Únete ahora a este programa 100% online donde profundizarás en Algoritmos de Aprendizaje Automático y sus aplicaciones en la Investigación Médica”

Los Algoritmos de Aprendizaje Automático desempeñan un papel clave a la hora de establecer tratamientos terapéuticos personalizados y efectivos. Este conjunto de instrucciones definidas por computadoras emplea tanto datos clínicos como biomédicos o genéticos para desarrollar modelos predictivos. De esta forma, los facultativos aplican terapias personalizadas y pueden predecir las respuestas a las terapias para que tengan una mayor probabilidad de éxito. Asimismo, estas herramientas pueden calcular la dosis de medicamentos con precisión, lo que mejora la eficacia de los abordajes. 

En este contexto, TECH implementará un avanzado programa que profundizará en el uso de la Inteligencia Artificial durante la planificación y ejecución de procedimientos médicos. Bajo la guía de un versado cuadro docente, este plan de estudios analizará el reconocimiento de patrones y Machine Learning en diagnósticos clínicos. Así pues, los especialistas interpretarán correctamente las imágenes médicas para suministrar los tratamientos más adecuados en cada individuo. También el temario proporcionará competencias exhaustivas sobre los protocolos terapéuticos más innovadores. En esta línea, los materiales didácticos ofrecerán los últimos avances en robótica quirúrgica asistida para que los egresados se mantengan a la vanguardia tecnológica.

Por otro lado, la metodología del programa constituirá un reflejo de la necesidad de flexibilidad y adaptación a las demandas profesionales contemporáneas. Con un formato 100% online, permitirá a los egresados avanzar en su formación sin comprometer sus responsabilidades laborales. Además, la aplicación del sistema Relearning, basado en la reiteración de conceptos clave, asegura una comprensión profunda y duradera. Este enfoque pedagógico refuerza la capacidad de los profesionales para aplicar efectivamente los conocimientos adquiridos en su práctica diaria. A su vez, lo único que necesitarán los médicos para completar este itinerario académico será un dispositivo con acceso a Internet y el empeño por actualizar sus conocimientos que les permitirá experimentar un salto de calidad en sus carreras.

Aplicarás la Inteligencia Artificial para responder ante emergencias sanitarias como brotes epidemiológicos y en el desarrollo de nuevas vacunas”

Esta Postgraduate diploma en Diagnosis, Treatment and Personalization of Medical Treatment with Artificial Intelligence contiene el programa científico más completo y actualizado del mercado. Sus características más destacadas son: 

• El desarrollo de casos prácticos presentados por expertos en Inteligencia Artificial en la Práctica Clínica
• Los contenidos gráficos, esquemáticos y eminentemente prácticos con los que está concebido recogen una información científica y práctica sobre aquellas disciplinas indispensables para el ejercicio profesional
• Los ejercicios prácticos donde realizar el proceso de autoevaluación para mejorar el aprendizaje
• Su especial hincapié en metodologías innovadoras 
• Las lecciones teóricas, preguntas al experto, foros de discusión de temas controvertidos y trabajos de reflexión individual
• La disponibilidad de acceso a los contenidos desde cualquier dispositivo fijo o portátil con conexión a internet

Fomentarás la autonomía de los pacientes mediante su participación activa en el diseño de tratamientos personalizados tras el estudio de este programa”

El programa incluye en su cuadro docente a profesionales del sector que vierten en esta capacitación la experiencia de su trabajo, además de reconocidos especialistas de sociedades de referencia y universidades de prestigio. 

Su contenido multimedia, elaborado con la última tecnología educativa, permitirá al profesional un aprendizaje situado y contextual, es decir, un entorno simulado que proporcionará una capacitación inmersiva programada para entrenarse ante situaciones reales.
 
El diseño de este programa se centra en el Aprendizaje Basado en Problemas, mediante el cual el profesional deberá tratar de resolver las distintas situaciones de práctica profesional que se le planteen a lo largo del curso académico. Para ello, contará con la ayuda de un novedoso sistema de vídeo interactivo realizado por reconocidos expertos.  

Gracias a TECH, serás capaz de realizar integraciones de datos clínicos multimodales para lograr diagnósticos más precisos”

Actualizarás tus conocimientos clave mediante la innovadora metodología Relearning para una asimilación efectiva de la materia” 

This Postgraduate diploma will delve into diagnosis in clinical practice using Artificial Intelligence. Designed by specialists in this field, the syllabus will address pattern recognition and Machine Learning for medical assessment. The syllabus will also delve into assisted treatment systems, taking into account machine learning algorithms for the establishment of therapeutic processes. Likewise, the teaching materials will describe the various applications of intelligent automation in fields such as pharmacogenomics and drug design. In addition, the program will enable physicians to master the design of personalized therapies based on the particular needs of their patients. 

