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

1,586 ratings
348 reviews

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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301 - 325 of 348 Reviews for AI for Medical Diagnosis

By Milan G

May 25, 2021

Learned a lot from the course, especially the assignments that helped to translate knowledge into action.

By Leon C

May 16, 2020

Very interesting theory. It could be more in-depth, and the notebooks with autograder contain some bugs.

By Aldemár F V

Oct 16, 2020

Bueno el curso, los materiales. Por mejorar, que no hay buen seguimiento del avance de los estudiantes.

By Faisal R

May 6, 2020

great course But not For the Pharmacy Student because You have to Know The Code Knowledge

By TheBlue P

Jul 21, 2020

The last coding exercise is quite lengthy and difficult, and the videos are too short


May 3, 2020

Liked this course. But some topics were not explained well. Like ROC curve

By Alex K

Dec 22, 2020

Great course, I wished some content could have been covered more in depth

By Jay

Jun 28, 2020

Best course for AI for medical diagnosis please do the pre-requisite.

By Abbas A K

Jul 1, 2020

it was better to have more detail of loss functions for segmentation

By Srinadh R B

Jul 5, 2020

As a case study for deep learning, this course helps us a lot.

By Josh B

Dec 16, 2020

They started diving deeper towards the end of the course.

By Zabirul i

Jul 25, 2020

Need to more clarify the notebook content in videos.

By Amir V

Aug 26, 2021

G​ood & practical, but could've been more advanced

By Sakshat R

May 5, 2020

Really nice and well-explained

By Michel F

Jun 7, 2020

Last assignment was insane.

By Huy P

Jul 5, 2020

The problem is quite easy

By Mimi C

Apr 25, 2020

The course is too basic.

By Muntaha S

Nov 10, 2020

excellent course

By 김영석

Jul 18, 2021

goog think

By Abdalkarem I F

Sep 10, 2020


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 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.