AI in Health Care: Applications, Benefits, and Examples

AI is changing how health care professionals provide care and how patients receive it. Learn more about what AI means for the field today – and in the coming years.

Artificial intelligence (AI) has already changed much of the world as we know it – from automating systems to improving the decisions we make and the ways we go about making them. Yet, perhaps the most impactful and personal ways AI is changing our world are within the field of health care, where it’s being used to diagnose, create personalized treatment plans, and even predict patient survival rates.

In this article, you’ll learn more about the types of AI used in health care, some of their applications and the benefits of AI within the field, as well as what the future might hold. You’ll also explore relevant jobs and online courses that can help you get started using AI for health care purposes today.

How is AI used in health care?

 

Artificial Intelligence (AI) uses computers and machine processes to simulate human intelligence and perform complex automated tasks. While they seek to reflect the abilities of the human mind, AI-enabled machines are also capable of exceeding it in a number of ways, particularly by sifting through large volumes of big data efficiently in order to identify patterns, anomalies, and trends.

Unsurprisingly, AI presents a wealth of opportunities to health care, where it can be used to enhance a variety of common medical processes – from diagnosing diseases to identifying the best treatment plans for patients facing critical illnesses like cancer. Robotic surgical equipment outfitted with AI can help surgeons better perform surgeries by decreasing their physical fluctuations and providing updated information during the operation.

Types of AI in health care

 

AI is an umbrella term covering a variety of distinct, but interrelated processes. Some of the most common forms of AI used within health care include:

 

• Machine learning (ML): training algorithms using data sets, such as health records, to create models capable of performing such tasks as categorizing information or predicting outcomes.

 

• Deep learning: A subset of machine learning that involves greater volumes of data, training times, and layers of ML algorithms to produce neural networks capable of more complex tasks.

 

• Neural language processing (NLP): the use of ML to understand human language, whether it be verbal or written. In health care, NLP is used to interpret documentation, notes, reports, and published research.

 

• Robotic process automation (RPA): the use of AI in computer programs to automate administrative and clinical workflows. Some health care organizations use RPA to improve the patient experience and the daily function of their facilities.

AI applications in health care

 

As artificial intelligence becomes more widely adopted, so too does the number of ways the technology is being used across industries. Researchers don’t expect AI to replace health care professionals just yet. Instead, they see it as supporting and improving the work of health providers and professionals in the near future. Here are some of the most common applications of AI in the field today:

 

• Health care analytics: ML algorithms are trained using historical data to produce insights, improve decision-making, and optimize health outcomes.

 

• Precision medicine: AI is used to produce personalized treatment plans for patients that take into account such factors as their medical history, environmental factors, lifestyles, and genetic makeup.

 

• Predict diseases and illness: Using predictive models, health care professionals can determine the likelihood that someone might develop a particular condition or contract a disease.

 

• Interpret tests and diagnose diseases: ML models can be trained using common medical scans, like MRIs or X-rays, to interpret and diagnose such conditions as cancerous lesions.

• Benefits of AI in health care

   

  AI provides a number of benefits to the field of health care, the professionals working within it, and the patients that interact with it every day. While health care professionals can expect lower operational costs due to improved decision-making and more efficient automated services, providers can leverage the technology to design bespoke treatment plans and diagnose conditions more quickly and accurately than they could alone. Patients can expect potentially improved health outcomes and lower costs resulting from more efficient health services.

• The future of AI in health care

   

  As with many other industries, AI is poised to change the health care landscape over the coming years. In addition to improving health facility operations, patient diagnoses, treatment plan development, and overall health outcomes, AI is also expected to help with the development and discovery of new medical cures.

   

  The use of artificial intelligence in health care is expected to grow significantly over the next decade. According to Grand View Research, AI in health care is forecasted to be valued at $208.2 billion in 2030, which is many times higher than its 2022 market size value of $15.4 billion [1].

  While some research indicates that AI could lead to significant job cuts as technology automates tasks like interpreting radiologic images, others believe that this is unlikely to be the case. One 2019 research paper, for instance, asserts that actual job loss is likely to be just five percent or less over the next ten to twenty years, indicating that most job seekers have little to worry about for the foreseeable future [2].

• Get started with Coursera

   

  AI is a growing and complicated field with a wealth of potential. If you’re considering a career using AI to help improve health care and maybe even solve some of the most elusive medical mysteries, then you might consider taking a relevant course through Coursera.

  Deeplearning.ai’s AI for Medicine Specialization, for example, provides practical experience applying machine learning to concrete problems in medicine like predicting patient survival rates, estimating treatment plan efficacy, and diagnosing diseases from 3D MRI brain scans.