality of medical care, reduce errors and open new frontiers for personalized medicine and biomedical research"

Artificial Intelligence can be applied to Medical Practice, analyzing large medical datasets to identify patterns and trends, and facilitating more accurate and earlier diagnoses. Furthermore, in patient management, AI is able to foresee potential complications, personalize treatments and optimize resource allocation, improving the efficiency and quality of medical care. The automation of routine tasks also frees up time for professionals to focus on more complex and human aspects of care, promoting significant advances in medicine. For this reason, TECH has developed this Professional Master's Degree in Intelligence in Clinical Practice, with a comprehensive and specialized approach. Specific modules will range from mastering the practical tools of AI to a critical understanding of its ethical and legal application in medicine. A focus on specific medical applications, such as AIassisted diagnosis and pain management, will equip professionals with advanced skills and knowledge in key areas of healthcare.

It will also foster multidisciplinary collaboration, preparing graduates to work in diverse teams within clinical settings. In addition, its ethical, legal and governance focus will ensure responsible understanding and practical application in the development and implementation of AI solutions in healthcare. The combination of theoretical and practical learning, along with the application of Big Data in healthcare, will enable clinicians to address current and future challenges in the field in a comprehensive and competent manner. 

Accordingly, TECH has devised a comprehensive program based on the innovative Relearning methodology, to train highly competent AI experts. This form of learning focuses on the repetition of key concepts to ensure a solid understanding. Only an electronic device with an Internet connection will be needed to access the content at any time, freeing participants from fixed schedules or the obligation to attend in person. 

The modular structure of the program will allow you a coherent progression, from the fundamentals to the most advanced applications"

This Professional Master's Degree in Artificial Intelligence contains the most complete and up-to-date scientific program on the market. The most important features include:

• Development of practical cases presented by experts in Artificial Intelligence in Clinical Practice 
•  The graphic, schematic, and practical contents with which they are created, provide scientific and practical information on the disciplines that are essential for professional practice
•  Practical exercises where self-assessment can be used 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'll delve into AI-backed data science in healthcare, exploring biostatistics and big data analytics through 2,250 hours of innovative content"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational 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 learn in 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 academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.     

You will analyze how AI interprets genetic data to design specific therapeutic strategies, thanks to this 100% online program"

You will apply data mining and machine learning in the context of healthcare. What are you waiting for to enroll?"

This Professional master’s degree is meticulously designed to merge clinical excellence with technological innovation. Its structure is based on specialized modules, ranging from the fundamentals of AI to specific applications in medical environments. Accordingly, the contents will offer a perfect balance between advanced theory and practical application, enabling professionals to tackle everything from data analysis to the personalization of treatments. In this way, graduates are prepared to make a difference in medicine, with a progressive vision and solid technical skills. 

Update your daily clinical practice to be at the forefront of the technological revolution in health, contributing to the advancement of Clinical Practice"  

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 Film 
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 Data 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 Indices
2.7.3. Data Mining

2.8. Data Warehouse (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/Safety

2.10. Regulatory Aspects 

2.10.1. Data Protection Law
2.10.2. Good Practices
2.10.3. Other Normative 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 Pre-Processing 

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, Pre-Processing 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 Pre-Processing 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. Mathematical Analysis Criteria for 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. 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 Pre-processing 

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. Algorithm C 
7.3.3. Overtraining and Pruning 
7.3.4. Analysis of Results 

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. Union of Layers 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 Graphics 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 TensorFlowGraphics 

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 Graphics 

10.4.1. Functions with TensorFlow 
10.4.2. Use of Graphs for Model Training 
10.4.3. Graphics 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 TensorFlowTools for Data Manipulation 

10.6. The API tfdata 

10.6.1. Using the tfdataAPI for Data Processing 
10.6.2. Construction of Data Streams with tfdata 
10.6.3. Using the tfdata API for Model Training 

10.7. The TFRecord Format 

10.7.1. Using the  TFRecordAPI 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. Preprocessing Pipelined Construction 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 Application 
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. Architecture ResNet 

