AI's ability to integrate data from various sources, as well as to predict outcomes, contribute to more accurate and personalized medicine”

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

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

Furthermore, the degree integrates cutting-edge technologies such as genomic sequencing and biomedical image analysis, addressing emerging issues such as sustainability in biomedical research and big data management. In this context, students will be equipped with the necessary skills to lead at the intersection of AI and Clinical Research.

TECH has conceived a comprehensive program based on the innovative Relearning  methodology with the purpose of forging highly competent AI specialists. This learning modality focuses on reiterating key concepts to consolidate an optimal understanding. You will only need an electronic device connected to the Internet to access the contents at any time, eliminating the need for face-to-face attendance or to comply with established schedules.

This program in Artificial Intelligence in Clinical Research is highly relevant to the current landscape of healthcare and technology"

This professional master’s degree in Artificial Intelligence in Clinical Research 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 it is conceived scientific and practical information on those disciplines that are essential for professional practice
• Practical exercises where the self-assessment process can be carried out to improve learning
• Its special emphasis on innovative methodologies
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments
• Content that is accessible from any fixed or portable device with an Internet connection

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

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

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

You will delve into the implementation of Big Data and machine learning techniques in Clinical Research. Enroll now!"

This program not only aims to provide a deep understanding of Artificial Intelligence applied to Clinical Research, but also to train leaders capable of addressing current and future challenges in medicine. By entering this degree program, graduates will be immersed in an academic environment where innovation and ethics are intertwined to transform medical care. In this way, they will address medical data analysis techniques, the development of predictive models for clinical trials and the implementation of innovative solutions for the personalization of treatments. Thus, they will address clinical problems with data-driven solutions.

Bet on TECH! You will develop capabilities in AI and address clinical problems with data-driven solutions”

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle
• Evaluate the crucial role of data in the development and implementation of AI solutions
• Delve into algorithms and complexity to solve specific problems
• Explore the theoretical basis of neural networks for Deep Learning development
• Analyze bio-inspired computing and its relevance in the development of intelligent systems
• Analyze current strategies of Artificial Intelligence in various fields, identifying opportunities and challenges
• Gain a comprehensive view of the transformation of Clinical Research through AI, from its historical foundations to current applications
• Learn effective methods for integrating heterogeneous data into Clinical Research, including natural language processing and advanced data visualization
• Acquire solid knowledge of model validation and simulations in the biomedical domain, exploring the use of synthetic datasets and practical applications of AI in health research
• Understand and apply genomic sequencing technologies, data analysis with AI and use of AI in biomedical imaging
• Acquire expertise in key areas such as personalization of therapies, precision medicine, AI-assisted diagnostics and clinical trial management
• Gain a solid understanding of the concepts of Big Data in the clinical setting and become familiar with essential tools for its analysis
• Delve into ethical dilemmas, review legal considerations, explore the socioeconomic and future impact of AI in healthcare, and promote innovation and entrepreneurship in the field of clinical AI

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Explore the concept of the semantic web and its influence on the organization and understanding of information in digital environments

Module 2. Data Types and Data Life Cycle

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data 
• Analyze the life cycle of data, from generation to disposal, identifying key stages 
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure 
• Study data collection processes, including methodology, tools and collection channels 
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design 
• Analyze the regulatory aspects related to data management, complying with privacy and security regulations, as well as best practices

Module 3. Data in Artificial Intelligence

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Analyze supervised and unsupervised models, including methods and classification 
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence 

Module 4. Data Mining: Selection, Pre-Processing and Transformation 

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends 
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining 
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context 
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set 
• Address data preprocessing in Big Data environments 

Module 5. Algorithm and Complexity in Artificial Intelligence

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving 
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space 
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts 
• Explore tree-based algorithms, understanding their structure and applications 
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation 
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships 
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems 
• Study semantic reasoners, knowledge-based systems and expert systems, understanding their functionality and applications in intelligent decision making

Module 7. Machine Learning and Data Mining 

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems 
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers 
• Analyze regression and continuous response models for predicting numerical values from data 
• Study clustering techniques to identify patterns and structures in unlabeled data sets 
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text 

Module 8. Neural networks, the basis of Deep Learning

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning 
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately 
• Understand the effective linking of layers and operations to design complex and efficient neural network architectures 
• Use trainers and optimizers to tune and improve the performance of neural networks 
• Explore the connection between biological and artificial neurons for a deeper understanding of model design 
• Tune hyperparameters for Fine Tuning of neural networks, optimizing their performance on specific tasks 

Module 9. Deep Neural Networks Training

• Solve gradient-related problems in deep neural network training 
• Explore and apply different optimizers to improve the efficiency and convergence of models 
• Program the learning rate to dynamically adjust the convergence speed of the model 
• Understand and address overfitting through specific strategies during training 
• Apply practical guidelines to ensure efficient and effective training of deep neural networks 
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks 
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization 
• Develop practical applications using Transfer Learning to solve real-world problems 
• Understand and apply regularization techniques to improve generalization and avoid overfitting in deep neural networks 

Module 10. Model Customization and Training with TensorFlow

• Master the fundamentals of TensorFlow and its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow 
• Explore the tfdata API to efficiently manage and manipulate datasets 
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow 
• Use Keras preprocessing layers to facilitate the construction of custom models 
• Explore the TensorFlow Datasets project to access predefined datasets and improve development efficiency 
• Develop a Deep Learning  application with TensorFlow, integrating the knowledge acquired in the module 
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations 

