Learner Reviews & Feedback for Evaluations of AI Applications in Healthcare by Stanford University

More examples would have been better to understand some of the concepts.

Excellent, methodical coverage of the topics involved. Great course!

Good course on an important but fairly dry topic. The exams and practice questions were filled with typos and some mistakes unfortunately. More interactivity would be appreciated for properly learning the material.

Interesting subject, and very knowledgeable instructor, but the lectures felt like a lot of course materials distilled into walls of text and a lot of bullet points, with whole chapters just explained with a sentence or two. The "reflection questions" also felt like just repeating the bullet points. May be more reflective of me as a student than the course, but nevertheless. I would have preferred a smaller number of mental frameworks or acronyms to learn and more in-depth instead with real-life examples. There was also an error with the final quiz. Questions 5 and 7 were identical, I gave the same answer but one was rated correct and one incorrect:

Question 5

True or False - AI Solutions that involve an operational action paired with the outcome have greater clinical benefit thank those that have a medical action paired with the outcome

Answer: False.

Correct

Both can be very beneficial

Question 7

True or False - AI Solutions that involve an operational action paired with the outcome have greater clinical benefit thank those that have a medical action paired with the outcome

Answer: False.

Incorrect

Both can be very beneficial

This is an excellent course on the regulatory and operational considerations of AI solutions in the medical field. Forums, threads, and collaboration with cohorts are disabled (not good). Big negative are the constant social justice warrior tropes, unconscious bias, racial injustice, income inequality, NY Times view of the world, etc. that take away from the value of the material which is extremely good otherwise. There are problems with audio, tests, and the use of paywall restricted content. Highly recommended, but be ready for a heavy dose of politics with your medicine. I have not seen this material elsewhere and it is a very time-efficient course.

The course has abundant information to evaluate AI applications in Healthcare. Thanks to the instructor who gave clear guidance on the whole product life cycle of AI application. The course includes AI model setup and development (data management, model training, model validation), clinical validation and AI application deployment and AI monitoring, regulations. The well prepared instruction material is very impressive. Really enjoy the learning. Would recommend to others.

The instructress was very clear in articulating concepts. Again, pace of course is considered fast and for me. While I passed, this is only my beginning. Will need to work on the job to fully appreciated the concepts. Nonetheless, this has been a very good course that challenges a broader thinking before one jumps onto the bandwagon.

The course was extremely helpful in understand the principles and guidelines to keep in mind while talking an AI model into the real world. It was well structured, with enough depth to understand the basics while also giving me topics that I'd want to further dive into

Una visión muy amplia con el detalle adecuado para poder preparar proyectos con rigor y en condiciones de poder extender su estudio a uso clínico

Thanks a lot for this great course, extremely practical, allowing to create a clear action plan for AI evaluation.

Nicely Framed and Executed in a simple language so anyone can catch up earliest.

I was expecting the Medical genetics professor as a teacher also.

This course was really fulfiling and I enjoyed it very much.

Outstanding informational content and operating platform!

Through and engaging presentation. Thank you

Best course in the program so far.

Good course for PM's :)

This course contains an extraordinary amount of considerations and information critical to the process of development and deployment of AI Applications in Healthcare. The topics include Evaluation Frameworks, Deployment Methodologies, Regulatory considerations, key considerations of fairness and bias in AI Applications, and Ethical considerations in AI. In many ways, any organization that intends to or is in the process of developing AI Applications should take these topics into consideration even before development begins. The material provided includes references to helpful frameworks and guidelines, which if used in the initial stages of the life cycle of an AI Application, would probably help reduce lead time, as also enable the deployment usage of the application in actual healthcare settings.

too many theories but no practica examples or exercises

While a great deal of issues where covered, they where all mentioned as of equal significance. It is not easy to follow which are fundamentals and which issues follow as next. There where not enough emphasis given on the main challenges. Looked like a machine gun shooting info. At the end I was just able to remember a small piece of all that.