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. Transfer Learning 
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 Natural Recurrent Networks (NNN) 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. Using TransformerModels for Natural Language Processing 
12.6.2. Application of Transformer Models for Vision 
12.6.3. Advantages of Transformer Models 

12.7. Transformers for Vision 

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

12.8. Hugging Face’s TransformersLibrary 

12.8.1. Using the Hugging Face’s TransformersLibrary 
12.8.2. Hugging Face’s TransformersLibrary App 
12.8.3. Advantages of  Hugging Face’s TransformersLibrary 

12.9. Other Transformers Libraries. Comparison 

12.9.1. Comparison between different TransformersLibraries 
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 Application 

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. Automatic Encoder Denoising 

13.5.1. Application of Filters 
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. Educational

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. Diagnosis in Clinical Practice Using AI 

16.1. Technologies and Tools for AI-assisted Diagnosis 

16.1.1. Development of Software for AI-assisted Diagnosis in Various Medical Specialties 
16.1.2. Use of Advanced Algorithms for Rapid and Accurate Analysis of Clinical Symptoms and Signs 
16.1.3. Integration of AI in Diagnostic Devices to Improve Efficiency 
16.1.4. AI Tools to Assist in the Interpretation of Laboratory Test Results 

16.2. Integration of Multimodal Clinical Data for Diagnosis 

16.2.1. AI Systems for Combining Imaging, Laboratory, and Clinical Record Data 
16.2.2. Tools for Correlating Multimodality Data into More Accurate Diagnoses 
16.2.3. Use of AI to Analyze Complex Patterns from Different Types of Clinical Data 
16.2.4. Integration of Genomic and Molecular Data in AI-assisted Diagnosis 

16.3. Creation and Analysis of Health Datasets with AI 

16.3.1. Development of Clinical Databases for Training AI Models 
16.3.2. Use of AI for Analysis and Extraction of Insights  from Large Health Datasets 
16.3.3. AI Tools for Clinical Data Cleaning and Preparation 
16.3.4. AI Systems for Identifying Trends and Patterns in Health Data 

16.4. Visualization and Management of Health Data with AI 

16.4.1. AI Tools for Interactive and Understandable Visualization of Health Data 
16.4.2. AI Systems for Efficient Management of Large Volumes of Clinical Data  
16.4.3. Use of AI-based Dashboards for Monitoring of Health Indicators 
16.4.4. AI Technologies for Health Data Management and Security 

16.5. Pattern Recognition and Machine Learning in Clinical Diagnostics 

16.5.1. Application of Machine Learning Techniques for Pattern Recognition in Clinical Data 
16.5.2. Use of AI in the Early Identification of Diseases through Pattern Analysis  
16.5.3. Development of Predictive Models for More Accurate Diagnoses 
16.5.4. Implementation of Automatic Learning Algorithms in the Interpretation of Health Data 

16.6. Interpretation of Medical Images Using AI 

16.6.1. AI Systems for Anomaly Detection and Classification of Medical Image Anomalies 
16.6.2. Use of Deep Learning in the Interpretation of X-rays, MRIs and CT Scans 
16.6.3. AI Tools for Improving Accuracy and Speed in Diagnostic Imaging  
16.6.4. Implementation of AI for Image-Based Clinical Decision-Making Assistance 

16.7. Natural Language Processing on Medical Records for Clinical Diagnosis 

16.7.1. Use of NLP for the Extraction of Relevant Information from Medical Records 
16.7.2. AI Systems for Analyzing Physician Notes and Patient Reports 
16.7.3. AI Tools for Summarizing and Classifying Information from Medical Records 
16.7.4. Application of NLP in Identifying Symptoms and Diagnoses from Clinical Texts  

16.8. Validation and Evaluation of AI-assisted Diagnostic Models 

16.8.1. Methods for Validation and Testing of AI Models in Real Clinical Environments 
16.8.2. Evaluation of the Performance and Accuracy of AI-assisted Diagnostic Tools 
16.8.3. Use of AI to Ensure Reliability and Ethics in Clinical Diagnosis 
16.8.4. Implementation of Continuous Assessment Protocols for AI Systems in Health Care 

