Through an extensive library of the most innovative multimedia resources, you will be able to integrate wearable devices and remote monitoring into clinical studies"

Artificial Intelligence mechanisms play an important role in tailoring drug doses and combinations in healthcare. These tools can help healthcare professionals make more informed and accurate decisions as to how much medication a patient should receive and whether different drugs need to be combined. One example of this is that Machine Learning employs individual user data such as weight, age or kidney function to calculate the optimal administration of products. This ensures that the amount administered is appropriate for the particular users, thus maximizing their efficacy.

Given this reality, TECH implements a Postgraduate certificate that will address genomic sequencing technologies and data analysis with Artificial Intelligence. The curriculum will develop intelligent systems for anomaly detection in medical images. In this way, the agenda will delve into the optimization of laboratory workflows, using predictive algorithms for therapies based on genetic profiles. In addition, the materials will examine the development of wearable devices with Artificial

Intelligence for continuous health monitoring and the implementation of remote tracking systems for clinical trials.
It is worth noting that the program will provide professionals with a theoretical foundation, but will also allow them to adequately deal with practical situations. The only thing they will need is an electronic device with Internet access to access the

Virtual Campus (cell phone, computer or even a tablet). In addition, this program is backed by the extensive experience of TECH's outstanding faculty, and is supported by an innovative and cutting-edge pedagogical methodology. This is Relearning, which consists of the repetition of essential concepts to achieve more effective knowledge acquisition.

You will address topics such as sustainability in biomedical research, future trends and innovation in Machine Learning applied to Clinical Research"

This Postgraduate certificate in Practical Applications of Artificial Intelligence in Clinical Research 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 Application of AI Technologies 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 develop skills in Artificial Intelligence and address clinical problems with data-driven solutions, all through a 100% online program"

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.

Get ready to lead the revolution in healthcare and contribute to the significant advancement of personalized medicine"

You will achieve your objectives thanks to TECH's didactic tools, including explanatory videos and interactive summaries"

This program will focus on biomedical image analysis, emphasizing how Artificial Intelligence improves both interpretation and diagnosis using medical images. The program will delve into robotics in clinical laboratories, urging graduates to optimize research processes. The curriculum will also delve into automation to personalize therapies based on individual circumstances. Moreover, the curriculum will offer a variety of concrete applications in vaccine development, assisted immunological analysis and integration of wearable devices for remote monitoring in clinical studies. 

The program has no fixed schedule and the curriculum is available from day one. Set your own learning pace!"

Module 1. Practical Application of AI in Clinical Research

1.1. Genomic Sequencing Technologies and Data Analysis with AI

1.1.1. Use of AI for Rapid and Accurate Analysis of Genetic Sequences
1.1.2. Implementation of Automatic Learning Algorithms in the Interpretation of Genomic Data
1.1.3. AI Tools to Identify Genetic Variants and Mutations
1.1.4. Application of AI in Genomic Correlation with Diseases and Traits

1.2. AI in Biomedical Image Analysis

1.2.1. Development of AI Systems for the Detection of Anomalies in Medical Imaging
1.2.2. Use of Deep Learning in the interpretation of X-rays, MRI and CT scans
1.2.3. AI Tools for Improving Accuracy in Diagnostic Imaging
1.2.4. Implementation of AI in the Classification and Segmentation of Biomedical Images

1.3. Robotics and Automation in Clinical Laboratories

1.3.1. Use of Robots for the Automation of Tests and Processes in Laboratories
1.3.2. Implementation of Automated Systems for the Management of Biological Samples
1.3.3. Development of Robotic Technologies to Improve Efficiency and Accuracy in Clinical Analyses
1.3.4. Application of AI in the Optimization of Laboratory Workflows

1.4. AI in the Personalization of Therapies and Precision Medicine

1.4.1. Development of AI Models for the Personalization of Medical Treatments
1.4.2. Use of Predictive Algorithms in the Selection of Therapies Based on Genetic Profiles
1.4.3. AI Tools in Dose Adaptation and Drug Combinations
1.4.4. Application of AI in the Identification of Effective Treatments for Specific Groups

1.5. Innovations in AI-assisted Diagnosis

1.5.1. Implementation of AI Systems for Rapid and Accurate Diagnostics
1.5.2. Use of AI in Early Disease Identification through Data Analysis
1.5.3. Development of AI Tools for Clinical Test Interpretation
1.5.4. Application of AI in Combining Clinical and Biomedical Data for Comprehensive Diagnostics

1.6. Applications of AI in Microbiome and Microbiology Studies

1.6.1. Use of AI in the Analysis and Mapping of the Human Microbiome
1.6.2. Implementation of Algorithms to Study the Relationship Between the Microbiome and Disease
1.6.3. AI Tools in the Identification of Patterns in Microbiological Studies
1.6.4. Application of AI in Microbiome-based Therapeutics Research

1.7. Wearables and Remote Monitoring in Clinical Studies

1.7.1. Development of Wearable Devices with AI for Continuous Health Monitoring
1.7.2. Use of AI in the Interpretation of Data Collected by Wearable Devices
1.7.3. Implementation of Remote Monitoring Systems in Clinical Trials
1.7.4. Application of AI in the Prediction of Clinical Events through Wearable Data

1.8. AI in Clinical Trial Management

1.8.1. Use of AI Systems to Optimize Clinical Trials Management
1.8.2. Implementation of AI in Participant Selection and Follow-Up
1.8.3. AI Tools for the Analysis of Clinical Trial Data and Results
1.8.4. Application of AI in Improving Trial Efficiency and Reducing Trial Costs

1.9. AI-assisted Development of Vaccines and Treatments

1.9.1. Use of AI in Accelerating Vaccine Development
1.9.2. Implementation of Predictive Models in the Identification of Potential Treatments
1.9.3. AI Tools for Simulating Vaccine and Drug Responses
1.9.4. Application of AI in the Personalization of Vaccines and Therapies

1.10. AI Applications in Immunology and Immune Response Studies

1.10.1. Development of AI Models for Understanding Immunological Mechanisms
1.10.2. Use of AI in the Identification of Patterns in Immune Responses
1.10.3. Implementation of AI in the Investigation of Autoimmune Disorders
1.10.4. Application of AI in the Design of Personalized Immunotherapies

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology"