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Pranav Rajpurkar,
Bora Uyumazturk,
Amirhossein Kiani,
and
Eddy Shyu
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. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Read more
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. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Read more
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. This Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Medical treatment may impact patients differently based on their existing health conditions. In this third course, you’ll recommend treatments more suited to individual patients using data from randomized control trials. In the second week, you’ll apply machine learning interpretation methods to explain the decision-making of complex machine learning models. Finally, you’ll use natural language entity extraction and question-answering methods to automate the task of labeling medical datasets.
These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend that you take the Deep Learning Specialization.
Register for this course and see more details by visiting:
OpenCourser.com/course/x15pf4/ai
What's inside
Syllabus
Treatment Effect Estimation
In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
Read more
Syllabus
Treatment Effect Estimation
In this week, you will learn: How to analyze data from a randomized control trial, interpreting multivariate models, evaluating treatment effect models, and interpreting ML models for treatment effect estimation.
Read more
Medical Question Answering
In this week, you will learn how to extract disease labels from clinical reports, and also question answering with BERT.
ML Interpretation
In this week, you will learn how to interpret deep learning models, and also feature importance in machine learning.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Develops expertise in applying machine learning to medical use cases
Taught by recognized instructors in the field of medical AI
Examines nuances in medical AI applications
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Reviews summary
Ai in medical treatments
Learners say this outstanding course offers a valuable introduction to real applications of machine learning and deep learning in medical treatments. It is well-structured, engaging, and appropriate for both medical professionals and AI/ML practitioners. Assignments are challenging and practical, with a strong emphasis on implementation rather than abstract theory.
Course emphasizes practical implementation of AI techniques over abstract theory.
"This is more of a course on pandas dataframes and various data preprocessing and formatting tasks than really understanding the functionality of scikit-learn and tensoflow as applied to medical research."
"I'm not saying that it is not important to structure and manipulate the data, but at least equal (in fact more) time should be dedicated to understanding and using the AI tools."
Suitable for learners with diverse backgrounds, including those new to AI and medical treatments.
"This course was very challenging for me."
"However, since I'm not a professional programmer or even an expert in python, the platform and course syllabus was amazing in allowing me to learn more about AI/ML in medicine despite my predominantly medical background."
Clear and concise video lectures and explanations ensure effective learning.
"Great specialization for anyone interested in both AI and medicine."
"You need to know a bit about AI already; not so much about medicine, and there is plenty of supplementary information available anyway."
Engaging assignments provide hands-on experience implementing AI and deep learning techniques.
"Programming assignments really contributed to the understanding of the material."
"The assignment of this course though had some typos/fixes, but was enthralling to solve those ourselves."
Practical course with assignments that cover real applications of AI and deep learning in medicine.
"Uses BERT for question answering with medical reports."
"Applies GradCAM to chest X-rays."
"Builds a treatment model and evaluates its impact."
Some learners found the course to be less organized and comprehensive than previous ones.
"I agree with the comments of many of the other students."
"This third course seemed quite a bit less organized and complete compared to the other weeks."
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 Treatment with these
activities:
Follow Tutorials on Machine Learning Interpretation
Show steps
Expand your knowledge of machine learning interpretation by exploring tutorials and resources on the topic.
Browse courses on
Explainable AI
Show steps
-
Identify reputable sources
-
Select tutorials relevant to the course
-
Follow the tutorials and apply what you learn
Design a Treatment Effect Experiment
Show steps
Apply your understanding of treatment effect estimation by designing a controlled experiment to test a treatment effect in a real-world setting.
Show steps
-
Define your research question
-
Design your experiment
-
Collect and analyze your data
Practice Identifying Features in Medical Data
Show steps
Strengthen your ability to identify and extract important features from medical data.
Browse courses on
Natural Language Processing
Show steps
-
Gather a dataset of medical records
-
Review the data and identify common features
-
Practice extracting these features from the data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explain a Treatment Effect Estimation Method
Show steps
Solidify your understanding of a specific treatment effect estimation method by presenting it to others.
Show steps
-
Choose a specific method
-
Gather materials on the method
-
Create your presentation
Develop a Natural Language Processing Model for Medical Question Answering
Show steps
Demonstrate your proficiency in natural language processing by building a model that can answer medical questions from text.
Browse courses on
Natural Language Processing
Show steps
-
Collect a dataset of medical questions and answers
-
Train a BERT model on the dataset
-
Evaluate the performance of your model
Participate in a Machine Learning Hackathon
Show steps
Challenge yourself and test your skills in a competitive environment by participating in a machine learning hackathon.
Browse courses on
Machine Learning
Show steps
-
Find a hackathon that aligns with your interests
-
Form a team or work individually
-
Develop a solution to the hackathon challenge
Create a Study Guide
Show steps
Reinforce your understanding of the course material by compiling a comprehensive study guide.