16.9. AI in the Diagnosis of Rare Diseases 

16.9.1. Development of AI Systems Specializing in the Identification of Rare Diseases 
16.9.2. Use of AI to Analyze Atypical Patterns and Complex Symptomatology 
16.9.3. AI Tools for Early and Accurate Diagnosis of Rare Diseases 
16.9.4. Implementation of Global Databases with AI to Improve Diagnosis of Rare Diseases 

16.10. Success Stories and Challenges in AI Diagnostics Implementation 

16.10.1. Analysis of Case Studies Where AI Has Significantly Improved Clinical Diagnosis 
16.10.2. Assessment of the Challenges in the Adoption of AI in Clinical Settings 
16.10.3. Discussion of Ethical and Practical Barriers in Implementing AI for Diagnosis 
16.10.4. Examination of Strategies to Overcome Obstacles in Integrating AI in Medical Diagnostics 

Module 17. Treatment and Management of the AI Patient  

17.1. AI-assisted Treatment Systems 

17.1.1. Development of AI Systems to Assist in Therapeutic Decision Making 
17.1.2. Use of AI for the Personalization of Treatments Based on Individual Profiles 
17.1.3. Implementation of AI Tools in the Administration of Dosage and Medication Scheduling 
17.1.4. Integration of AI in Real-Time Monitoring and Adjustment of Treatments 

17.2. Definition of Indicators for Monitoring Patient Health Status 

17.2.1. Establishment of Key Parameters using AI for Patient Health Monitoring 
17.2.2. Use of AI to Identify Predictive Indicators of Health and Disease  
17.2.3. Development of Early Warning Systems Based on Health Indicators  
17.2.4. Implementation of AI for Continuous Assessment of Patient Health Status 

17.3. Tools for Monitoring and Controlling Health Indicators 

17.3.1. Development of AI-enabled Mobile and Wearable Applications for Health Monitoring  
17.3.2. Implementation of AI Systems for the Real-Time Analysis of Health Data 
17.3.3. Use of AI-based Dashboards for Visualization and Monitoring of Health Indicators 
17.3.4. Integration of IoT Devices in the Continuous Monitoring of Health Indicators with AI 

17.4. AI in the Planning and Execution of Medical Procedures 

17.4.1. Use of AI Systems to Optimize the Planning of Surgeries and Medical Procedures 
17.4.2. Implementation of AI in the Simulation and Practice of Surgical Procedures 
17.4.3. Use of AI to Improve Accuracy and Efficiency in the Execution of Medical Procedures 
17.4.4. Application of AI in Surgical Resource Coordination and Management 

17.5. Machine Learning Algorithms for the Establishment of Therapeutic Treatments 

17.5.1. Use of Machine Learning to Develop Personalized Treatment Protocols 
17.5.2. Implementation of Predictive Algorithms for the Selection of Effective Therapies 
17.5.3. Development of AI Systems for Real-time Tailoring of Treatments 
17.5.4. Application of AI in the Analysis of the Effectiveness of Different Therapeutic Options 

17.6. Adaptability and Continuous Updating of Therapeutic Protocols Using AI 

17.6.1. Implementation of AI Systems for Dynamic Review and Updating of Treatments 
17.6.2. Use of AI in Adaptation of Therapeutic Protocols to New Findings and Data 
17.6.3. Development of AI Tools for Continuous Personalization of Treatments 
17.6.4. Integration of AI in Adaptive Response to Evolving Patient Conditions 

17.7. Optimization of Healthcare Services with AI Technology 

17.7.1. Use of AI to Improve the Efficiency and Quality of Health Care Services 
17.7.2. Implementation of AI Systems for Healthcare Resource Management  
17.7.3. Development of AI Tools for Workflow Optimization in Hospitals 
17.7.4. Application of AI in the Reduction of Waiting Times and Improvement of Patient Care 

17.8. Application of AI in the Response to Health Emergencies 

17.8.1. Implementation of AI Systems for Rapid and Efficient Healthcare Crisis Management 
17.8.2. Use of AI in Optimizing the Distribution of Resources in Emergencies 
17.8.3. Development of AI Tools for Disease Outbreak Prediction and Response  
17.8.4. Integration of AI in Warning and Communication Systems during Health Emergencies 