Enjoy the most up-to-date medical-scientific contents in the field without time restrictions or unnecessary travel to a study center”

Module 1. Diagnosis in Clinical Practice using AI

1.1. Technologies and Tools for AI-Assisted Diagnostics

1.1.1. Development of Software for AI-Assisted Diagnosis in Different Medical Specialties through ChatGPT
1.1.2. Use of Advanced Algorithms for Rapid and Accurate Analysis of Clinical Symptoms and Signs
1.1.3. Integration of AI into Diagnostic Devices to Improve Efficiency
1.1.4. AI Tools to Assist in the Interpretation of Laboratory Test Results through IBM Watson Health

1.2. Integration of Multimodal Clinical Data for Diagnosis

1.2.1. AI Systems to Combine Imaging, Laboratory, and Clinical Record Data using AutoML
1.2.2. Tools for Correlating Multimodal Data into More Accurate Diagnoses through Enlitic Curie
1.2.3. Use of AI to Analyze Complex Patterns from Different Types of Clinical Data through Flatiron Health’s OncologyCloud
1.2.4. Integration of Genomic and Molecular Data in AI-assisted Diagnosis

1.3. Creation and Analysis of Healthcare Datasets with AI using the Google Cloud Healthcare API

1.3.1. Development of Clinical Databases for AI Model Training
1.3.2. Use of AI for the Analysis and Extraction of Insights from Large Health Datasets
1.3.3. AI Tools for Clinical Data Cleaning and Preparation
1.3.4. AI Systems for Identifying Trends and Patterns in Health Data

1.4. Visualization and Management of Health Data with AI

1.4.1. AI Tools for Interactive and Understandable Visualization of Health Data
1.4.2. AI Systems for Efficient Management of Large Volumes of Clinical Data
1.4.3. Use of AI-Based Dashboards for the Monitoring of Health Indicators
1.4.4. AI Technologies for Health Data Management and Security

1.5. Pattern Recognition and Machine Learning in Clinical Diagnostics through PathAI

1.5.1. Application of Machine Learning Techniques for Pattern Recognition in Clinical Data
1.5.2. Use of AI in the Early Identification of Diseases through Pattern Analysis with PathAI
1.5.3. Development of Predictive Models for More Accurate Diagnoses
1.5.4. Implementation of Machine Learning Algorithms in the Interpretation of Health Data

1.6. Interpretation of Medical Images Using AI through Aidoc

1.6.1. AI Systems for Detection and Classification of Medical Image Anomalies
1.6.2. Use of Deep Learning in the Interpretation of X-rays, MRI and CT Scans
1.6.3. AI Tools to Improve Accuracy and Speed in Diagnostic Imaging
1.6.4. Implementation of AI for Image-based Clinical Decision Support

1.7. Natural Language Processing on Medical Records for Clinical Diagnosis Using ChatGPT and Amazon Comprehend Medical

1.7.1. Use of NLP for the Extraction of Relevant Information from Medical Records
1.7.2. AI Systems for Analyzing Physician Notes and Patient Reports
1.7.3. AI Tools for Summarizing and Classifying Medical Record Information
1.7.4. Application of NLP in the Identification of Symptoms and Diagnosis from Clinical Texts

1.8. Validation and Evaluation of AI-assisted Diagnostic Models through ConcertAI

1.8.1. Methods for Validation and Testing of AI Models in Real Clinical Settings
1.8.2. Performance and Accuracy Evaluation of AI-Assisted Diagnostic Tools
1.8.3. Use of AI to Ensure Reliability and Ethics in Clinical Diagnosis
1.8.4. Implementation of Continuous Assessment Protocols for AI Systems in Healthcare

1.9. AI in the Diagnosis of Rare Diseases using Face2Gene

1.9.1. Development of AI Systems Specialized in Rare Diseases Identification
1.9.2. Use of AI for Analyzing Atypical Patterns and Complex Symptomatology
1.9.3. AI Tools for Early and Accurate Diagnosis of Rare Diseases
1.9.4. Implementation of Global Databases with AI to Improve Diagnosis of Rare Diseases

