AI Literacy E-Learning Program for Healthcare Professionals: a groundbreaking, comprehensive learning experience designed to propel your career into the exciting world of artificial intelligence. This unique program combines cutting-edge technology with expert insights, equipping you with the knowledge and skills necessary to harness the full potential of AI in your healthcare practice.
Our AI Literacy E-Learning Program is tailored to meet the diverse needs of healthcare professionals, from physicians and nurses to administrators and researchers. The curriculum covers the fundamentals of AI, its applications in healthcare, and the ethical considerations surrounding its use, ensuring a well-rounded understanding of this transformative technology.
Through interactive modules, engaging case studies, and real-world scenarios, our program will inspire you to embrace the potential of AI, paving the way for more intelligent, more efficient, patient-centred care. You’ll learn to integrate AI solutions effectively into your practice, driving innovation and enhancing patient outcomes.
The AI Literacy E-Learning Program is your gateway to a future where AI and healthcare unite to create a better, healthier world. Don’t miss the opportunity to be at the forefront of this technological revolution. Enrol today and embark on a journey that will redefine your career, empower your patients, and shape the future of healthcare. Experience the power of AI – the future is in your hands.
Curriculum
- 8 Sections
- 351 Lessons
- Lifetime
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- Module 1- Introduction to Medical Artificial IntelligenceIn module 1 you will have an overview of AI , we will look at the past, present and peak at the future of AI and how it can Transform Healthcare.17
- 1.0Welcome to Medical AI4 Minutes
- 1.1Defining AI4 Minutes
- 1.2The History of AI – Antiquity4 Minutes
- 1.3The History of AI – Early7 Minutes
- 1.4Eliza AI4 Minutes
- 1.6SHRDLU6 Minutes
- 1.8The History of AI – Modern5 Minutes
- 1.9IBM’s Deep Blue9 Minutes
- 1.11AlphaGO7 Minutes
- 1.13The current level of AI4 Minutes
- 1.14DeepMind’s AlphaFold4 Minutes
- 1.16IBM’s Watson4 Minutes
- 1.18Openai’s Gpt-44 Minutes
- 1.20Wayve AI6 Minutes
- 1.22How Medical AI is created4 Minutes
- 1.23Why AI Liteacy is Vital for Healthcare Professionals
- 1.24Intro to AI Quiz10 Minutes17 Questions
- Module 2- See, Understand and DefineModule Two addessed the most important key out of the whole Medical AI process, is the catalyst that will give you the direction and the drive to create something amazing !11
- 2.1See, Understand and Define the Problem4 Minutes
- 2.2Contextual Understanding3 Minutes
- 2.3Specificity3 Minutes
- 2.4Data Considerations3 Minutes
- 2.5Ethical and Legal Aspects3 Minutes
- 2.6Integration into Clinical Workflows3 Minutes
- 2.7Validation and Verification3 Minutes
- 2.8Post-Deployment Monitoring and Maintenance3 Minutes
- 2.9Stakeholder Engagement and Communication3 Minutes
- 2.10Overview of See, Understand and Define the Problem – Reflection10 Minutes2 Questions
- 2.11See, Understand and Define the Problem- Quiz14 Questions
- Module 3 - Undersatnding DataWelcome to Module 3, where we delve into the crucial role of data in the realm of Medical AI. This module is designed to provide a comprehensive understanding of how data forms the backbone of artificial intelligence in healthcare.93
- 3.0Intro to Module 3
- 3.1What is Data?6 Minutes
- 3.2What is Data Science?5 Minutes
- 3.3Working with Data7 Minutes
- 3.4Demystifying Data Collection5 Minutes
- 3.5Data and Data Collection Quiz10 Questions
- 3.6Data Preprocessing – Definition2 Minutes