17.9. Interdisciplinary Collaboration in AI-assisted Treatments 

17.9.1. Promotion of Collaboration between Different Medical Specialties through AI Systems 
17.9.2. Use of AI to Integrate Knowledge and Techniques from Different Disciplines in Treatment  
17.9.3. Development of AI Platforms to Facilitate Interdisciplinary Communication and Coordination 
17.9.4. Implementation of AI in the Creation of Multidisciplinary Treatment Teams  

17.10. Successful Experiences of AI in the Treatment of Diseases 

17.10.1. Analysis of Successful Cases in the Use of AI for Effective Treatment of Diseases 
17.10.2. Evaluation of the Impact of AI in Improving Treatment Outcomes 
17.10.3. Documentation of Innovative Experiences in the Use of AI in Different Medical Areas 
17.10.4. Discussion on the Advances and Challenges in the Implementation of AI in Medical Treatments 

Module 18. Health Personalization through AI 

18.1. AI Applications in Genomics for Personalized Medicine 

18.1.1. Development of AI Algorithms for the Analysis of Genetic Sequences and their Relationship to Diseases 
18.1.2. Use of AI in the Identification of Genetic Markers for Personalized Treatments 
18.1.3. Implementation of AI for the Rapid and Accurate Interpretation of Genomic Data 
18.1.4. AI Tools in Correlating Genotypes with Drug Responses 

18.2. AI in Pharmacogenomics and Drug Design 

18.2.1. Development of AI Models for Predicting Drug Efficacy and Safety  
18.2.2. Use of AI in the Identification of Therapeutic Targets and Drug Design 
18.2.3. Application of AI in the Analysis of Gene-Drug Interactions for Personalization of Treatments 
18.2.4. Implementation of AI Algorithms to Accelerate New Drug Discovery 

18.3. Personalized Monitoring with Smart Devices and AI 

18.3.1. Development of Wearables with AI for Continuous Monitoring of Health Indicators 
18.3.2. Use of AI in the Interpretation of Data Collected by Smart Devices  
18.3.3. Implementation of AI-based Early Warning Systems for Health Conditions 
18.3.4. AI Tools for Personalization of Lifestyle and Health Recommendations 

18.4. Clinical Decision Support Systems with AI 

18.4.1. Implementation of AI to Assist Clinicians in Clinical Decision Support Systems  
18.4.2. Development of AI Systems that Provide Clinical Data-Based Recommendations 
18.4.3. Use of AI in Risk/Benefit Assessment of Different Therapeutic Options 
18.4.4. AI tools for the Integration and Analysis of Real-Time Healthcare Data 

18.5. Trends in Health Personalization with AI 

18.5.1. Analysis of the Latest Trends in AI for Healthcare Personalization 
18.5.2. Use of AI in the Development of Preventive and Predictive Approaches in Health Care 
18.5.3. Implementation of AI in the Adaptation of Health Plans to Individual Needs 
18.5.4. Exploration of New AI Technologies in the Field of Personalized Health Care 

18.6. Advances in AI-assisted Surgical Robotics 

18.6.1. Development of AI-assisted Surgical Robots for Precise and Minimally Invasive Procedures 
18.6.2. Use of AI to Improve Accuracy and Safety in Robotic-Assisted Surgeries 
18.6.3. Implementation of AI Systems for Surgical Planning and Operative Simulation  
18.6.4. Advances in the Integration of Tactile and Visual Feedback in Surgical Robotics with AI 

18.7. Development of Predictive Models for Personalized Clinical Practice 

18.7.1. Use of AI to Create Predictive Models of Disease Based on Individual Data 
18.7.2. Implementation of AI in the Prediction of Treatment Responses 
18.7.3. Development of AI Tools for Health Risk Anticipation 
18.7.4. Application of Predictive Models in the Planning of Preventive Interventions  

18.8. AI in Pain Management and Personalized Pain Treatment 

18.8.1. Development of AI Systems for Personalized Pain Assessment and Management 
18.8.2. Use of AI in the Identification of Pain Patterns and Treatment Responses 
18.8.3. Implementation of AI Tools in the Personalization of Pain Therapies 
18.8.4. Application of AI in Monitoring and Adjustment of Pain Treatment Plans  