Module 11. Deep Computer Vision with Convolutional Neural Networks

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision 
• Explore and apply convolutional layers to extract key features from images 
• Implement clustering layers and their use in  Deep Computer Vision models with Keras 
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts 
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance 
• Use pre-trained Keras models to leverage transfer learning for specific tasks 
• Apply classification and localization techniques in Deep Computer Vision environments 
• Explore object detection and object tracking strategies using Convolutional Neural Networks 
• Implement semantic segmentation techniques to understand and classify objects in images in a detailed manner 

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

• Developing skills in text generation using Recurrent Neural Networks (RNN) 
• Apply RNNs in opinion classification for sentiment analysis in texts 
• Understand and apply attentional mechanisms in natural language processing models 
• Analyze and use Transformers models in specific NLP tasks 
• Explore the application of Transformers models in the context of image processing and computer vision 
• Become familiar with the Hugging Face  Transformers library for efficient implementation of advanced models 
• Compare different Transformers libraries to evaluate their suitability for specific tasks 
  Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems 

Module 13. Autoencoders, GANs, and Diffusion Models

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation 
• Implement and understand the operation of stacked autoencoders 
• Explore and apply convolutional autoencoders for efficient visual data representations 
• Analyze and apply the effectiveness of sparse automatic encoders in data representation 
• Generate fashion images from the MNIST dataset using Autoencoders 
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models 
• Implement and compare the performance of Diffusion Models and GANs in data generation 

Module 14. Bio-Inspired Computing

• Introduce the fundamental concepts of bio-inspired computing
• Explore social adaptation algorithms as a key approach in bio-inspired computing 
• Analyze space exploration-exploitation strategies in genetic algorithms 
• Examine models of evolutionary computation in the context of optimization
• Continue detailed analysis of evolutionary computation models
• Apply evolutionary programming to specific learning problems 
• Address the complexity of multi-objective problems in the framework of bio-inspired computing 
• Explore the application of neural networks in the field of bio-inspired computing
• Delve into the implementation and usefulness of neural networks in bio-inspired computing

Module 15. Artificial Intelligence: Strategies and Applications 

• Develop strategies for the implementation of artificial intelligence in financial services
• Analyze the implications of artificial intelligence in the delivery of healthcare services 
• Identify and assess the risks associated with the use of AI in the healthcare field 
• Assess the potential risks associated with the use of AI in industry 
• Apply artificial intelligence techniques in industry to improve productivity 
• Design artificial intelligence solutions to optimize processes in public administration 
• Evaluate the implementation of AI technologies in the education sector 
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity 
• Optimize human resources processes through the strategic use of artificial intelligence 

Module 16. AI Methods and Tools for Clinical Research 

• Gain a comprehensive view of the AI is transforming Clinical Research, from its historical foundations to current applications 
• Implement advanced statistical methods and algorithms in clinical studies to optimize data analysis 
• Design experiments with innovative approaches and perform comprehensive analysis of results in Clinical Research 
• Apply natural language processing to improve scientific and clinical documentation in the Research context 
• Effectively integrate heterogeneous data using state-of-the-art techniques to enhance interdisciplinary clinical research 

Module 17. Biomedical Research with AI   

• Acquire solid knowledge on the validation of models and simulations in the biomedical field, ensuring their accuracy and clinical relevance 
• Integrate heterogeneous data using advanced methods to enrich the multidisciplinary analysis in Clinical Research 
• Develop deep learning algorithms to improve the interpretation and analysis of biomedical data in clinical trials 
• Explore the use of synthetic datasets in clinical studies and understand the practical applications of AI in health research 
• Understand the crucial role of computational simulation in drug discovery, analysis of molecular interactions, and modeling of complex diseases 

Module 18. Practical Application of AI in Clinical Research  

• Acquire expertise in key areas such as personalization of therapies, precision medicine, AI-assisted diagnostics, clinical trial management and vaccine development 
• Incorporate robotics and automation in clinical laboratories to optimize processes and improve the quality of results 
• Explore the impact of AI in microbiome, microbiology, wearables and remote monitoring in clinical studies 
• Address contemporary challenges in the biomedical field, such as efficient management of clinical trials, development of AI-assisted treatments, and application of AI in immunology and immune response studies 
• Innovate in AI-assisted diagnostics to improve early detection and diagnostic accuracy in clinical and biomedical research settings 

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

• Gain a solid understanding of the concepts fundamental of Big Data in the clinical setting and become familiar with essential tools Used for its analysis 
• Explore advanced data mining techniques, machine learning algorithms, predictive analytics, and AI applications in epidemiology and public health 
• Analyze biological networks and disease patterns to identify connections and potential treatments 
• Address data security and manage the challenges associated with large volumes of data in biomedical research 
• Investigate case studies that demonstrate the potential of Big Data in biomedical research 

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

• Understand the ethical dilemmas that arise when applying AI in clinical research and review the relevant legal and regulatory considerations in the biomedical field 
• Address specific challenges in the management of informed consent in AI studies 
• Investigate how AI can influence equity and access to health care 
• Analyze future perspectives on how AI will shape Clinical Research, exploring its role in the sustainability of biomedical research practices and identifying opportunities for innovation and entrepreneurship 
• Comprehensively address the ethical, legal and socioeconomic aspects of AI-driven Clinical Research 

Update your skills to be at the forefront of the technological revolution in health, contributing to the advancement of Clinical Research"