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AI for Medical Diagnosis

Approx. 19 hours to complete

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Course Summary

This course teaches the application of AI in medical diagnosis, including the use of image analysis, natural language processing, and clinical data analysis.

Key Learning Points

Learn to apply AI to diagnose diseases using medical imaging and other clinical data
Understand how to evaluate the performance of AI models in medical diagnosis
Explore ethical considerations and limitations of using AI in medical diagnosis

Job Positions & Salaries of people who have taken this course might have

Medical Imaging Data Analyst
- USA: $85,000 - $110,000
- India: INR 5,00,000 - INR 8,00,000
- Spain: €30,000 - €45,000
Clinical Data Scientist
- USA: $100,000 - $130,000
- India: INR 6,00,000 - INR 10,00,000
- Spain: €35,000 - €50,000
AI Medical Diagnosis Researcher
- USA: $120,000 - $160,000
- India: INR 8,00,000 - INR 14,00,000
- Spain: €50,000 - €70,000

Learning Outcomes

Understand the fundamentals of applying AI in medical diagnosis
Learn how to train and evaluate AI models for medical diagnosis
Explore ethical and practical considerations in using AI for medical diagnosis

Prerequisites or good to have knowledge before taking this course

Basic knowledge of machine learning and Python programming
Familiarity with medical terminology and concepts

Course Difficulty Level

Intermediate

Course Format

Self-paced
Online
Video lectures
Assignments and quizzes

Similar Courses

AI for Medical Prognosis
AI in Healthcare
Machine Learning for Healthcare

Related Education Paths

Notable People in This Field

Eric Topol
Fei-Fei Li

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. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine. If you're already familiar with some of the math and coding behind AI algorithms, and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!

This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: - In Course 1, you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders. - In Course 2, you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. - In Course 3, you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases. As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don't need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential. If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially. Join us in this specialization and begin your journey toward building the future of healthcare.

Outline

Disease detection with computer vision
Welcome to the Specialization with Andrew and Pranav
Demo
Recommended prerequisites
Medical Image Diagnosis
Eye Disease and Cancer Diagnosis
Building and Training a Model for Medical Diagnosis
Training, prediction, and loss
Image Classification and Class Imbalance
Binary Cross Entropy Loss Function
Impact of Class Imbalance on Loss Calculation
Resampling to Achieve Balanced Classes
Multi-Task
Multi-task Loss, Dataset size, and CNN Architectures
Working with a Small Training Set
Generating More Samples
Model Testing
Splitting data by patient
Sampling
Ground Truth and Consensus Voting
Additional Medical Testing
Connect with your mentors and fellow learners on Slack
About the automatic grader
How to refresh your workspace
Week 1 Quiz: Disease detection with computer vision

Evaluating models
Sensitivity, Specificity, and Evaluation Metrics
Accuracy in terms of conditional probability
Sensitivity, Specificity and Prevalence
PPV, NPV
Confusion matrix
ROC curve and Threshold
Varying the threshold
Sampling from the Total Population
Confidence intervals
95% Confidence interval
Calculating PPV in terms of sensitivity, specificity and prevalence

Image segmentation on MRI images
Medical Image Segmentation
MRI Data and Image Registration
Segmentation
2D U-Net and 3D U-Net
Data augmentation for segmentation
Loss function for image segmentation
Different Populations and Diagnostic Technology
External validation
Measuring Patient outcomes
Congratulations!
Convolutional Neural networks
More about U-Net (Optional)
Acknowledgements
Citations

Summary of User Reviews

The AI for Medical Diagnosis course on Coursera has received positive reviews from users. The course has been praised for its practical applications and real-world examples. Users have found the course to be informative and engaging, with a good balance of theory and practice. The overall rating of the course is high, indicating that it is a valuable resource for those interested in applying AI to medical diagnosis.

Key Aspect Users Liked About This Course

The practical applications and real-world examples provided in the course have been praised by many users.

Pros from User Reviews

Informative and engaging course material
Good balance of theory and practice
Practical applications and real-world examples
Taught by experienced instructors
Valuable resource for those interested in applying AI to medical diagnosis

Cons from User Reviews

Some users have found the course material to be too technical
The course may be too basic for those with a strong background in AI or medical diagnosis
Limited opportunities for interaction with instructors or other students
Some users have reported technical difficulties with the platform
The course may require a significant time commitment

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