18.9. Patient Autonomy and Active Participation in Customization 

18.9.1. Promotion of Patient Autonomy through AI Tools for Health Management 
18.9.2. Development of AI Systems that Empower Patients in Decision Making 
18.9.3. Use of AI to Provide Personalized Information and Education to Patients 
18.9.4. AI Tools that Facilitate Active Patient Involvement in Treatment 

18.10. Integration of AI in Electronic Medical Records 

18.10.1. Implementation of AI for the Efficient Analysis and Management of Electronic Medical Records 
18.10.2. Development of AI Tools for Extraction of Clinical Insights  from Electronic Records 
18.10.3. Use of AI to Improve the Accuracy and Accessibility of Medical Record Data 
18.10.4. AI Application for Correlation of Medical Record Data with Treatment Plans 

Module 19. Analysis of Big Data  in the Healthcare Sector with AI   

19.1. Big Data Fundamentals in Health   

19.1.1. The Explosion of Data in Healthcare 
19.1.2. Concept of Big Data and Main Tools 
19.1.3. Applications of Big Data in Healthcare 

19.2. Text Processing and Analysis of Health Data   

19.2.1. Concepts of Natural Language Processing 
19.2.2. Embedding Techniques 
19.2.3. Application of Natural Language Processing in Health Care 

19.3. Advanced Methods for Data Retrieval in Health Care  

19.3.1. Exploration of Innovative Techniques for Efficient Data Retrieval in Health Care 
19.3.2. Development of Advanced Strategies for Extracting and Organizing Information in Health Care Settings 
19.3.3. Implementation of Adaptive and Personalized Data Retrieval Methods for Diverse Clinical Contexts 

19.4. Quality Assessment in Health Data Analysis   

19.4.1. Development of Indicators for Rigorous Assessment of Data Quality in Health Care Settings 
19.4.2. Implementation of Tools and Protocols for Quality Assurance of Data Used in Clinical Analyses  
19.4.3. Continuous Assessment of the Accuracy and Reliability of Results in Health Data Analysis Projects  

19.5. Data Mining and Automatic Learning in Healthcare   

19.5.1. Main Methodologies for Data Mining 
19.5.2. Health Data Integration 
19.5.3. Detection of Patterns and Anomalies in Health Data  

19.6. Innovative Areas of Big Data and AI in Healthcare   

19.6.1. Exploring New Frontiers in the Application of Big Data and AI to Transform the Healthcare Sector 
19.6.2. Identifying Innovative Opportunities for the Integration of Big Data and AI Technologies in Medical Practices  
19.6.3. Development of Cutting-edge Approaches to Maximize the Potential ofBig Dataand AI in Healthcare  

19.7. Medical Data Collection and Preprocessing   

19.7.1. Development of Efficient Methodologies for Medical Data Collection in Clinical and Research Settings 
19.7.2. Implementation of Advanced Preprocessing Techniques to Optimize Medical Data Quality and Utility 
19.7.3. Design of Collection and Preprocessing Strategies that Guarantee the Confidentiality and Privacy of Medical Information 

19.8. Data Visualization and Health Communication   

19.8.1. Design of Innovative Visualization Tools in Health Care 
19.8.2. Creative Health Communication Strategies 
19.8.3. Integration of Interactive Technologies in Health 

19.9. Data Security and Governance in the Health Sector   

19.9.1. Development of Comprehensive Data Security Strategies to Protect Confidentiality and Privacy in the Health Sector 
19.9.2. Implementation of Effective Governance Frameworks to Ensure Responsible and Ethical Data Management in Medical Settings 
19.9.3. Design of Policies and Procedures to Ensure the Integrity and Availability of Medical Data, Addressing Health Sector-Specific Challenges 

19.10. Practical Applications of Big Data in Healthcare   

19.10.1. Development of Specialized Solutions for Managing and Analyzing Large Data Sets in Healthcare Environments  
19.10.2. Use of Practical Tools Based on Big Data to Support Clinical Decision Making 
19.10.3. Application of Innovative Big Data Approaches to Address Specific Challenges within the Healthcare Sector 

