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Tina Hernandez-Boussard
and
Mildred Cho
With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.
Read more
With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.
Read more
With artificial intelligence applications proliferating throughout the healthcare system, stakeholders are faced with both opportunities and challenges of these evolving technologies. This course explores the principles of AI deployment in healthcare and the framework used to evaluate downstream effects of AI healthcare solutions.
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; 3) Disclosure of financial relationships for every person in control of activity content.
Register for this course and see more details by visiting:
OpenCourser.com/course/uk6tqg/evaluations
What's inside
Syllabus
AI in Healthcare
Evaluations of AI in Healthcare
AI Deployment
Read more
Syllabus
AI in Healthcare
Evaluations of AI in Healthcare
AI Deployment
Read more
Downstream Evaluations of AI in Healthcare: Bias and Fairness
The Regulatory Environment for AI in Healthcare
Best Ethical Practices for AI in Health Care
Readings related to best ethical practices for AI in health care
AI and Medicine (Optional Content)
Course Wrap Up
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Provides principles for deploying AI in the healthcare system and methodologies to evaluate implementation effects
Covers the regulatory environment for AI in healthcare
Examines bias and fairness in AI healthcare solutions
Suitable for intermediate learners interested in AI applications in healthcare
Requires a fundamental understanding of AI
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Reviews summary
Ai healthcare evaluation
According to students, Evaluations of AI Applications in Healthcare is an engaging course that provides a comprehensive overview of the process of evaluating and implementing AI solutions in healthcare. Learners say that the course covers a wide range of topics, including AI model development, clinical validation, deployment, and monitoring. They also appreciate the inclusion of ethical considerations and regulatory aspects of AI in healthcare. Overall, students find the course to be well-structured and informative, with clear and concise explanations of complex concepts.
Knowledgeable instructor
"The instructress was very clear in articulating concepts."
"The instructor was learned and charismatic."
Well-structured and organized
"This course was really valuable for linking and embedding my knowledge gained by reading FDA guidance documents and knowledge sharing from my Quality Assurance and Regulatory Affairs colleagues"
Inclusion of ethical considerations
"This is an excellent course on the regulatory and operational considerations of AI solutions in the medical field."
Comprehensive and informative
"This course contains an extraordinary amount of considerations and information critical to the process of development and deployment of AI Applications in Healthcare."
"The course was extremely helpful in understand the principles and guidelines to keep in mind while talking an AI model into the real world."
Errors in exams and technical issues
"There are several mistakes in the final exam questions."
"the content was really interesting, but it seemed it the way it was given was not interactive, it was just like "reading slides" and the videos had flaws at some points (glitches)."
Some repetition in early lessons
"Useful content, but there is a lot of repetition early in the course."
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 Evaluations of AI Applications in Healthcare with these
activities:
Review fundamental concepts of artificial intelligence
Show steps
Reviewing the fundamental concepts of artificial intelligence will provide a strong foundation for this course.
Show steps
-
Review the history of AI
-
Identify the different types of AI
-
Understand the limitations of AI
Review Healthcare AI
Show steps
Refresh your foundation before the course officially begins to flag any areas you may want to focus on during the semester.
Show steps
-
Review previous notes and coursework
-
Take practice questions or quizzes
Watch tutorials on AI applications in healthcare
Show steps
Watching tutorials will provide you with practical examples of how AI is being used in healthcare.
Browse courses on
AI in Healthcare
Show steps
-
Find tutorials on AI applications in healthcare
-
Watch the tutorials and take notes
-
Apply what you have learned to your own projects
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve practice problems on AI algorithms
Show steps
Solving practice problems will help strengthen your understanding of AI algorithms and techniques.
Browse courses on
AI Algorithms
Show steps
-
Identify the type of AI algorithm being used
-
Apply the algorithm to solve a problem
-
Evaluate the results of the algorithm
Ethics case studies
Show steps
Apply the ethical principles you learn in the course through case studies to begin solidifying knowledge.
Browse courses on
AI Ethics
Show steps
-
Read case study of AI in healthcare
-
Identify ethical issues
-
Propose a solution
AI bias evalutations
Show steps
Practice what you learn through specific exercises in detecting and evaluating AI bias.
Browse courses on
AI Bias
Show steps
-
Review concepts of bias in AI
-
Simulate AI bias in datasets
-
Evaluate bias and propose solutions
Review the book Artificial Intelligence in Healthcare
Show steps
The book covers the latest developments in AI in healthcare and provides a comprehensive overview of the field.
View
Business Intelligence, Analytics, Data Science,...
on Amazon
Show steps
-
Read the book and take notes
-
Summarize the key concepts of each chapter
-
Apply what you have learned to your own projects
Develop a AI healthcare solution to a real-world problem
Show steps
Developing an AI healthcare solution will allow you to apply your knowledge and skills to a real-world problem.