1.10. Success Stories and Challenges in AI Diagnostics Implementation

1.10.1. Analysis of Case Studies where AI has Significantly Improved Clinical Diagnosis
1.10.2. Assessment of Challenges in AI adoption in Clinical Settings
1.10.3. Discussion on Ethical and Practical Barriers in the Implementation of AI for Diagnosis
1.10.4. Examination of Strategies for Overcoming Obstacles to the Integration of AI in Medical Diagnostics

Module 2. Treatment and Management of Patients with AI

2.1. AI-Assisted Treatment Systems

2.1.1. Development of AI Systems to Assist in Therapeutic Decision Making
2.1.2. Use of AI for the Personalization of Treatments Based on Individual Profiles
2.1.3. Implementation of AI Tools in the Administration of Doses and Medication Schedules
2.1.4. Integration of AI in Real-Time Treatment Monitoring and Adjustment

2.2. Definition of Indicators for Monitoring Patient Health Status

2.2.1. Establishment of Key Parameters Through AI for Patient Health Monitoring
2.2.2. Use of AI to Identify Predictive Indicators of Health and Disease
2.2.3. Development of Early Warning Systems Based on Health Indicators
2.2.4. Implementation of AI for Continuous Assessment of Patient Health Status

2.3. Tools for the Monitoring and Control of Health Indicators

2.3.1. Development of AI-Enabled Mobile and Wearable Applications for Health Monitoring 
2.3.2. Implementation of AI Systems for Real-Time Analysis of Health Data 
2.3.3. Use of AI-Based Dashboards for Visualization and Monitoring of Health Indicators 
2.3.4. Integration of IoT Devices in the Continuous Monitoring of Health Indicators with AI 

2.4. AI in the Planning and Execution of Medical Procedures with Intuitive Surgical's da Vinci Surgical System

2.4.1. Use of AI Systems to Optimize Planning of Surgeries and Medical Procedures
2.4.2. Implementation of AI in Simulation and Practice of Surgical Procedures
2.4.3. Use of AI to Improve Accuracy and Efficiency in the Execution of Medical Procedures 
2.4.4. Application of AI in Surgical Resource Coordination and Management

2.5. Machine Learning Algorithms for the Establishment of Therapeutic Treatments 

2.5.1. Use of Machine Learning to Develop Personalized Treatment Protocols
2.5.2. Implementation of Predictive Algorithms for the Selection of Effective Therapies
2.5.3. Development of AI Systems for Real-Time Treatment Adaptation
2.5.4. Application of AI in the Analysis of the Effectiveness of Different Therapeutic Options

2.6. Adaptability and Continuous Updating of Therapeutic Protocols using AI with IBM Watson for Oncology 

2.6.1. Implementation of AI Systems for Dynamic Review and Update of Treatments 
2.6.2. Use of AI in Adapting Therapeutic Protocols to New Findings and Data 
2.6.3. Development of AI Tools for Continuous Personalization of Treatments
2.6.4. Integration of AI in Adaptive Response to Evolving Patient Conditions 

2.7. Optimization of Healthcare Services Using AI Technology with Optum

2.7.1. Use of AI to Improve the Efficiency and Quality of Healthcare Services 
2.7.2. Implementation of AI Systems for Healthcare Resource Management
2.7.3. Development of AI Tools for the Optimization of Hospital Workflows 
2.7.4. Application of AI in the Reduction of Waiting Times and Improvement of Patient Care

2.8. Application of AI in the Response to Health Emergencies

2.8.1. Implementation of AI Systems for Rapid and Efficient Healthcare Crisis Management with BlueDot 
2.8.2. Use of AI in Optimizing Resource Allocation in Emergencies 
2.8.3. Development of AI Tools for Disease Outbreak Prediction and Response
2.8.4. Integration of AI in Warning and Communication Systems during Health Emergencies

2.9. Interdisciplinary Collaboration in AI-Assisted Treatments

2.9.1. Promoting Collaboration between Different Medical Specialties using AI Systems 
2.9.2. Use of AI to Integrate Knowledge and Techniques from Different Disciplines in Treatment 
2.9.3. Development of AI Platforms to Facilitate Interdisciplinary Communication and Coordination 
2.9.4. Implementation of AI in the Creation of Multidisciplinary Treatment Teams

2.10. Successful Experiences of AI in the Treatment of Diseases

2.10.1. Analysis of Successful Cases in the Use of AI for Effective Treatment of Diseases
2.10.2. Evaluation of the Impact of AI in Improving Treatment Outcomes
2.10.3. Documentation of Innovative Experiences in the Use of AI in Different Medical Areas
2.10.4. Discussion on the Advances and Challenges in the Implementation of AI in Medical Treatments

