AI For Medicine

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. In this Specialization, you’ll gain practical experience applying machine learning to concrete problems in medicine. You’ll learn how to:

  • Diagnose diseases from x-rays and 3D MRI brain images
  • Predict patient survival rates more accurately using tree-based models
  • Estimate treatment effects on patients using data from randomized trials
  • Automate the task of labeling medical datasets using natural language processing

These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.

Course 1: AI For Medical Diagnosis

How can AI be applied to medical imaging to diagnose diseases? In this first course, you’ll learn about the nuances of working with both 2D and 3D medical image data, for multi-class classification and image segmentation. You’ll then apply what you’ve learned to classify diseases in x-ray images and segment tumors in 3D MRI brain images. Finally, you’ll learn how to properly evaluate the performance of your models.

Week 1:

  • Introduction: A conversation with Andrew Ng
  • Diagnosis examples
  • Model training on chest x-rays
  • Training, prediction, and loss
  • Class imbalance
  • Binary cross entropy loss function
  • Resampling methods
  • Multi-task loss
  • Transfer learning and data augmentation
  • Model testing


Week 2:

  • Introduction: A conversation with Andrew Ng
  • Evaluation metrics
  • Accuracy in terms of conditional probability
  • Sensitivity, specificity, and prevalence
  • Confusion matrix
  • ROC curve
  • Threshold (operating point)
  • Confidence intervals
  • Width of confidence intervals and sample size
  • Using a sample to estimate the population


Week 3:

  • Introduction: A conversation with Andrew Ng
  • Representing MRI data
  • Image registration
  • 2D and 3D segmentation
  • 3D U-Net
  • Data augmentation for segmentation
  • Loss function for image segmentation
  • Soft dice loss
  • External validation
  • Retrospective vs. prospective data
  • Working with cleaned vs. raw data
  • Measuring patient outcomes
  • Algorithmic bias
  • Model influence on medical decision-making


Enroll in Course 1

Course 2: AI For Medical Prognosis

Machine learning is a powerful tool for prognosis, a branch of medicine that specializes in predicting the future health of patients. First, you’ll walk through multiple examples of prognostic tasks. You’ll then use decision trees to model non-linear relationships, which are commonly observed in medical data, and apply them to predicting mortality rates more accurately. Finally, you’ll learn how to handle missing data, a key real-world challenge.


Week 1:

  • Introduction: A conversation with Andrew Ng
  • Examples of prognostic tasks
  • Patient profile to risk score
  • Risk score for atrial fibrillation
  • Liver disease mortality
  • Calculate 10-year risk of heart disease
  • Risk score computation
  • Evaluating prognostic models
  • Concordant pairs
  • Risk ties
  • Permissible pairs
  • C-index interpretation


Week 2:

  • Decision trees for prognosis
  • Predicting mortality risk
  • Dividing the input space
  • Non-linear associations
  • Class boundaries of a decision tree
  • Random forest
  • Ensemble methods
  • Survival data
  • Problems with dropping incomplete rows
  • Dropping incomplete case changes the distribution
  • Imputation
  • Mean imputation
  • Regression imputation


Week 3:

  • Survival function
  • Censoring
  • Collecting time data
  • Heart attack data
  • Estimating the survival function
  • Using censored data
  • Chain rule of conditional probability
  • Derivation
  • Calculating probabilities from the data
  • Comparing estimates
  • Kaplan Meier Estimate


Week 4:

  • Hazard functions
  • Survival to hazard
  • Cumulative hazard
  • Individualized predictions
  • Individual vs. baseline hazard
  • Smoker vs. non-smoker
  • Effect of age on hazard
  • Factor risk increase or decrease
  • Survival trees
  • Nelson Aelen estimator
  • Mortality score
  • Evaluating survival models
  • Permissible pair examples
  • Harrell’s concordance index


Enroll in Course 2

Course 3: AI For Medical Treatment

Medical treatment may impact patients differently based on their existing health conditions. In this final course, you’ll estimate treatment effects using data from randomized control trials and applying tree-based models. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. In the final week of this course, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.


Week 1:

  • Treatment effect estimation
  • Randomized control trials
  • Average risk reductio
  • Individualized treatment effect
  • T-Learner and S-Learner
  • C-for-benefit

Week 2:

  • Information extraction from medical reports
  • Rules-based label extraction
  • Text matching
  • Negation detection
  • Dependency parsing
  • Question-Answering with BERT


Week 3:

  • Machine Learning Interpretation
  • Interpret CNN models with GradCAM
  • Aggregate and Individual feature importance
  • Permutation Importance
  • Shapley Values
  • Interpret random forest models


Enroll in Course 3

Frequently Asked Questions

Who is the Specialization for?

The AI For Medicine Specialization is for anyone who has a basic understanding of deep learning and wants to apply AI to the medicine space. After taking the Specialization, you could go on to pursue a career in the medical industry as a data scientist, machine learning engineer, innovation officer, or business analyst.

What will I learn in the Specialization?

In Course 1, you’ll learn how AI can help doctors make better medical diagnoses. In Course 2, you’ll learn how AI can improve predictions of patients’ future health. In Course 3, you’ll learn how AI can make better treatment recommendations based on individual patients’ health data.

Are there any prerequisites?

We recommend taking the Deep Learning Specialization first, but it’s not required. You should be able to code in Python and understand statistics and probability. 

How do I take the Specialization?

You can enroll in the Specialization on Coursera’s platform. You will watch videos and complete assignments on Coursera as well.

How long is the Specialization?

It typically takes 3-4 weeks, 4-6 hours per week to complete each course. There will be three courses in the Specialization.

How much does the Specialization cost?

The Specialization costs $49/month. You can also purchase each course for $49.

How do I audit the Specialization?

You can audit the Specialization for free by going to the homepage of the course, clicking “Enroll,” and clicking “audit” at the bottom of the window. Note that you will not receive a certificate at the end of the course if you choose to audit.

Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee. You can apply for it by going to the Coursera course page and clicking on the Financial Aid link beneath the “Enroll” button on the left.
Will I receive a certificate at the end of the Specialization?

You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you would have to repurchase the course. If you audit the course for free, you will not receive a certificate. 

Once all three courses in the Specialization are released and you are subscribed to the Specialization, you will also receive a certificate for the Specialization if you complete all three courses.

Is this a stand-alone course or a Specialization?

This is a Specialization made up of multiple courses. You can take the first two courses now on Coursera. Course 3 will be released by the end of May 2020.

About the Instructors

Pranav Rajpurkar is a 5th year PhD candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. His research interest is in building artificial intelligence (AI) technologies to tackle real world problems in medicine. His PhD work has led to the development of AI technologies for clinical medicine (CheXNet), and large datasets that have facilitated advancements of AI technologies in both medicine (CheXpert) and natural language processing systems (SQuAD). His long term mission is to build AI technologies that will be used routinely for diagnosis, prognosis, and treatment of patients.

Andrew Ng is a global leader in AI and co-founder of Coursera. Dr. Ng is also the CEO and founder of and founder of Landing AI. He is an Adjunct Professor in the Computer Science Department at Stanford University. 

He was until recently Chief Scientist at Baidu, where he was responsible for driving the company’s global AI strategy and infrastructure. He was also the founding lead of the Google Brain team. Dr. Ng has authored or co-authored over 100 research papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.

We use cookies to collect information about our website and how users interact with it. We’ll use this information solely to improve the site. You are agreeing to consent to our use of cookies if you click ‘OK’. All information we collect using cookies will be subject to and protected by our Privacy Policy, which you can view here.