- 3.7Data Preprocessing – Data Acquisition2 Minutes
- 3.8Data Preprocessing – Data Cleaning7 Minutes
- 3.9Data Preprocessing – Data Integration3 Minutes
- 3.10Data Preprocessing – Data Transformation2 Minutes
- 3.11Data Preprocessing – Data Reduction4 Minutes
- 3.12Data Preprocessing – Challenges4 Minutes
- 3.13Data Preprocessing – Challenges Reflection1 Question
- 3.14Data Preprocessing – Future of Data Preprocessing4 Minutes
- 3.15Data Preprocessing – Conclusion3 Minutes
- 3.16Data preprocessing – Quiz10 Questions
- 3.17Feature Engineering – Intro & Definition3 Minutes
- 3.18Feature Engineering – The Need3 Minutes
- 3.19Feature Engineering – Steps
- 3.20Feature Engineering – Feature Selection2 Minutes
- 3.21Feature Engineering – Feature Preprocessing4 Minutes
- 3.22Feature Engineering – Feature Construction3 Minutes
- 3.23Feature Engineering – Feature Evaluation4 Minutes
- 3.24Feature Engineering – Feature Do’s and Don’ts4 Minutes
- 3.25Feature Engineering – Conclusion and Next Steps
- 3.26Feature Engineering – Quiz10 Questions
- 3.27Model Selection – Introduction3 Minutes
- 3.28Model Selection – Steps*4 Minutes
- 3.29Model Selection – Understanding the Problem3 Minutes
- 3.30Model Selection – Preprocessing3 Minutes
- 3.31Model Selection – Feature Selection3 Minutes
- 3.32Model Selection – Model Training3 Minutes
- 3.33Model Selection – Model Evaluation3 Minutes
- 3.34Model Selection – Model Optimization3 Minutes
- 3.35Model Selection – Model Validation3 Minutes
- 3.36Model Selection – Recap and Final Thoughts4 Minutes
- 3.37Model Selection – Quiz10 Minutes10 Questions
- 3.38Model Training – Introduction2 Minutes
- 3.39Model Training – Understanding Medical AI3 Minutes
- 3.40Model Training – Overview3 Minutes
- 3.41Model Training – Data Collection4 Minutes
- 3.42Model Training – Data Preprocessing3 Minutes
- 3.43Model Training – Model Selection and Training3 Minutes
- 3.44Model Training – Model Evaluation5 Minutes
- 3.45Model Training – Model Optimization3 Minutes
- 3.46Model Training -Model Deployment3 Minutes
- 3.47Model Training – Recap & Key Takeaways3 Minutes
- 3.48Model Training – Quiz10 Minutes10 Questions
- 3.49Model Evaluation – Intro & Definition2 Minutes
- 3.50Model Evaluation – Overview
- 3.51Model Evaluation – Metrics, Dos & Don’ts4 Minutes
- 3.52Model Evaluation – Validation Techniques4 Minutes
- 3.53Model Evaluation – Clinical Validation4 Minutes
- 3.54Model Evaluation – Fine-Tuning3 Minutes
- 3.55Model Evaluation – Model Performance in Practice3 Minutes
- 3.56Model Evaluation – Role of Ethics2 Minutes
- 3.57Model Evaluation – Role of Ethics Reflection2 Questions
- 3.58Model Evaluation – Conclusion3 Minutes
- 3.59Model Evaluation – Quiz10 Minutes10 Questions
- 3.60Model Optimization – Introduction2 Minutes
- 3.61Model Optimization – Importance2 Minutes
- 3.62Model Optimization – Steps in Model Optimization3 Minutes
- 3.63Model Optimization – Hyper-parameter Tuning2 Minutes
- 3.64Model Optimization – Feature Selection2 Minutes
- 3.65Model Optimization – Regularization
- 3.66Model Optimization – Ensemble Methods2 Minutes
- 3.67Model Optimization – Pruning2 Minutes
- 3.68Model Optimization – Evaluation & Monitoring2 Minutes
- 3.69Model Optimization – Ethical Considerations2 Minutes
- 3.70Model Optimization – Summary and Conclusion2 Minutes
- 3.71Model Optimization – Quiz10 Questions
- 3.72Model Deployment – Introduction3 Minutes
- 3.73Model Deployment – Preparing for Deployment3 Minutes
- 3.74Model Deployment – Model Serialization3 Minutes
- 3.75Model Deployment – The Right Deployment Platform3 Minutes
- 3.76Model Deployment – Containers and Microservices3 Minutes
- 3.77Model Deployment – Model Serving2 Minutes
