This collection aims to list free high-quality courses, specializations and workshops.
Note: Some of the specializations may charge you if you want to earn a degree/certificate, but you can "audit" the courses for free if you are happy with only reading and viewing the course content.
Link: https://www.coursera.org/specializations/ai-for-medicine
Abstract:
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 three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine.
Link: https://www.coursera.org/learn/intro-to-healthcare
Abstract:
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.
Link: https://www.coursera.org/learn/intro-to-healthcare
Abstract:
This capstone project takes you on a guided tour exploring all the concepts we have covered in the different classes up till now. We have organized this experience around the journey of a patient who develops some respiratory symptoms and given the concerns around COVID19 seeks care with a primary care provider. We will follow the patient's journey from the lens of the data that are created at each encounter, which will bring us to a unique de-identified dataset created specially for this specialization. The data set spans EHR as well as image data and using this dataset, we will build models that enable risk-stratification decisions for our patient. We will review how the different choices you make -- such as those around feature construction, the data types to use, how the model evaluation is set up and how you handle the patient timeline -- affect the care that would be recommended by the model. During this exploration, we will also discuss the regulatory as well as ethical issues that come up as we attempt to use AI to help us make better care decisions for our patient. This course will be a hands-on experience in the day of a medical data miner.
Link: https://www.coursera.org/learn/intro-to-healthcare
Abstract:
This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.
Link: https://www.coursera.org/learn/intro-to-healthcare
Abstract:
- Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles.
- This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. We will explore machine learning approaches, medical use cases, metrics unique to healthcare, as well as best practices for designing, building, and evaluating machine learning applications in healthcare.
- The course will empower those with non-engineering backgrounds in healthcare, health policy, pharmaceutical development, as well as data science with the knowledge to critically evaluate and use these technologies.
Link: https://www.coursera.org/specializations/ai-healthcare
Abstract:
- Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses.
- In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically.
- This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.
Link: https://www.coursera.org/learn/intro-to-healthcare?specialization=ai-healthcare
Abstract:
- Solving the problems and challenges within the U.S. healthcare system requires a deep understanding of how the system works. Successful solutions and strategies must take into account the realities of the current system.
- This course explores the fundamentals of the U.S. healthcare system. It will introduce the principal institutions and participants in healthcare systems, explain what they do, and discuss the interactions between them. The course will cover physician practices, hospitals, pharmaceuticals, and insurance and financing arrangements.
- We will also discuss the challenges of healthcare cost management, quality of care, and access to care. While the course focuses on the U.S. healthcare system, we will also refer to healthcare systems in other developed countries.
Link: https://mlhc17mit.github.io/
Abstract:
- Explores machine learning methods for clinical and healthcare applications.
- Covers concepts of algorithmic fairness, interpretability, and causality.
- Discusses application of time-series analysis, graphical models, deep learning and transfer learning methods to solving problems in healthcare.
- Considers how newly emerging machine learning techniques will shape healthcare policy and personalized medicine.
Link: https://www.udacity.com/course/ai-for-healthcare-nanodegree--nd320
Abstract:
Learn to build, evaluate, and integrate predictive models that have the power to transform patient outcomes. Begin by classifying and segmenting 2D and 3D medical images to augment diagnosis and then move on to modeling patient outcomes with electronic health records to optimize clinical trial testing decisions. Finally, build an algorithm that uses data collected from wearable devices to estimate the wearer’s pulse rate in the presence of motion.