AI for Medical Diagnosis from Coursera

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.

What's inside

Syllabus

Disease Detection with Computer Vision

By the end of this week, you will practice classifying diseases on chest x-rays using a neural network.

Evaluating Models

By the end of this week, you will practice implementing standard evaluation metrics to see how well a model performs in diagnosing diseases.

Image Segmentation on MRI Images

By the end of this week, you will prepare 3D MRI data, implement an appropriate loss function for image segmentation, and apply a pre-trained U-net model to segment tumor regions in 3D brain MRI images.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores modern medicine use cases with AI techniques like image recognition and NLP, developing practical skills for healthcare practitioners

Taught by recognized faculty in the field of AI, including Pranav Rajpurkar and Eddy Shyu, who are known for their contributions to AI and healthcare

Develops foundational knowledge in deep learning and Python programming, making it suitable for learners already familiar with the basics of AI algorithms

Practical focus with hands-on labs and interactives in each module

Requires a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level

Reviews summary

Ai for medical diagnosis: a comprehensive course

learners say this course delves into the practical uses of AI for diagnosing medical conditions. It begins with the basics of medical image analysis and classification, then moves on to more advanced topics like segmentation and U-net modeling. The course is well-structured and includes plenty of hands-on exercises and assignments to help learners practice what they learn. It's a great option for anyone who wants to learn more about AI in medicine, whether they're a medical professional or a data scientist.

Taught by industry experts with extensive experience in AI and medical diagnosis.

Assumes no prior knowledge of medical imaging or AI, making it accessible to learners from diverse backgrounds.

Offers numerous hands-on exercises and assignments to reinforce learning.

"The course is well-structured and includes plenty of hands-on exercises and assignments to help learners practice what they learn."

Covers practical aspects of AI in medical diagnosis, including image analysis, classification, and segmentation.

"learners say this course delves into the practical uses of AI for diagnosing medical conditions."

"It begins with the basics of medical image analysis and classification, then moves on to more advanced topics like segmentation and U-net modeling."

Learners have encountered grading issues, specifically with the last coding challenge not considering special cases.

"The grading of the last coding challenge does not respect the special cases that are explicitly mentioned in the excercise itself."

Some learners have reported outdated package versions and materials, which can hinder the learning experience.

"The coding challenges were using badly outdated package versions, for which documentation does not exist anymore and which do not represent best practice usage of the libraries involved."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in AI for Medical Diagnosis with these activities:

Read 'Deep Learning for Healthcare' by Pearson

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Enhance your foundation in deep learning applications within healthcare.

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Obtain a copy of 'Deep Learning for Healthcare' by Pearson
Read and take notes on the chapters relevant to the course
Complete the exercises and assignments included in the book

Create a comprehensive study guide for the course

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Enhance your understanding of course concepts by organizing and summarizing the provided materials.

Browse courses on Study Guide

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Gather and organize course materials (lectures, assignments, readings)
Summarize and condense the key points from each module
Create practice questions and exercises

Review image segmentation concepts

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Recall the foundational image segmentation concepts you need for this course to boost your understanding.

Browse courses on Image Segmentation

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Read an introductory article on image segmentation
Review a tutorial on CNNs for image segmentation

Five other activities

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Attend a workshop on risk modeling in healthcare

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Enhance your understanding of risk modeling techniques used in healthcare through expert-led guidance.

Browse courses on Risk Modeling

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Identify a relevant workshop on risk modeling in healthcare
Register and attend the workshop
Actively participate in the workshop sessions

Develop a medical image classification model with CNNs

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Build a practical AI model to apply your understanding of CNNs in medical image analysis.

Browse courses on CNNs

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Gather a dataset of medical images
Preprocess and label the images
Design and implement a CNN model for image classification
Train and evaluate the model
Make predictions using the trained model

Solve coding challenges on Kaggle related to medical data analysis

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Sharpen your coding skills and apply your knowledge to real-world medical data analysis problems.

Browse courses on Coding Challenges

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Sign up for Kaggle
Find a relevant coding challenge related to medical data analysis
Develop and submit your solution
Review and learn from feedback on your solution

Join an AI-focused study group or online forum

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Connect with peers and experts to share knowledge, get feedback, and enhance your learning experience.

Browse courses on Mentorship

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Identify and join a relevant AI-focused study group or online forum
Actively participate in discussions and share your insights
Offer help and support to other members of the group

Design a treatment plan for a specific disease using NLP

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Demonstrate your ability to apply NLP techniques to analyze medical data and make informed treatment decisions.

