Online Course – Google Certified Professional Internship in AI in Healthcare, Stanford University

Discover the latest information about our products and services. Experience the quality of our products and excellent customer service. Find more details on our website.

Suggested by: Coursera (What is Coursera?)

Start your Coursera.com free trial today
קורס אונליין - התמחות מקצועית מוסמכת ב-AI in Healthcare של Google

Professional Certificate

Beginners

No prior knowledge required

Time to complete the course

7-day free trial

No unnecessary risks

Skills you will acquire in the course

  • Ability to analyze medical data and external information
  • Understanding of artificial intelligence applications in healthcare
  • Knowledge of bringing AI technologies to the clinic in a safe and ethical manner
  • Improving collaboration between health professions and computer science
  • Experience in a practical final project in the health field
  • Understanding the impact of choices on medical care recommended by AI models

What you will learn in the course

Courses for which the course is suitable

  • Doctors
  • Medical data analysts
  • Health researchers
  • Digital health professionals
  • Healthcare developers
  • Healthcare project managers
  • Healthcare consultants
  • Computer science professionals
  • Artificial intelligence experts in healthcare

Internship – Series of 5 courses

Artificial intelligence (AI) has transformed industries around the world and has the potential to dramatically change the healthcare sector. Imagine being able to analyze data on patient clinic visits, medications prescribed, lab tests and procedures performed, alongside data from outside the healthcare system – such as social media, credit card purchases, population records, and internet search logs that contain valuable health information.

In this internship, we will discuss current and future applications of artificial intelligence in healthcare, with the aim of learning how to bring AI technologies to the clinic in a safe and ethical manner.

Target audience

  • Health professionals
  • Computer science professionals

This specialization offers insights for improving collaboration between disciplines.

CME Accreditation

Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. Complete information about CME accreditation can be found on the specific course’s FAQ page.

Hands-on Learning Project

The final course will include a final project that will take you on a guided tour where we will explore all the concepts we have covered in the various classes. It will be a hands-on experience that will focus on a patient’s journey through the lens of data, using a unique dataset created for this internship.

We’ll review how the various choices you make—such as those related to feature construction, the types of data you want to use, how the model is evaluated, and how you deal with the patient’s timeline—affect the treatment recommended by the model.

Details of the courses that make up the specialization

Introduction to health

Course 1

11 hours
4.8 (981 ratings)

  • Key challenges in the American healthcare system
  • Problems that may occur in efforts to improve health care delivery and the health system
  • Who are the key players in the American healthcare system?

Course 2

11 hours
4.7 (336 ratings)

  • How to apply a methodology in the field of medical data mining
  • Ethical use of data in healthcare decision-making
  • How to make use of data that may be inaccurate in systematic ways
  • What is an important research question and how to build a workflow for success in data mining

Course 3

14 hours
4.8 (466 ratings)

  • Define important connections between the fields of machine learning, biostatistics, and traditional programming
  • Learn about advanced neural network architectures for tasks such as text classification and object recognition and mapping
  • Learn important approaches to exploiting data to train, validate, and test machine learning models
  • Understand how dynamic medical practice and changing needs impact the development and adoption of clinical machine learning applications

Course 4

11 hours
4.6 (234 ratings)

  • Principles and practical considerations for integrating AI into clinical workflows
  • Best practices for AI applications to promote fair and equitable healthcare solutions
  • Regulatory challenges in AI applications and what components of a regulatory model are
  • Which standard assessment matrix is ​​satisfactory and which is not?

Course 5

10 hours
4.6 (210 ratings)

  • A final project dedicated to exploring all the concepts learned in the various classes.
  • The journey of a patient with respiratory symptoms and tracking the data generated in each session
  • Building models for patient risk decision making
  • Discussing regulatory and ethical issues in using AI to make better decisions

Related products