- 3.78Model Deployment – Monitoring and Updating3 Minutes
- 3.79Model Deployment – Ethical Considerations2 Minutes
- 3.80Model Deployment – Regulations and Compliance2 Minutes
- 3.81Model Deployment – Conclusion & Future Trends2 Minutes
- 3.82Model Deployment – Quiz10 Minutes10 Questions
- 3.83Monitoring & Maintenance – Introduction2 Minutes
- 3.84Monitoring & Maintenance – The Role of Monitoring2 Minutes
- 3.85Monitoring & Maintenance – The Do’s of Monitoring2 Minutes
- 3.86Monitoring & Maintenance – Don’ts of Monitoring2 Minutes
- 3.87Monitoring & Maintenance – The Do’s of Maintenance2 Minutes
- 3.88Monitoring & Maintenance-The Don’ts of Maintenance2 Minutes
- 3.89Monitoring & Maintenance – Best Practices2 Minutes
- 3.90Monitoring & Maintenance – Future Trends
- 3.91Monitoring & Maintenance – Conclusion2 Minutes
- 3.93Monitoring & Maintenance – Quiz10 Minutes10 Questions
- Module 4 - Supervised LearningWelcome to Supervised Learning56
- 4.1Suppervised Learnig4 Minutes
- 4.2Linear Regression – Definition2 Minutes
- 4.3Linear Regression – How does it work?3 Minutes
- 4.4Linear Regression – Assumptions10 Minutes
- 4.5Linear Regression – Training3 Minutes
- 4.6Linear Regression – Application in Medical AI3 Minutes
- 4.7Linear Regression – Case Study3 Minutes
- 4.8Liniar Regression – Benefits and Limitations3 Minutes
- 4.9Linear Regression – The future in Medical AI3 Minutes
- 4.10Linear Regression – Conclusion
- 4.11Linear Regression – Quiz10 Minutes10 Questions
- 4.12Logistic Regression – What is it?3 Minutes
- 4.13Logistic Regression- The Role & Function6 Minutes
- 4.14Logistic Regression – Challenges and Limitations2 Minutes
- 4.15Logistic Regression – Future1 Minute
- 4.16Logistic Regression – Conclusion1 Minute
- 4.17Logistic Regression – Quiz10 Minutes10 Questions
- 4.18Introduction to Decision Trees3 Minutes
- 4.19Decision Trees – What are they?3 Minutes
- 4.20Decision Trees – Working of Decision Trees3 Minutes
- 4.21Decision Trees – Why use in Medicine?3 Minutes
- 4.22Decision Trees – Constructing a Decision Tree3 Minutes
- 4.23Decision Trees – Overfitting and Underfitting3 Minutes
- 4.24Decision Trees – Pros and Cons3 Minutes
- 4.25Decision Trees – Ethics3 Minutes
- 4.26Decision Trees – Practical Cases Uses3 Minutes
- 4.27Decision Trees – Quiz10 Minutes10 Questions
- 4.28Introduction to Random Forests2 Minutes
- 4.29Random Forests – The Idea10 Minutes
- 4.30Random Forests vs Decision Trees2 Minutes
- 4.31Random Forests – Advantages2 Minutes
- 4.32Random Forests – Limitations2 Minutes
- 4.33Random Forests – Healthcare3 Minutes
- 4.34Random Forests – Ethical Considerations3 Minutes
- 4.35Random Forests – Summary3 Minutes
- 4.36Random Forests – Quiz10 Minutes10 Questions
- 4.37Introduction to Lasso Regression2 Minutes
- 4.38Lasso – Basics of Regression and Regularization2 Minutes
- 4.39Lasso – The Mathematics Behind Lasso Regression6 Minutes
- 4.40Lasso – The Benefits2 Minutes
- 4.41Lasso – Limitations2 Minutes
- 4.42Lasso – Regression in Practice2 Minutes
- 4.43Lasso – Regression in Medical AI2 Minutes
- 4.44Lasso – Future Perspectives2 Minutes
- 4.45Lasso – Summary2 Minutes
- 4.46Lasso Regression – Quiz10 Minutes10 Questions
- 4.47Introduction to Ridge Regression2 Minutes
- 4.48Ridge Regression – Understanding Linear Regression2 Minutes
- 4.49Ridge Regression- Mathematics2 Minutes
- 4.50Ridge Regression – Advantages2 Minutes
- 4.51Ridge Regression – Limitations2 Minutes
- 4.52Ridge Regression – In Healthcare2 Minutes
- 4.53Ridge Regression – Looking Forward2 Minutes
- 4.54Ridge Regression – Summary3 Minutes
- 4.55Ridge Regression- Quiz10 Minutes10 Questions
- 4.56Congratulations!