Module 3. Personalization of Healthcare through AI

3.1. AI Applications in Genomics for Personalized Medicine with DeepGenomics

3.1.1. Development of AI Algorithms for the Analysis of Genetic Sequences and their Relationship with Diseases 
3.1.2. Use of AI in the Identification of Genetic Markers for Personalized Treatments 
3.1.3. AI Implementation for Fast and Accurate Interpretation of Genomic Data
3.1.4. AI Tools in the Correlation of Genotypes with Drug Responses

3.2. AI in Pharmacogenomics and Drug Design using AtomWise

3.2.1. Development of AI Models for Predicting Drug Efficacy and Safety
3.2.2. Use of AI in Therapeutic Target Identification and Drug Design 
3.2.3. Application of AI in the Analysis of Gene-Drug Interactions for Treatment Personalization
3.2.4. Implementation of AI Algorithms to Accelerate the Discovery of New Drugs

3.3. Personalized Monitoring with Smart Devices and AI

3.3.1. Development of Wearables with AI for Continuous Monitoring of Health Indicators 
3.3.2. Use of AI in the Interpretation of Data Collected by Smart Devices with FitBit 
3.3.3. Implementation of AI-Based Early Warning Systems for Health Conditions 
3.3.4. AI Tools for Personalization of Lifestyle and Health Recommendations 

3.4. AI-Enabled Clinical Decision Support Systems

3.4.1. Implementation of AI to Assist Physicians in Clinical Decision Making with Oracle Cerner 
3.4.2. Development of AI Systems that Provide Recommendations Based on Clinical Data 
3.4.3. Use of AI in the Evaluation of Risks and Benefits of Different Therapeutic Options 
3.4.4. AI Tools for Real-time Health Data Integration and Analysis

3.5. Trends in Health Personalization with AI

3.5.1. Analysis of the Latest Trends in AI for Healthcare Personalization
3.5.2. Use of AI in the Development of Preventive and Predictive Approaches in Health
3.5.3. Implementing AI in Adapting Health Plans to Individual Needs
3.5.4. Exploring New AI Technologies in the Field of Personalized Health

3.6. Advances in AI-assisted Surgical Robotics with Intuitive Surgical's da Vinci Surgical System

3.6.1. Development of Surgical Robots with AI for Precise and Minimally Invasive Procedures
3.6.2. Using AI to Create Predictive Disease Models Based on Individual Data
3.6.3. Implementation of AI Systems for Surgical Planning and Simulation of Operations
3.6.4. Advances in the Integration of Tactile and Visual Feedback in Surgical Robotics with AI

3.7. Development of Predictive Models for Personalized Clinical Practice

3.7.1. Using AI to Create Predictive Disease Models Based on Individual Data
3.7.2. Implementation of AI in Predicting Treatment Responses
3.7.3. Development of AI Tools for Anticipating Health Risks
3.7.4. Application of Predictive Models in the Planning of Preventive Interventions

3.8. AI in Personalized Pain Management and Treatment with Kaia Health

3.8.1. Development of AI Systems for Personalized Pain Assessment and Management
3.8.2. Use of AI in Identifying Pain Patterns and Responses to Treatments
3.8.3. Implementation of AI Tools in Customizing Pain Therapies
3.8.4. Application of AI in Monitoring and Adjusting Pain Treatment Plans

3.9. Patient Autonomy and Active Participation in Personalization

3.9.1. Promoting Patient Autonomy through AI Tools for Patient Health Management with Ada Health
3.9.2. Development of AI Systems that Empower Patients in Decision Making
3.9.3. Using AI to Provide Personalized Information and Education to Patients
3.9.4. AI Tools that Facilitate Active Patient Participation in Treatment

3.10. Integration of AI in Electronic Medical Records with Oracle Cerner

3.10.1. AI Implementation for Efficient Analysis and Management of Electronic Medical Records
3.10.2. Development of AI Tools for Extracting Clinical Insights from Electronic Medical Records
3.10.3. Using AI to Improve Accuracy and Accessibility of Data in Medical Records
3.10.4. Application of AI for the Correlation of Clinical History Data with Treatment Plans

The didactic material of this program will take you deeper into machine learning algorithms for the establishment of therapies in a more visual way. Take this opportunity enroll now”