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Learner Reviews & Feedback for AI for Medical Diagnosis by DeepLearning.AI

1,819 ratings

About the Course

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 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....

Top reviews


Jul 2, 2020

It was a nice course. Though it covers basics. A follow-up advanced specilization can be made. Overall, it's sufficient for beginner for an engineer trying to learn application of AI for medical field


May 26, 2020

Throughout this course, I was able to understand the different medical and deep learning terminology used. Definitely a good course to understand the basic of image classification and segmentation!

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351 - 375 of 386 Reviews for AI for Medical Diagnosis

By Borun C

Feb 17, 2021

The course is useful but the grading is terrible. In case of testing the code based on test cases, the grader looks into the code and only passes the code if it's written in a way consistent with the hints. This means that vectorized computations are out and one has to implement loops by hand. Furthermore, since its not clear what the grader is complaining about, one ends up wasting a lot of time if one really cares about "completing" the course. Furthermore, despite a lot of complaints the instructors have not fixed this issue.

By Karen F

Aug 14, 2021

What I am finding in most Coursera courses: 1) Important topics are just glossed over in often < 3 minutes. That itself make me wonder if the courses are worth the price even.

2) Code assignments seem to take most time figuring out what the provided code does, and how I am expected to fill in a few blanks. And after completion I am far from being able to, for example, build a basic x-ray classifier from beginning to end.

By Kenny F C

Oct 13, 2021

As other reviews have mentioned, though the course introduces important concepts for evaluating models in a medical context (confusion matrix, ROC curves), the concepts and exercises were too basic and surface level. Keep in mind the medical context is solely from the point of view of medical imaging. The autograder was also buggy and I was unable to start new topics in Discourse to ask questions about it.

By Jakub V

May 11, 2020

This is interesting topic and I learnt how these things are done in medicine. However, from technical point of view, there are many issues. Bugs, typos, unexplained terms (dear learner, now please calculate background ratio) make this course messy and leaves the taste of "rushed product of corona crisis".

By Volodymyr F

Apr 22, 2020

The course is very shallow. It explains in detail some simple concepts like Sensitivity and Specificity and then immediately touches complex topics like image recognition architectures, without much explanation. The course materials are unclear and the auto-grader is buggy.

By Sadra H

Jan 2, 2023

The course was awesome and practical. I used the content so much in my work. However, there were some errors in the assignments or the labs which made me a little confused. It would be better to fix them in order to clarify everything in the course.

By Amina K

May 3, 2020

Instructions in the graded assignment did not have clear instructions. Sometimes, correct implementation was graded 'incorrect' by the grader. Also, videos of the ROC curve was not clear about why it is needed or what does it say about a model.

By Tasneem. A

Apr 25, 2020

Hi Sir/Madam,

i took this course then realised it is beyond my understanding. I am a grade 12 student . please help me to cancel this ,so, i can take another course which can benefit me.

i will appreciate your help.

thanking you

By Shahzad H

Mar 7, 2023

The final assignments should included end to end projects like e.g. hypertuning of parameters.

Most of the utility code especially for understanding the entire image from patch is not clear and should be explained separately.

By Subair A

Jun 5, 2020

Too much task was given but less explanation. It was really hard to complete all the tasks. It would be better if easiest tasks are given or more explanation with huge explanation.

By Ravi C

Apr 25, 2021

Expected content that would be new but found content which I was already familiar with. Disappointed a little on that. Course could have some more interesting and new content.

By Harit J

May 18, 2020

Good instructor but concepts were not taught in-depth. The assignments gave only a superficial understanding of the subject and cannot prepare one for working in the industry.

By nithin s

May 8, 2020

The course touches on several aspects of ML for medical. However, the content seems too little and narrow. Only a few cases and architectures are explored.

By Sundeep L

May 1, 2020

Would like it if the projects were more in-depth. We should understand the end-to-end pipeline: from preprocessing to deploying in production

By Laurin R

Jan 3, 2021

Some concepts used in the assignments are note explained in the videos e. g. the calculation of AUC.

By Marcel L

Mar 7, 2022

Its a good introduction course, but it spends alot of time going over a lot general concepts.

By Pedro G

Nov 19, 2021

The lectures are good, but I experienced many issues on homeworks.

By Thiago M d O

Jul 21, 2020

Content is too shallow, could have gone deeper into some topics.

By mohit r

Jun 21, 2020

The codes should have be explained ...

By Andrey A

Aug 22, 2020

Too general for practical usage

By Hanan S A

Jul 13, 2020

good as an introduction

By Matthias K

May 7, 2020

Fairly shallow.

By Apoorv G

Aug 8, 2020

I first took Deep Learning Specialization by Andrew and then took NLP specialization by Younes Mourri and then this course. One difference I noticed that Andrew explained all the stuff by himself in detailed 8-10 min video and here these in these two coursers, the two instructor explained concept in 1-2 min video and left the remaining concept to learned by ourselves through notebooks. Andrew put much more effort than these two guys.

By Michael L

Jan 9, 2021

Did a good job of explaining some of the terms and processes involved in using AI for medical diagnosis, but the flow and organization of the course were really poor and the methods taught were not general enough to be able to extrapolate to use in new ways outside of the course.