- Module 5 - Unsupervised LearningIn Module 5 we will cover Unsupervised Learnig and it value within the Medical AI.43
- 5.1Unsupervised Learning4 Minutes
- 5.2How Unsupervised learnign works3 Minutes
- 5.3Types of Unsupervised Learning3 Minutes
- 5.4Unsupervised Learning in Healthcare3 Minutes
- 5.5Disease Progression with Unsupervised Learning2 Minutes
- 5.6Challenges & Considerations3 Minutes
- 5.7The Future of Unsupervised Learning in Medicine3 Minutes
- 5.8Unsupervised Learning – Conclusion2 Minutes
- 5.9Unsupervised Learning – Quiz10 Minutes10 Questions
- 5.10What is Clustering?2 Minutes
- 5.11Clustering – Techniques2 Minutes
- 5.12Clustering – K-means Clustering2 Minutes
- 5.13Clustering – Hierarchical Clustering2 Minutes
- 5.14Clustering – DBSCAN2 Minutes
- 5.15Clustering – Evaluating Clustering Performance3 Minutes
- 5.16Clustering in Medical AI3 Minutes
- 5.17Clustering – Challenges and Future Directions in Clustering2 Minutes
- 5.18Clustering – Summary and Key Takeaways2 Minutes
- 5.19Clustering – Quiz10 Minutes10 Questions
- 5.20Introduction to Dimensionality Reduction3 Minutes
- 5.21Dimensionality Reduction – High-Dimensional Data Challenges2 Minutes
- 5.22Dimensionality Reduction in Medical AI2 Minutes
- 5.23Dimensionality Reduction – Principal Component Analysis (PCA)6 Minutes
- 5.24Dimensionality Reduction – Linear Discriminant Analysis (LDA)17 Minutes
- 5.25Dimensionality Reduction – Non-negative Matrix Factorization (NMF)12 Minutes
- 5.26Dimensionality Reduction – t-Distributed Stochastic Neighbor Embedding14 Minutes
- 5.27Dimensionality Reduction – UMAP11 Minutes
- 5.28Dimensionality Reduction – Autoencoders for Dimensionality Reduction6 Minutes
- 5.29Dimensionality Reduction – The Right Dimensionality Reduction Technique2 Minutes
- 5.30Dimensionality Reduction – Summary and Key Takeaways2 Minutes
- 5.31Dimensionality Reduction – Quiz10 Minutes10 Questions
- 5.32Introduction to Anomaly Detection7 Minutes
- 5.33Anomaly Detection – Understanding Anomalies8 Minutes
- 5.34Anomaly Detection -Unsupervised Learning and Anomaly Detection3 Minutes
- 5.35Anomaly Detection – Clustering-based Anomaly Detection3 Minutes
- 5.36Anomaly Detection – Association-based Anomaly Detection2 Minutes
- 5.37Anomaly Detection – Neural Network-Based Anomaly Detection3 Minutes
- 5.38Anomaly Detection – Anomaly Detection in Medical Imaging3 Minutes
- 5.39Anomaly Detection – Anomaly Detection in Patient Medical Records2 Minutes
- 5.40Anomaly Detection – Anomaly Detection in Real-Time Patient Monitoring3 Minutes
- 5.41Anomaly Detection – Summary and Future Perspectives3 Minutes
- 5.42Anomaly Detection – Conclusion and Learning Check2 Minutes
- 5.43Anomaly Detection- Quiz10 Minutes10 Questions
- Module 6 - Reinforcement Learning54
- 6.0Reinforcement Learning4 Minutes
- 6.1What is Reinforcement Learning?6 Minutes
- 6.2Principles of Reinforcement Learning6 Minutes
- 6.3Techniques in Reinforcement Learning3 Minutes
- 6.4Medical Applications of RL – Case Study3 Minutes
- 6.5Challenges and Future Directions3 Minutes
- 6.6Reinforcement Learning – Conclusion3 Minutes
- 6.7Reinforcement Learning – Quiz10 Minutes10 Questions
- 6.8Introduction to Value Iteration2 Minutes
- 6.9Value Iteration – Understanding Value Iteration3 Minutes
- 6.10Value Iteration – Role of Value Iteration in Reinforcement Learning3 Minutes
- 6.11Value Iteration – Case Study for Oncology Treatment3 Minutes
- 6.12Value Iteration – Case Study for Bed Allocation3 Minutes
- 6.13Value Iteration – Challenges and Limitations of Value Iteration3 Minutes
- 6.14Value Iteration – Future Prospects and Conclusion3 Minutes
- 6.15Value Iteration – Quiz10 Minutes10 Questions
