The AI in Healthcare Course: AI-assisted diagnosis opens the doors to a a rapidly expanding field of vital importance to the healthcare sector. The Artificial intelligence is transforming medical diagnosis, optimising processes y improving the accuracy of the results. This online course, designed for adapt to your pace, provides you with the skills required to understand and apply AI models in clinical diagnosis. You’ll learn from the An Introduction to AI in Healthcare until the natural language processing in medical records, passing through the analysis of images and clinical data. The growing demand for professionals trained in these technologies makes this course a a key investment in your career.
Cross-cutting
AI Academy
45 hours
Healthcare: AI-assisted diagnosis
AI Academy
45 hours
Introduction
Objectives
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Understanding the impact of AI on improving medical diagnosis.
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Identify AI models applicable to clinical diagnosis.
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Applying AI-based medical image processing techniques.
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Analysing clinical data using effective AI algorithms.
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To evaluate the use of natural language processing (NLP) in medical records for diagnostic purposes.
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Integrating cognitive AI into efficient clinical workflows.
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Putting disease diagnosis into context using specialised AI.
Table of Contents
TEACHING UNIT 1. INTRODUCTION TO AI IN HEALTHCARE
Fundamentals of Artificial Intelligence in Healthcare
The historical development of AI in medicine
Main types of AI in healthcare
Current applications of AI in healthcare
Benefits and limitations of AI in healthcare
Ethical and regulatory considerations
The Future of AI in Healthcare
TEACHING UNIT 2. AI MODELS FOR DIAGNOSIS
Fundamentals of Modelling in Medical AI
Classical machine learning models
Neural networks and deep learning
Advanced architectures
Generative models
Model selection and evaluation
Multimodal models
Implementation considerations
Interpretability and explainability
Future trends in modelling
TEACHING UNIT 3. APPLICATION OF AI MODELS IN THE DIAGNOSTIC PROCESS
Conceptual framework for the assisted diagnostic process
Data preparation for clinical implementation
Development and validation of diagnostic systems
Technical implementation in hospital systems
Change management and clinical adoption
System monitoring and maintenance
Clinical impact assessment
Case studies of successful implementation
Implementation challenges and solutions
The future of diagnostic AI implementation
TEACHING UNIT 4. AI-ASSISTED MEDICAL IMAGE PROCESSING
Fundamentals of Medical Image Processing
Pre-processing of medical images
Feature extraction in medical images
Medical image segmentation
Detection and classification of lesions
Applications by imaging modality
Validation and clinical evaluation
Challenges and limitations
Future trends
TEACHING UNIT 5. ANALYSIS OF CLINICAL AND BIOMEDICAL DATA USING AI
Characteristics of clinical and biomedical data
Pre-processing of clinical data
Analysis of clinical laboratory data
Genomic analysis and precision medicine
Proteomics and metabolomics analysis
Clinical predictive models
Implementation of decision support systems
Ethical and privacy considerations
TEACHING UNIT 6. CONTEXTUALISED DIAGNOSIS OF SPECIFIC DISEASES
Cardiovascular diseases
Cancer
Neurological disorders
Infectious diseases
A&E medicine
Paediatric medicine
Geriatric medicine
Population and epidemiological considerations
Multidisciplinary integration
Assessment of clinical effectiveness
TEACHING UNIT 7. NATURAL LANGUAGE PROCESSING (NLP) IN MEDICAL RECORDS
Fundamentals of the Medical PLN
Fundamental techniques in medical NLP
Extraction of clinical information
Semantic Analysis and Contextual Comprehension
Specific Applications in Medical Documentation
Automatic Generation of Medical Text
Documentation Quality Assessment
Integration with Clinical Systems
Challenges and Limitations
Future Trends
TEACHING UNIT 8. INTEGRATING COGNITIVE AI INTO THE CLINICAL WORKFLOW
Concepts of Cognitive AI in Medicine
Architecture of Medical Cognitive Systems
Assisted Clinical Workflows
The Transformation of Professional Roles
Organisational Change Management
Ethical and Regulatory Issues
Impact on the Doctor-Patient Relationship
Success Metrics and Evaluation
Implementation Case Studies
The Future of Cognitive AI in Medicine
Preparing for the Future
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