Course Summary
This course explores how AI can be applied to medical treatment, covering topics such as disease diagnosis, personalized treatment, and clinical trial optimization.Key Learning Points
- Learn about the latest advancements in AI for medical treatment
- Discover how AI can be used for disease diagnosis and personalized treatment
- Understand how AI can optimize clinical trials and improve patient outcomes
Related Topics for further study
Learning Outcomes
- Understand how AI can be applied to medical treatment
- Develop skills in disease diagnosis and personalized treatment using AI
- Learn techniques for optimizing clinical trials and improving patient outcomes
Prerequisites or good to have knowledge before taking this course
- Basic understanding of AI and machine learning
- Familiarity with medical terminology and concepts
Course Difficulty Level
IntermediateCourse Format
- Online self-paced
- Video lectures
- Quizzes and assignments
Similar Courses
- AI in Healthcare Specialization
- Artificial Intelligence for Healthcare
Related Education Paths
Related Books
Description
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Knowledge
- Estimate treatment effects using data from randomized control trials
- Explore methods to interpret diagnostic and prognostic models
- Apply natural language processing to extract information from unstructured medical data
Outline
- Treatment Effect Estimation
- Intro to Course 3 with Andrew and Pranav
- About Course 3
- Absolute risk reduction
- Randomized control trials
- Causal inference
- Average treatment effect
- Conditional average treatment effect
- T-Learner
- S-Learner
- Evaluate individualized treatment effect
- C-for-benefit
- C-for-benefit calculation
- Connect with your mentors and fellow learners on Slack
- About the automatic grader
- How to refresh your workspace
- Quiz: Measuring Treatment Effects
- Medical Question Answering
- Medical question answering
- Handling words with multiple meanings
- Define the answer in a text
- Automatic label extraction for medical imaging
- Synonyms for labels
- Is-a relationships for labels
- Presence or absence of a disease
- Evaluating label extraction
- Precision and recall and F1 score
- Evaluating on multiple disease categories
- Quiz: Information Extraction with NLP
- ML Interpretation
- Drop column method
- Permutation method
- Individual feature importance
- Shapley values
- Combining importances
- Shapley values for all patients
- Interpreting CNN models
- Localization maps
- Heat maps
- Acknowledgements
- Citations
- (Optional) Opportunity to Mentor Other Learners
- ML Interpretation
Summary of User Reviews
Discover the intersection of AI and medicine with this top-rated course on Coursera. Students rave about the comprehensive coverage of the topic and the hands-on approach to learning.Key Aspect Users Liked About This Course
The hands-on approach to learningPros from User Reviews
- Comprehensive coverage of the topic
- Engaging and interactive course materials
- Expert instructors with real-world experience
- Great balance of theory and practical application
- Flexible schedule allows for self-paced learning
Cons from User Reviews
- Lack of in-depth technical discussion
- Some sections may be too basic for experienced AI developers
- Limited opportunities for interaction with other students
- Not suitable for those without a background in medicine or AI
- Occasional technical difficulties with the online platform
Pranav Rajpurkar
4.7 Raiting
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