- 6.16Introduction to Policy Iteration2 Minutes
- 6.17Policy Iteration- Theoretical Background3 Minutes
- 6.18Policy Iteration – Policy Evaluation Step2 Minutes
- 6.19Policy Iteration – Policy Improvement Step2 Minutes
- 6.20Policy Iteration – The Algorithm2 Minutes
- 6.21Policy Iteration – Healthcare Applications3 Minutes
- 6.22Policy Iteration – Advantages and Disadvantages3 Minutes
- 6.23Policy Iteration – The Future of Policy Iteration in Healthcare AI2 Minutes
- 6.24Policy Iteration – Conclusion
- 6.25Policy Iteration – Quiz10 Minutes10 Questions
- 6.26Introduction to Q-Learning2 Minutes
- 6.27Q-Learning – The Mechanics of Q-Learning3 Minutes
- 6.28Q-Learning – Components of Q-Learning3 Minutes
- 6.29Q-Learning – Algorithms3 Minutes
- 6.30Q-Learning – Medical AI Applications3 Minutes
- 6.31Q-Learning – Challenges and Ethical Considerations3 Minutes
- 6.32Q-Learning – Future Prospects of Q-Learning in Healthcare2 Minutes
- 6.33Q-Learning – Summary and Conclusion2 Minutes
- 6.34Q-Learning – Quiz10 Minutes10 Questions
- 6.35Introduction to Deep Q-Learning
- 6.36Deep Q-Learning – How Deep Q-Learning Works2 Minutes
- 6.37Deep Q-Learning vs. Traditional Q-Learning2 Minutes
- 6.38Deep Q-Learning in Healthcare: Medical Imaging2 Minutes
- 6.39Deep Q-Learning – Treatment Recommendation2 Minutes
- 6.40Deep Q-Learning – Challenges and Ethical Considerations2 Minutes
- 6.41Deep Q-Learning – Future of DQN in Healthcare2 Minutes
- 6.42Deep Q-Learning -Conclusion2 Minutes
- 6.43Deep Q-Learning – Quiz10 Minutes10 Questions
- 6.44Introduction to Policy Gradients *2 Minutes
- 6.45Policy Gradients – Understanding Policy-based Reinforcement Learning2 Minutes
- 6.46Policy Gradients – The Concept of Policy Gradient Methods2 Minutes
- 6.47Policy Gradients – Algorithms3 Minutes
- 6.48Policy Gradients -Mathematical Foundations of Policy Gradients3 Minutes
- 6.49Policy Gradients – Implementation in Medical AI3 Minutes
- 6.50Policy Gradients – Challenges and Ethical Considerations3 Minutes
- 6.51Policy Gradients – Future Directions and Conclusion2 Minutes
- 6.52Policy Gradients – Quiz10 Minutes10 Questions
- 6.53Congratulations2 Minutes
- Module 7 - Deep Learning118
- 7.0Deep Learning2 Minutes
- 7.1Deep Learnign – Overview of Artificial Intelligence in Healthcare3 Minutes
- 7.2What is Deep Learning?3 Minutes
- 7.3Deep Learning Architecture3 Minutes
- 7.4Key Concepts in Deep Learning3 Minutes
- 7.5Deep Learning in Medical Imaging3 Minutes
- 7.6Deep Learning in Genomics3 Minutes
- 7.7Deep Learning in Drug Discovery3 Minutes
- 7.8Challenges and Future Directions3 Minutes
- 7.9Deep Learning – Conclusion2 Minutes
- 7.10Deep Learning Intro – Quiz10 Minutes10 Questions
- 7.11Introduction to Multilayer Perceptrons2 Minutes
- 7.12MLPs Architecture3 Minutes
- 7.13Multilayer Perceptrons – Neurons and Activation Functions2 Minutes
- 7.14Multilayer Perceptrons – Understanding the Feedforward Process2 Minutes
- 7.15Multilayer Perceptrons – Backpropagation and Learning3 Minutes
- 7.16Multilayer Perceptrons – Regularization2 Minutes
- 7.17Multilayer Perceptrons – In Medical Field2 Minutes
- 7.18Multilayer Perceptrons – Challenges and Future Directions3 Minutes
- 7.19Multilayer Perceptrons in Healthcare – A Case Study3 Minutes
- 7.20Multilayer Perceptrons – Conclusion and Takeaways3 Minutes
- 7.21Multilayer Perceptrons – Quiz10 Minutes10 Questions
- 7.22Introduction to Convolutional Neural Networks2 Minutes
- 7.23Convolutional Neural Networks – Understanding the Basics3 Minutes
- 7.24Convolutional Neural Networks – Convolutional Layer, The Building Block of CNNs3 Minutes
- 7.25Convolutional Neural Networks – Pooling and Fully Connected Layers3 Minutes
- 7.26Convolutional Neural Networks – Training a CNN3 Minutes
