Best Artificial Intelligence courses for Healthcare You should learn 2022

Here are the best Artificial Intelligence courses for Healthcare that you should know in 2022 to learn AI in healthcare courses.

6 Best Artificial Intelligence Courses for Healthcare 2022

1. AI for Medicine Specialization — deeplearning.ai

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.

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 taking the Deep Learning Specialization.

Applied Learning Project

Medicine is one of the fastest-growing and important application areas, with unique challenges like handling missing data. You’ll start by learning the nuances of working with 2D and 3D medical image data. You’ll then apply tree-based models to improve patient survival estimates.

You’ll also use data from randomized trials to recommend treatments more suited to individual patients. Finally, you’ll explore how natural language extraction can more efficiently label medical datasets.

However, it is good to enrol for the full course that can cost $39– $48 per month so that you can get feedback on how you build your models so can ensure that you are on the right track of you learned skills

Rating: 4.7 | Course Duration: 84 hours| Level: Intermediate

Pricing: Starts from $39

Info: Visit the Website get Special Financial aid is available with 50%off

2. AI in healthcare specialization — Stanford University

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.

CME Accreditation

The Stanford University School of Medicine is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians. View the full CME accreditation information on the individual course FAQ page.

Applied Learning Project:

The final course will consist of a capstone project that will take you on a guided tour exploring all the concepts we have covered in the different classes. This will be a hands-on experience following a patient’s journey from the lens of the data, using a unique dataset created for this specialization.

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.

Rating: 4.7 | Course Duration: 72 hours| Level: Beginner

Pricing: Starts from $40

Info: Visit the Website get Special Financial aid is available with 50%off

3. Statistical Analysis with R for Public Health Specialization– Imperial College London

Statistics are everywhere. The probability it will rain today. Trends over time in unemployment rates. The odds that India will win the next cricket world cup. In sports like football, they started out as a bit of fun but have grown into big business. Statistical analysis also has a key role in medicine, not least in the broad and core discipline of public health.

In this specialisation, you’ll take a peek at what medical research is and how — and indeed why — you turn a vague notion into a scientifically testable hypothesis. You’ll learn about key statistical concepts like sampling, uncertainty, variation, missing values and distributions.

Then you’ll get your hands dirty with analysing data sets covering some big public health challenges — fruit and vegetable consumption and cancer, risk factors for diabetes, and predictors of death following heart failure hospitalisation — using R, one of the most widely used and versatile free software packages around.

This specialisation consists of four courses — statistical thinking, linear regression, logistic regression and survival analysis — and is part of our upcoming Global Master in Public Health degree, which is due to start in September 2019.

The specialisation can be taken independently of the GMPH and will assume no knowledge of statistics or R software. You just need an interest in medical matters and quantitative data.

Applied Learning Project:

In each course, you’ll be introduced to key concepts and a data set to be used as a worked example throughout that course. Public health data are messy, with missing values and weird distributions all too common. The data you’ll use are either real or simulated from real patient-level data sets (all anonymised and with usage permissions in place).

The emphasis will be on “learning through doing” and “learning through discovery” as you encounter typical data and analysis problems for you to solve and discuss among your fellow learners. You’ll get the chance to work things out for yourself and with your peers before accessing the answers and explanation provided by the instructors.

Rating: 4.7 | Course Duration: 48 hours| Level: Beginner

Pricing: Starts from $40

Info: Visit the Website get Special Financial aid is available with 50%off

4. Fundamentals of Machine Learning for Healthcare– Stanford University

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.

Rating: 4.7 | Course Duration: 48 hours| Level: Beginner

Pricing: Starts from $40

Info: Visit the Website get Special Financial aid is available with 50%off

5. AI for Medical Diagnosis– deeplearning.ai

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. As an AI practitioner, you have the opportunity to join in this transformation of modern medicine.

If you’re already familiar with some of the math and coding behind AI algorithms and are eager to develop your skills further to tackle challenges in the healthcare industry, then this specialization is for you. No prior medical expertise is required!

This program will give you practical experience in applying cutting-edge machine learning techniques to concrete problems in modern medicine: 

Course 1: you will create convolutional neural network image classification and segmentation models to make diagnoses of lung and brain disorders.

 Course 2: you will build risk models and survival estimators for heart disease using statistical methods and a random forest predictor to determine patient prognosis. 

Course 3: you will build a treatment effect predictor, apply model interpretation techniques and use natural language processing to extract information from radiology reports. These courses go beyond the foundations of deep learning to give you insight into the nuances of applying AI to medical use cases.

As a learner, you will be set up for success in this program if you are already comfortable with some of the math and coding behind AI algorithms. You don’t need to be an AI expert, but a working knowledge of deep neural networks, particularly convolutional networks, and proficiency in Python programming at an intermediate level will be essential.

If you are relatively new to machine learning or neural networks, we recommend that you first take the Deep Learning Specialization, offered by deeplearning.ai and taught by Andrew Ng. The demand for AI practitioners with the skills and knowledge to tackle the biggest issues in modern medicine is growing exponentially.

Join us in this specialization and begin your journey toward building the future of healthcare.

Rating: 4.7 | Course Duration: 20hours | Level: Intermediate 

Pricing: Starts from $40/month

Info: Visit the Website get Special Financial aid is available with 50%off

6. AI for Healthcare — Udacity

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 modelling 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.

Rating: 4.6 | Course Duration: 240 hours| Level: Intermediate

Pricing: $240/month Discount available

Info: Visit the Website get a Special personalized discount of 75% off