Show steps
-
Review your notes and assignments
-
Organize the material into logical sections
-
Summarize the key concepts and methods
Follow Tutorials on Machine Learning Interpretation
Show steps
Expand your knowledge of machine learning interpretation by exploring tutorials and resources on the topic.
Browse courses on
Explainable AI
Show steps
Show steps
Show steps
- Identify reputable sources
- Select tutorials relevant to the course
- Follow the tutorials and apply what you learn
Design a Treatment Effect Experiment
Show steps
Apply your understanding of treatment effect estimation by designing a controlled experiment to test a treatment effect in a real-world setting.
Show steps
Show steps
Show steps
- Define your research question
- Design your experiment
- Collect and analyze your data
Practice Identifying Features in Medical Data
Show steps
Strengthen your ability to identify and extract important features from medical data.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Gather a dataset of medical records
- Review the data and identify common features
- Practice extracting these features from the data
Four other activities
Expand to see all activities and additional details
Show all seven activities
Explain a Treatment Effect Estimation Method
Show steps
Solidify your understanding of a specific treatment effect estimation method by presenting it to others.
Show steps
Show steps
Show steps
- Choose a specific method
- Gather materials on the method
- Create your presentation
Develop a Natural Language Processing Model for Medical Question Answering
Show steps
Demonstrate your proficiency in natural language processing by building a model that can answer medical questions from text.
Browse courses on
Natural Language Processing
Show steps
Show steps
Show steps
- Collect a dataset of medical questions and answers
- Train a BERT model on the dataset
- Evaluate the performance of your model
Participate in a Machine Learning Hackathon
Show steps
Challenge yourself and test your skills in a competitive environment by participating in a machine learning hackathon.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find a hackathon that aligns with your interests
- Form a team or work individually
- Develop a solution to the hackathon challenge
Create a Study Guide
Show steps
Reinforce your understanding of the course material by compiling a comprehensive study guide.
Show steps
Show steps
Show steps
- Review your notes and assignments
- Organize the material into logical sections
- Summarize the key concepts and methods
Career center
Learners who complete AI For Medical Treatment will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
A Data Scientist develops and applies statistical techniques to extract knowledge from data. They work on a wide range of problems, including predicting customer behavior, identifying fraud, and improving healthcare outcomes. This course provides you with a solid foundation in machine learning and deep learning, which are essential skills for Data Scientists. The course also covers topics such as medical question answering and ML interpretation, which are highly relevant to the field of healthcare.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer designs, develops, and deploys machine learning models. They work on a wide range of applications, including image recognition, natural language processing, and predictive analytics. This course provides you with a deep understanding of machine learning algorithms and techniques, which are essential for Machine Learning Engineers. The course also covers topics such as medical question answering and ML interpretation, which are highly relevant to the field of healthcare.
Statistician
Statistician
A Statistician collects, analyzes, and interprets data to provide insights into a variety of problems. They work in a wide range of fields, including healthcare, finance, and marketing. This course provides you with a solid foundation in machine learning and deep learning, which are essential skills for Statisticians. The course also covers topics such as medical question answering and ML interpretation, which are highly relevant to the field of healthcare.
Software Engineer
Software Engineer
A Software Engineer designs, develops, and maintains software applications. They work on a wide range of projects, including healthcare, finance, and transportation. This course provides you with a solid foundation in machine learning and deep learning, which are increasingly used in software development for tasks such as image recognition, natural language processing, and predictive analytics.
Research Scientist
Research Scientist
A Research Scientist conducts research to advance scientific knowledge. They work on a wide range of topics, including medicine, biology, and computer science. This course provides you with a solid foundation in machine learning and deep learning, which are essential skills for Research Scientists. The course also covers topics such as medical question answering and ML interpretation, which are highly relevant to the field of medical research.
Public Health Analyst
Public Health Analyst
A Public Health Analyst collects, analyzes, and interprets data to improve the health of a population. They work on a wide range of issues, including disease prevention, health promotion, and environmental health. This course provides you with the skills and knowledge you need to analyze and interpret medical data, and to make evidence-based recommendations for improving public health.
Healthcare Analyst
Healthcare Analyst
A Healthcare Analyst collects, analyzes, and interprets data to improve the quality and efficiency of healthcare delivery. They work with healthcare providers, insurers, and other stakeholders to identify trends, develop strategies, and evaluate the impact of new programs and initiatives. This course provides you with the skills and knowledge you need to analyze and interpret medical data, and to make evidence-based recommendations for improving patient care.
Systems Analyst
Systems Analyst
A Systems Analyst designs and implements computer systems to solve business problems. They work with a variety of stakeholders, including end users, business analysts, and IT professionals. This course provides you with a foundation in machine learning and deep learning, which can be used to improve the design and implementation of computer systems in a variety of ways, such as by automating tasks, optimizing performance, and detecting fraud.