- 7.27Convolutional Neural Networks – In Medical Imaging2 Minutes
- 7.28Convolutional Neural Networks – Case Studies : CNNs in Action3 Minutes
- 7.29Convolutional Neural Networks – Challenges and Future Directions3 Minutes
- 7.30Convolutional Neural Networks – Conclusion2 Minutes
- 7.31Convolutional Neural Networks – Quiz10 Minutes10 Questions
- 7.32Introduction to Radial Basis Function Networks2 Minutes
- 7.33Radial Basis Function Networks – Fundamentals of RBFNs2 Minutes
- 7.34Radial Basis Function Networks – Learning in RBFNs2 Minutes
- 7.35Radial Basis Function Networks – Regularization Techniques in RBFNs2 Minutes
- 7.36Radial Basis Function Networks – RBFNs in Healthcare: Applications2 Minutes
- 7.37Radial Basis Function Networks – Advantages and Limitations of RBFNs3 Minutes
- 7.38Radial Basis Function Networks Vs. Other Neural Networks2 Minutes
- 7.39Radial Basis Function Networks – Current Research and Future Directions in RBFNs2 Minutes
- 7.40Radial Basis Function Networks – Case Study: RBFNs in Predicting Heart Diseases2 Minutes
- 7.41Radial Basis Function Networks – Recap & Conclusion3 Minutes
- 7.43Radial Basis Function Networks – Quiz10 Minutes10 Questions
- 7.44Introduction to Self Organizing Maps (SOMs)2 Minutes
- 7.45Self Organizing Maps – The Basics of SOMs2 Minutes
- 7.46Self Organizing Maps – Learning Process2 Minutes
- 7.47Self Organizing Maps – Advantage2 Minutes
- 7.48Self Organizing Maps – Limitations and Challenges of SOMs2 Minutes
- 7.49Self Organizing Maps vs. Other Neural Networks2 Minutes
- 7.50Self Organizing Maps in Healthcare2 Minutes
- 7.51Self Organizing Maps in Medical Imaging3 Minutes
- 7.52Self Organizing Maps in Genomic Data Analysis2 Minutes
- 7.53Self Organizing Maps – Future Directions and Challenges3 Minutes
- 7.54Self Organizing Maps – Conclusion and Takeaway Points2 Minutes
- 7.55Self Organizing Maps – Quiz10 Minutes10 Questions
- 7.56Introduction to Recurrent Neural Networks (RNNs)2 Minutes
- 7.57Recurrent Neural Networks – Understanding RNN Architecture2 Minutes
- 7.58Recurrent Neural Networks – Fundamentals of RNNs3 Minutes
- 7.59Recurrent Neural Networks – Advanced RNNs: LSTM and GRU2 Minutes
- 7.60Recurrent Neural Networks – Real-world Applications of RNNs in Healthcare2 Minutes
- 7.61Recurrent Neural Networks – Deep Dive: RNNs in Patient Outcome Prediction2 Minutes
- 7.62Recurrent Neural Networks – Deep Dive: RNNs in Medical Time Series Analysis2 Minutes
- 7.63Recurrent Neural Networks – Deep Dive: RNNs in Natural Language Processing2 Minutes
- 7.64Recurrent Neural Networks – Deep Dive: RNNs in Genomic Sequence Analysis2 Minutes
- 7.65Recurrent Neural Networks – Summary, Conclusions, and Future Directions2 Minutes
- 7.66Recurrent Neural Networks – Quiz10 Minutes10 Questions
- 7.67Introduction to Long Short-Term Memory Networks2 Minutes
- 7.68Long Short-Term Memory Networks – How LSTMs Work2 Minutes
- 7.69Long Short-Term Memory Networks – The Strengths of LSTMs2 Minutes
- 7.70Long Short-Term Memory Networks – Challenges of LSTMs2 Minutes
- 7.71Long Short-Term Memory Networks in Healthcare2 Minutes
- 7.72Long Short-Term Memory Networks – Case Study: Medical Image Analysis2 Minutes
- 7.73Long Short-Term Memory Networks – Future of LSTMs in Healthcare2 Minutes
- 7.74Long Short-Term Memory Networks – Conclusion3 Minutes
- 7.75Long Short-Term Memory Networks – Quiz10 Minutes10 Questions
- 7.76Introduction to Restricted Boltzmann Machines2 Minutes
- 7.77Restricted Boltzmann Machines – Structure of RBMs2 Minutes
- 7.78Restricted Boltzmann Machines – Understanding the ‘Restricted’ in RBMs2 Minutes
- 7.79Restricted Boltzmann Machines – Energy-Based Model of RBMs3 Minutes