Show steps
-
Evaluate the results of the AI solution
-
Identify a real-world healthcare problem
-
Design an AI solution to the problem
-
Implement the AI solution
-
Present your findings to others
Review fundamental concepts of artificial intelligence
Show steps
Reviewing the fundamental concepts of artificial intelligence will provide a strong foundation for this course.
Show steps
Show steps
Show steps
- Review the history of AI
- Identify the different types of AI
- Understand the limitations of AI
Review Healthcare AI
Show steps
Refresh your foundation before the course officially begins to flag any areas you may want to focus on during the semester.
Show steps
Show steps
Show steps
- Review previous notes and coursework
- Take practice questions or quizzes
Watch tutorials on AI applications in healthcare
Show steps
Watching tutorials will provide you with practical examples of how AI is being used in healthcare.
Browse courses on
AI in Healthcare
Show steps
Show steps
Show steps
- Find tutorials on AI applications in healthcare
- Watch the tutorials and take notes
- Apply what you have learned to your own projects
Five other activities
Expand to see all activities and additional details
Show all eight activities
Solve practice problems on AI algorithms
Show steps
Solving practice problems will help strengthen your understanding of AI algorithms and techniques.
Browse courses on
AI Algorithms
Show steps
Show steps
Show steps
- Identify the type of AI algorithm being used
- Apply the algorithm to solve a problem
- Evaluate the results of the algorithm
Ethics case studies
Show steps
Apply the ethical principles you learn in the course through case studies to begin solidifying knowledge.
Browse courses on
AI Ethics
Show steps
Show steps
Show steps
- Read case study of AI in healthcare
- Identify ethical issues
- Propose a solution
AI bias evalutations
Show steps
Practice what you learn through specific exercises in detecting and evaluating AI bias.
Browse courses on
AI Bias
Show steps
Show steps
Show steps
- Review concepts of bias in AI
- Simulate AI bias in datasets
- Evaluate bias and propose solutions
Review the book Artificial Intelligence in Healthcare
Show steps
The book covers the latest developments in AI in healthcare and provides a comprehensive overview of the field.
View
Business Intelligence, Analytics, Data Science,...
on Amazon
Show steps
Show steps
Show steps
- Read the book and take notes
- Summarize the key concepts of each chapter
- Apply what you have learned to your own projects
Develop a AI healthcare solution to a real-world problem
Show steps
Developing an AI healthcare solution will allow you to apply your knowledge and skills to a real-world problem.
Show steps
Show steps
Show steps
- Evaluate the results of the AI solution
- Identify a real-world healthcare problem
- Design an AI solution to the problem
- Implement the AI solution
- Present your findings to others
Career center
Learners who complete Evaluations of AI Applications in Healthcare will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
An AI Engineer works in the overlap of AI, software, and business domains. They take AI research from the theoretical and academic space and translate it into real-world applications. Graduates of the Evaluations of AI Applications in Healthcare course could bring about an understanding of AI deployment in healthcare, downstream evaluations, and best ethical practices for AI in health care. These skills would be crucial for an AI Engineer looking to apply their skills to the healthcare field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers leverage specialized knowledge to build scalable AI applications. They are responsible for the design, development, deployment, and maintenance of the AI models. They may also research, design, and implement AI or machine learning algorithms. Graduates of the Evaluations of AI Applications in Healthcare course may benefit from a deeper look into the evaluations and ethical applications of AI in healthcare, which would complement their skillset.
Data Scientist
Data Scientist
A Data Scientist collects, analyzes, and interprets large amounts of data. They typically have a deep understanding of statistics, programming, and machine learning. Graduates of the Evaluations of AI Applications in Healthcare course may use this course as a starting point for understanding how to evaluate downstream effects of AI healthcare solutions and what downstream biases and fairness concerns may exist.
Healthcare Consultant
Healthcare Consultant
Healthcare Consultants help healthcare organizations improve their performance. They may work on a variety of projects, such as improving patient care, reducing costs, or implementing new technologies. Graduates of the Evaluations of AI Applications in Healthcare course would bring about a deeper understanding of AI applications in healthcare, as well as an understanding of how to evaluate and deploy AI in healthcare settings.
Software Engineer
Software Engineer
Software Engineers design, develop, test, and maintain software systems. They may work on a variety of projects, from small personal apps to large enterprise systems. Graduates of the Evaluations of AI Applications in Healthcare course may find success working on the software side of AI in healthcare applications, where they would need to be aware of AI deployment, downstream evaluations, and regulatory environment.
Healthcare Administrator
Healthcare Administrator
Healthcare Administrators plan, direct, and coordinate medical and health services. They may work in a variety of settings, such as hospitals, clinics, and nursing homes. Graduates of the Evaluations of AI Applications in Healthcare course may use this course as a starting point to take on more technological leadership and management roles in the healthcare field.