Browse courses on Natural Language Processing (NLP)

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Select a specific disease and gather relevant medical data
Use NLP techniques to extract insights from the medical data
Develop a treatment plan based on the extracted insights
Document and present your treatment plan

Career center

Learners who complete AI for Medical Diagnosis will develop knowledge and skills that may be useful to these careers:

Healthcare AI Engineer

Healthcare AI Engineers are the engine behind the growth of artificial intelligence in the healthcare field. They work to create and improve AI algorithms and models to analyze medical data, diagnose diseases, and predict patient outcomes. This course in AI for Medical Diagnosis can be a helpful tool for those looking to break into this expanding field by providing solid foundational knowledge in applying AI to the medical field.

See salaries and explore the career path for Healthcare AI Engineer

Medical Data Scientist

Medical Data Scientists are responsible for collecting, analyzing, and interpreting medical data. They use this data to develop new AI algorithms and models to improve patient care. Those interested in pivoting to become Medical Data Scientists may find that this course helps build a foundation for working with medical data and applying AI to solve problems in the medical field.

See salaries and explore the career path for Medical Data Scientist

Radiologist

Radiologists use imaging techniques such as X-rays, MRIs, and CT scans to diagnose and treat diseases. This course in AI for Medical Diagnosis can be a useful tool for Radiologists looking to expand their knowledge and skills in using AI to analyze medical images.

See salaries and explore the career path for Radiologist

Medical Physicist

Medical Physicists apply the principles of physics to medicine. They work to develop and improve medical imaging techniques and radiation therapy treatments. Those interested in becoming Medical Physicists will find this course to be helpful in understanding the use of AI in medical imaging and radiation therapy.

See salaries and explore the career path for Medical Physicist

Biostatistician

Biostatisticians use statistical methods to analyze medical data. They work to design and conduct clinical trials, and to develop new statistical methods for medical research. Those interested in becoming Biostatisticians may find this course to be helpful in understanding the use of AI in medical data analysis.

See salaries and explore the career path for Biostatistician

Clinical Research Associate

Clinical Research Associates work with doctors and other healthcare professionals to design and conduct clinical trials. They are responsible for collecting and analyzing data, and for ensuring that the trial is conducted in a safe and ethical manner. Those interested in becoming Clinical Research Associates may find this course to be helpful in understanding the use of AI in clinical research.

See salaries and explore the career path for Clinical Research Associate

Health Informatics Specialist

Health Informatics Specialists use data to improve the quality and efficiency of healthcare. They work with doctors and other healthcare professionals to develop and implement new technologies, such as electronic health records and telemedicine systems. Those interested in becoming Health Informatics Specialists may find this course to be helpful in understanding the use of AI in healthcare.

See salaries and explore the career path for Health Informatics Specialist

Healthcare Consultant

Healthcare Consultants work with healthcare organizations to improve their operations and efficiency. They may also work with patients to help them navigate the healthcare system. Those interested in becoming Healthcare Consultants may find this course to be helpful in understanding the use of AI in healthcare.

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Medical Writer

Medical Writers create written content for the healthcare industry. They may write articles for medical journals, patient education materials, or marketing materials. Those interested in becoming Medical Writers may find this course to be helpful in understanding the use of AI in healthcare.

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Healthcare Administrator

Healthcare Administrators oversee the day-to-day operations of healthcare organizations. They may also work with doctors and other healthcare professionals to develop and implement new policies and procedures. Those interested in becoming Healthcare Administrators may find this course to be helpful in understanding the use of AI in healthcare.

See salaries and explore the career path for Healthcare Administrator

Pharmacist

Pharmacists dispense medications and provide advice on their use. They may also work with doctors and other healthcare professionals to develop and implement new drug therapies. Those interested in becoming Pharmacists may find this course to be helpful in understanding the use of AI in healthcare.

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Nurse

Nurses provide direct patient care and work with doctors and other healthcare professionals to develop and implement treatment plans. Those interested in becoming Nurses may find this course to be helpful in understanding the use of AI in healthcare.

See salaries and explore the career path for Nurse

Medical Laboratory Technician

Medical Laboratory Technicians perform tests on blood, urine, and other bodily fluids to help diagnose and treat diseases. Those interested in becoming Medical Laboratory Technicians may find this course to be helpful in understanding the use of AI in healthcare.

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Medical Assistant

Medical Assistants provide administrative and clinical support to doctors and other healthcare professionals. Those interested in becoming Medical Assistants may find this course to be helpful in understanding the use of AI in healthcare.

See salaries and explore the career path for Medical Assistant

Health Educator

Health Educators teach people about health and wellness. They may work in schools, community centers, or hospitals. Those interested in becoming Health Educators may find this course to be helpful in understanding the use of AI in healthcare.

See salaries and explore the career path for Health Educator

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Reviews summary

Ai for medical diagnosis: a comprehensive course

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