- 7.80Restricted Boltzmann Machines – Learning in RBMs3 Minutes
- 7.81Restricted Boltzmann Machines in Medical Imaging2 Minutes
- 7.82Restricted Boltzmann Machines in Disease Detection2 Minutes
- 7.83Restricted Boltzmann Machines in Image Enhancement and Reconstruction2 Minutes
- 7.84Restricted Boltzmann Machines in Multi-modal Image Fusion2 Minutes
- 7.85Restricted Boltzmann Machines – Challenges and Future Directions3 Minutes
- 7.86Restricted Boltzmann Machines – Summary and Conclusion2 Minutes
- 7.87Restricted Boltzmann Machines – Quiz10 Minutes10 Questions
- 7.88Introduction to Autoecoders3 Minutes
- 7.89Autoecoder – What is an Autoencoder?3 Minutes
- 7.90Autoecoder – Learning Process3 Minutes
- 7.91Autoecoder – Types of Autoencoders3 Minutes
- 7.92Autoecoder – Convolutional Autoencoders3 Minutes
- 7.93Autoencoder in Medical Imaging2 Minutes
- 7.94Autoencoder in Anomaly Detection3 Minutes
- 7.95Autoencoder – Challenges and Limitations3 Minutes
- 7.96Autoencoder – Future Outlook of Autoencoders2 Minutes
- 7.97Autoencoder – Conclusion2 Minutes
- 7.98Autoencoder – Quiz10 Minutes10 Questions
- 7.99Introduction to Deep Belief Networks2 Minutes
- 7.100Deep Belief Networks – What are Deep Belief Networks?2 Minutes
- 7.101Deep Belief Networks – Learning Process in DBNs2 Minutes
- 7.102Deep Belief Networks – Restricted Boltzmann Machines (RBMs)
- 7.103Deep Belief Networks – Medical Applications of DBNs: Medical Image Analys2 Minutes
- 7.104Deep Belief Networks – Medical Predictive Modeling2 Minutes
- 7.105Deep Belief Networks -Challenges and Limitations2 Minutes
- 7.106Deep Belief Networks – Future Outlook of DBNs2 Minutes
- 7.107Deep Belief Networks – Conclusion and Key Takeaways2 Minutes
- 7.108Deep Belief Networks – Quiz10 Minutes10 Questions
- 7.109Introduction to GANs2 Minutes
- 7.110Generative Adversarial Networks : How they work2 Minutes
- 7.111Generative Adversarial Networks – Learning Techniques2 Minutes
- 7.112Generative Adversarial Networks – Types of GANs2 Minutes
- 7.113Generative Adversarial Networks in Healthcare: Medical Imaging2 Minutes
- 7.114Generative Adversarial Networks in Drug Discovery2 Minutes
- 7.115Generative Adversarial Networks – Challenges and Limitations of GANs2 Minutes
- 7.116Generative Adversarial Networks – Future Prospects of GANs in Healthcare2 Minutes
- 7.117Generative Adversarial Networks – Summary and Conclusion2 Minutes
- 7.118Generative Adversarial Networks – Quiz10 Minutes10 Questions
- Feedback and Improvment0
Medical AI refers to the use of artificial intelligence technologies, including machine learning and deep learning algorithms, to simulate human cognition in the analysis, interpretation, and comprehension of complex medical and healthcare data. The primary goal of medical AI is to support healthcare professionals in making better clinical decisions and improve patient outcomes.
This course is ideal for healthcare professionals (doctors, nurses, pharmacists), healthcare administrators, medical researchers, students in the medical and health informatics fields, and anyone interested in understanding the impact of AI on healthcare.
The courese will award you 30 CPD Points.
You can claim the cost of the course as tax deductibile by contacting your accountant, or you can do it youself when you submit the self assesment, under professional developlent section of your filling, be sure to keep the recipt and the certificate.
While specific prerequisites may vary, generally, a basic understanding of healthcare terminologies and concepts is beneficial. Familiarity with basic principles of data science and computer science can be helpful but is not mandatory, as introductory modules are often included.