Module 20. Ethics and Regulation in Medical AI   

20.1. Ethical Principles in the Use of AI in Medicine  

20.1.1. Analysis and Adoption of Ethical Principles in the Development and Use of Medical AI Systems  
20.1.2. Integration of Ethical Values in AI-assisted Decision Making in Medical Contexts  
20.1.3. Establishment of Ethical Guidelines to Ensure Responsible Use of Artificial Intelligence in Medicine 

20.2. Data Privacy and Consent in Medical Contexts   

20.2.1. Development of Privacy Policies to Protect Sensitive Data in Medical AI Applications 
20.2.2. Ensuring Informed Consent in the Collection and Use of Personal Data in Medical Settings  
20.2.3. Implementing Security Measures to Safeguard Patient Privacy in Medical AI Environments 

20.3. Ethics in the Research and Development of Medical AI Systems  

20.3.1. Ethical Evaluation of Research Protocols in the Development of AI Health Systems 
20.3.2. Ensuring Transparency and Ethical Rigor in the Development and Validation Phases of Medical AI Systems 
20.3.3. Ethical Considerations in the Publication and Sharing of Results in the Field of Medical AI 

20.4. Social Impact and Accountability in AI for Health   

20.4.1. Analysis of the Social Impact of AI in Health Care Delivery  
20.4.2. Development of Strategies to Mitigate Risks and Ethical Responsibility in AI Applications in Medicine 
20.4.3. Continuous Evaluation of the Social Impact and Adaptation of AI Systems to Make a Positive Contribution to Public Health 

20.5. Sustainable Development of AI in the Health Sector   

20.5.1. Integration of Sustainable Practices in the Development and Maintenance of AI Systems in Health 
20.5.2. Assessment of the Environmental and Economic Impact of AI Technologies in the Health Sector 
20.5.3. Development of Sustainable Business Models to Ensure Continuity and Improvement of AI Solutions in Healthcare 

20.6. Data Governance and International Regulatory Frameworks in Medical AI    

20.6.1. Development of Governance Frameworks for Ethical and Efficient Data Management in Medical AI Applications 
20.6.2. Adaptation to International Standards and Regulations to Ensure Ethical and Legal Compliance 
20.6.3. Active Participation in International Initiatives to Establish Ethical Standards in the Development of Medical AI Systems 

20.7. Economic Aspects of AI in the Healthcare Field   

20.7.1. Analysis of Economic and Cost-Benefit Implications in the Implementation of AI Systems in Healthcare 
20.7.2. Development of Business and Financing Models to Facilitate the Adoption of AI Technologies in the Healthcare Sector  
20.7.3. Assessment of Economic Efficiency and Equity in Access to AI-driven Health Services 

20.8. Human-centered Design of Medical AI Systems   

20.8.1. Integration of Human-Centered Design Principles to Improve Usability and Acceptability of Medical AI Systems 
20.8.2. Involvement of Healthcare Professionals and Patients in the Design Process to Ensure Relevance and Effectiveness of Solutions 
20.8.3. Continuous Evaluation of User Experience and Feedback to Optimize Interaction with AI Systems in Medical Settings 

20.9. Fairness and Transparency in Medical Machine Learning   

20.9.1. Development of Medical Machine Learning Models that Promote Fairness and Transparency 
20.9.2. Implementation of Practices to Mitigate Bias and Ensure Fairness in the Application of AI Algorithms in Healthcare 
20.9.3. Continued Assessment of Fairness and Transparency in the Development and Deployment of Machine Learning Solutions in Medicine 

20.10. Safety and Policy in the Deployment of AI in Medicine 

20.10.1. Development of Security Policies to Protect Data Integrity and Confidentiality in Medical AI Applications  
20.10.2. Implementation of Safety Measures in the Deployment of AI Systems to Prevent Risks and Ensure Patient Safety 
20.10.3. Continuous Evaluation of Safety Policies to Adapt to Technological Advances and New Challenges in the Deployment of AI in Medicine 

The teaching materials of this program, elaborated by these specialists, have contents that are completely applicable to your professional experiences"