Physician
Physician
A Physician is a licensed healthcare professional who provides medical care to patients. They diagnose and treat diseases, prescribe medications, and perform surgeries. This course provides you with a foundation in machine learning and deep learning, which can be used to improve patient care in a variety of ways, such as by developing new diagnostic tools, optimizing treatment plans, and predicting patient outcomes.
User Experience Designer
User Experience Designer
A User Experience Designer designs and evaluates the user experience of products and services. They work with a variety of stakeholders, including engineers, designers, and marketing professionals. This course provides you with a foundation in machine learning and deep learning, which can be used to improve the user experience of products and services in a variety of ways, such as by personalizing content, optimizing search results, and detecting fraud.
Pharmacist
Pharmacist
A Pharmacist provides medication to patients and advises them on its use. They work in a variety of settings, including hospitals, retail pharmacies, and long-term care facilities. This course provides you with a foundation in machine learning and deep learning, which can be applied to a variety of tasks in pharmacy, such as drug development, dosage optimization, and personalized medicine.
Medical Physicist
Medical Physicist
A Medical Physicist applies the principles of physics to the diagnosis and treatment of disease. They work with a wide range of medical technologies, including radiation therapy, imaging, and nuclear medicine. This course provides you with a foundation in machine learning and deep learning, which are increasingly used in medical physics for tasks such as image analysis, treatment planning, and data analysis.
Registered Nurse
Registered Nurse
A Registered Nurse provides care to patients in a variety of settings, including hospitals, clinics, and long-term care facilities. They assess patients' health, administer medications, and provide emotional support. This course provides you with a foundation in machine learning and deep learning, which can be used to improve patient care in a variety of ways, such as by developing new diagnostic tools, optimizing treatment plans, and predicting patient outcomes.
Technical Writer
Technical Writer
A Technical Writer creates user manuals, technical documentation, and other written materials to explain complex technical concepts. They work with a variety of stakeholders, including engineers, scientists, and product managers. This course provides you with a foundation in machine learning and deep learning, which can be used to create more effective and engaging technical documentation. The course also covers topics such as medical question answering and ML interpretation, which are highly relevant to the field of medical technology.
Biostatistician
Biostatistician
A Biostatistician applies statistical methods to the design of biomedical research studies, data collection, and data analysis to answer scientific questions. They collaborate with medical professionals, researchers, and other scientists to design, conduct, and interpret statistical analyses of biomedical data. This course provides you with practical experience in applying AI and machine learning to medical treatment, which can enhance your ability to analyze and interpret data, and make evidence-based decisions in the field of biostatistics.
For more career information including salaries, visit:
OpenCourser.com/course/x15pf4/ai
Reading list
We've selected 12 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
AI For Medical Treatment.
Save
This classic provides a comprehensive and in-depth foundation of deep learning theory and algorithms that are used in the healthcare domain.
Provides a comprehensive overview of deep learning techniques and their applications in healthcare. It useful reference for those who want to learn about the use of deep learning in healthcare or who need to apply deep learning techniques to healthcare problems.
Provides a comprehensive overview of machine learning (ML) techniques and their applications in medicine. It useful reference for those who want to learn about the use of ML in medicine or who need to apply ML techniques to medical problems.
Provides a practical introduction to interpretable machine learning, focusing on the methods and tools used to make machine learning models more understandable. It useful reference for those who want to learn more about interpretable machine learning or who need to evaluate the interpretability of machine learning models.
A widely used textbook for foundational knowledge of statistical learning and inference.
For the Treatment Effect Estimation module, this book provides a good foundation of causal inference.
Serves as a good general reference book for R code and syntax examples especially for deep learning applications.
Offers good practical examples of ML model building and implementation, using popular Python libraries.
This classic textbook introduces the statistical methods commonly used in medical research, including study design, data analysis, and interpretation of results. It provides a solid foundation for understanding and applying statistical concepts in healthcare.
Covers common feature engineering techniques, which is essential for building effective ML models.
Provides a more accessible introduction to Bayesian statistics, which is especially useful for designing and simulating treatment effect models.
Provides a good supplemental source of foundational info on ML theory.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/x15pf4/ai
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Effort
3 weeks, 5-6 hours/week
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructors
Pranav Rajpurkar
Bora Uyumazturk
Amirhossein Kiani
Eddy Shyu
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
AI for Medicine. It's comprised of three courses:
- AI For Medical Treatment (viewing)
- AI for Medical Prognosis
- AI for Medical Diagnosis
Good to know
Develops expertise in applying machine learning to medical use cases
Taught by recognized instructors in the field of medical AI
Examines nuances in medical AI applications
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