Public Health Analyst
Public Health Analyst
Public Health Analysts collect, analyze, and interpret data to identify and address public health problems. They may work on a variety of projects, such as preventing disease outbreaks, promoting healthy behaviors, and improving access to care. Graduates of the Evaluations of AI Applications in Healthcare course may find success in analyzing and interpreting data related to AI in healthcare and its impact on the public health landscape at large.
Biostatistician
Biostatistician
Biostatisticians apply statistical methods to data in the field of biology. They may work on a variety of projects, such as designing clinical trials, analyzing medical data, and developing statistical models. Graduates of the Evaluations of AI Applications in Healthcare course may use this course to build a foundation in understanding AI in healthcare so that they can better apply statistical methods to evaluate and understand AI in healthcare applications.
Epidemiologist
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems in populations. They may work on a variety of projects, such as tracking disease outbreaks, identifying risk factors for disease, and developing prevention strategies. Graduates of the Evaluations of AI Applications in Healthcare course may find success learning more about how AI and other technology can be used to improve how healthcare is delivered during an outbreak.
Medical Writer
Medical Writer
Medical Writers create and edit written materials about medical topics. They may work for a variety of organizations, such as pharmaceutical companies, medical journals, and government agencies. Graduates of the Evaluations of AI Applications in Healthcare course may find success in creating and editing technical documentation, white papers, and other written materials on AI applications in healthcare.
Healthcare Policy Analyst
Healthcare Policy Analyst
Healthcare Policy Analysts analyze and develop policies that affect the healthcare system. They may work for a variety of organizations, such as government agencies, think tanks, and advocacy groups. Graduates of the Evaluations of AI Applications in Healthcare course may find success analyzing and evaluating the evolving healthcare policies surrounding AI applications in healthcare.
Clinical Research Coordinator
Clinical Research Coordinator
Clinical Research Coordinators manage the day-to-day operations of clinical research studies. They may work on a variety of projects, such as recruiting patients, collecting data, and ensuring compliance with regulations. Graduates of the Evaluations of AI Applications in Healthcare course would gain knowledge of AI in healthcare and its downstream effects in a clinical research setting.
Health Educator
Health Educator
Health Educators develop and implement educational programs to promote healthy behaviors. They may work in a variety of settings, such as schools, community centers, and hospitals. Graduates of the Evaluations of AI Applications in Healthcare course may find success using their knowledge of AI in healthcare to build and implement educational programs on the use and benefit of AI in healthcare settings.
Medical Librarian
Medical Librarian
Medical Librarians help people find and use medical information. They may work in a variety of settings, such as hospitals, clinics, and universities. Graduates of the Evaluations of AI Applications in Healthcare course may find success learning about AI and staying abreast of AI's impact on healthcare information systems.
Healthcare Information Manager
Healthcare Information Manager
Healthcare Information Managers plan, direct, and coordinate the management of health information. They may work in a variety of settings, such as hospitals, clinics, and insurance companies. Graduates of the Evaluations of AI Applications in Healthcare course may find success working on projects related to AI applications in healthcare, specifically on how healthcare information is collected, stored, and utilized.
For more career information including salaries, visit:
OpenCourser.com/course/uk6tqg/evaluations
Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Evaluations of AI Applications in Healthcare.
Save
Written by Eric Topol, a renowned cardiologist, this book offers insights and predictions regarding the role of AI in healthcare. It covers a range of topics including the use of AI in diagnostics, personalized medicine, and drug discovery.
Discusses the ethical and regulatory implications of AI in healthcare, including topics like bias, transparency, and accountability.
Provides a comprehensive overview of machine learning techniques commonly used in healthcare informatics. It includes practical guidance on data preparation, model selection, and evaluation methods.
A comprehensive overview of the use of machine learning in healthcare, covering the fundamentals of machine learning, applications in clinical practice, and ethical considerations.
Offers a practical overview of AI in medicine, covering applications in diagnosis, treatment, and patient management.
Examines the opportunities and challenges of AI in healthcare, addressing its potential impact on patient care, healthcare delivery, and the healthcare workforce.
Save
As a textbook in health informatics, this book provides a solid foundation in the field. It covers topics such as data management, information systems, and the use of informatics in healthcare.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/uk6tqg/evaluations
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Level
Introductory
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Coursera
Institution
Stanford University
Instructors
Tina Hernandez-Boussard
Mildred Cho
Language
English
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آذربایجان دیلی
This course belongs to a
collection
called
AI in Healthcare. It's comprised of five courses:
- AI in Healthcare Capstone
- Introduction to Healthcare
- Evaluations of AI Applications in Healthcare (viewing)
- Fundamentals of Machine Learning for Healthcare
- Introduction to Clinical Data
Good to know
Provides principles for deploying AI in the healthcare system and methodologies to evaluate implementation effects
Covers the regulatory environment for AI in healthcare
Examines bias and fairness in AI healthcare solutions
Suitable for intermediate learners interested in AI applications in healthcare
Requires a fundamental understanding of AI
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