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

Big Data analysis significantly improves medical care and healthcare research. Such advanced systems provide experts with the opportunity to personalize treatments. Patient information such as medical history, genetics or lifestyle are used to tailor therapeutic plans and medications individually. In addition, these tools contribute to continuous monitoring of patients outside the clinical setting, which is especially beneficial for users suffering from chronic conditions. Therefore, AI resources contribute to the development of more effective and care-enhancing care management procedures.

For this reason, TECH has designed a Professional master’s degree that will delve into the analysis of Big Data and Machine Learning in Clinical Research. The syllabus will address aspects such as data mining in both clinical and biomedical records, while focusing on algorithms and providing predictive analysis techniques. Moreover, the program will explore the interactions that occur in biological networks for the identification of disease patterns. In addition, the curriculum will pay careful attention to the ethical and legal factors of AI in the medical context. In this way, graduates will gain a responsible conscience when carrying out their procedures.

It should be noted that, in order to consolidate all these contents, TECH relies on the revolutionary Relearning methodology. This teaching system is based on the reiteration of key concepts in order to consolidate an optimal understanding. The only requirement for students is to have an electronic device (such as a cell phone, computer or Tablet) connected to the Internet, in order to access the Virtual Campus and view the contents at any time. In this way, they will learn from the comfort of their homes, forgetting about classroom attendance and pre-established schedules.

You will master TensorFlow Datasets for data loading and achieve efficient medical data preprocessing thanks to this program”

This Professional master’s degree in Artificial Intelligence in Clinical Practice contains the most complete and up-to-date 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 will be at the forefront of the medical field! This program merges clinical excellence with the technological revolution of Machine Learning”

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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.

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

Forget about memorizing! With the Relearning system you will integrate the concepts in a natural and progressive way"

This academic degree merges the scientific rigor of Clinical Research with the disruptive innovations of Machine Learning. Comprised of 20 modules, this program will cover everything from the interpretation of medical data to the development of predictive algorithms and the implementation of technological solutions in clinical settings. The curriculum will offer content that combines theory and practice, laying the foundations 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.

You will delve into health data science, exploring biostatistics and big data analytics through 2,250 hours of innovative content”

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. Creation of 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. 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. 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 thetfdata 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 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. 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 (NRN) 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 Transformers Library

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, MRI 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 of Big 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

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