Key topics include the fundamentals of AI and machine learning, data privacy and ethics, applications of AI in diagnostics, treatment planning, personalized medicine, AI in medical imaging, natural language processing in healthcare, regulatory considerations, and future trends in medical AI.
Course duration can vary widely based on the the Student, as it is up each individual to adjust their learnign time frame. You decide - at an intensive rate the course can be completed within 2 weeks.
Yes, a bright new certificate with your name and achievements will show the world how amazing you are!
Requirements
- Educational Background: A basic understanding of healthcare concepts, which usually means having a degree or equivalent experience in a healthcare-related field such as medicine, nursing, pharmacy, or biomedical sciences.
- Basic Computer Literacy: Familiarity with computers and common software applications, as much of the course content and tools for learning about AI will likely be digital.
- Understanding of Basic Statistics and Data Analysis: Since AI heavily relies on data interpretation, a foundational knowledge of statistics can be very helpful.
- Interest in Technology and Innovation: A genuine interest in learning about AI technologies and their application in healthcare is crucial for staying engaged with the course material.
- Professional Experience in Healthcare: Practical experience in the healthcare sector can enrich understanding of how AI tools can be applied in real-world settings.
- Time Commitment: Ability to commit time to attend the course sessions, complete assignments, and engage in self-study. This is important for a comprehensive understanding of the material.
- Access to Required Technology: Having the necessary technology, such as a computer or a mobile with internet access, to participate in online modules, webinars, and access course materials.
- Language Proficiency: If the course is offered in English with multi language pack to come.
- Problem-Solving Skills: As AI involves dealing with complex problems, good analytical and problem-solving skills can be beneficial.
- Ethical and Legal Awareness: Some awareness of the ethical and legal considerations in healthcare, as AI applications often bring up new challenges in these areas.
Features
- Comprehensive Curriculum: Covering fundamental concepts of AI, machine learning, and their applications in various medical fields like diagnostics, treatment planning, and patient care management.
- Case Studies and Real-World Applications: Including detailed case studies that demonstrate the practical use of AI in healthcare, providing real-world context.
- Continuing Professional Develpoment : Providing CPD credits to healthcare professionals, which are necessary for maintaining their medical licenses.fessionals, which are necessary for maintaining their medical licenses.
- Flexible Learning Options: We understand that your time is so very little and pecious , so the learign is adjusted to your needs.
- Certification of Completion: Providing a certificate upon course completion, which can be added to professional credentials.
- Personalized Feedback and Assessment: Offering personalized feedback on assignments and assessments to aid in the learning process.
- Regulatory and Ethical Considerations: Addressing legal, ethical, and regulatory aspects of using AI in healthcare.
- Peer Collaboration and Networking Opportunities: Facilitating collaboration and networking with peers and experts in the field.
- Discussion Forums and Q&A Sessions: Platforms for students to discuss course material and have their questions answered by experts.
- Post-Course Support and Resources: Offering ongoing support and resources even after course completion to aid continuous learning and application of AI in healthcare.
Target audiences
- Healthcare Practitioners: Doctors, nurses, and other clinical staff who are directly involved in patient care and can apply AI insights in diagnosis, treatment planning, and patient monitoring.
- Medical Researchers: Individuals involved in medical research who can leverage AI for data analysis, predictive modeling, and conducting large-scale studies.
- Healthcare Administrators: Hospital administrators and healthcare managers who need to understand AI to make informed decisions about incorporating AI technologies into healthcare facilities.
- Medical Students and Residents: Those currently studying medicine or in residency programs who will soon be entering a healthcare landscape increasingly dominated by AI technologies.
- Biomedical Engineers and Technicians: Professionals involved in developing or maintaining medical equipment, where AI is increasingly used for innovation and improvement.
- Healthcare IT Professionals: IT staff within healthcare organizations who need to understand how AI can be integrated and managed within existing healthcare systems.
- Pharmaceutical Professionals: Individuals in the pharma industry who can use AI for drug discovery, patient data analysis, and personalized medicine.
- Healthcare Policy Makers: Government officials and policy makers involved in healthcare regulation who require an understanding of AI to create informed policies and regulations.
- Health Informatics Specialists: Professionals specializing in health informatics who can use AI to improve information management and decision support systems in healthcare.
- Public Health Officials and Epidemiologists: Experts who can use AI for tracking disease patterns, managing public health crises, and improving health outcomes on a population level.
