Everything You Need to Know About Artificial Intelligence in Medicine

From better and faster diagnostics to unprecedented precision in surgery and accelerated research and development of new therapies, artificial intelligence, or AI, is transforming the world of medicine. The combination of advanced algorithms capable of independent decision making and massive data sets that help these algorithms “learn” can give healthcare professionals an unprecedented array of sophisticated new tools for improving outcomes and bringing new treatments to market. The adoption of AI-powered tools and platforms comes as part of a digital revolution that promises to transform all areas of healthcare.

What is Artificial Intelligence?

Artificial intelligence is the name for an entire branch of computer science dedicated to creating machines that can perform tasks that usually require the decision making abilities of human beings. These “smart” machines range from home applications such as thermostats and voice assistants like Amazon’s Alexa to the sophisticated infrastructures that keep large cities interconnected and operating smoothly.

In essence, artificial intelligence technologies aim to answer a question that was once the province of pulp science fiction stories: can machines think? And while movies and books often portray intelligent machines as malevolent entities that want to take over the world, today’s smart machines are designed to work with, not against, humans in a partnership that makes the best use of the unique properties of our brains.

AI is very different from automation – robotic functioning in which a machine performs repetitive tasks so humans can turn their attention to more complex work. Instead, AI algorithms learn how to do tasks and make decisions that are largely independent of human input, which is accomplished through a process called machine learning.

Machine learning is the training by which an algorithm acquires the skills to make accurate, relevant conclusions and predictions. Using large data sets, algorithms “learn” to find patterns and structures and make the appropriate response. With practice, they also learn to adapt when confronted with new data.

Deep learning models train AI applications using multilayered neural networks that are capable of complex processing on extremely large data sets. In a paradigm developers call “Humans in the Loop,” humans can then use the information produced by artificial intelligences to refine the algorithm’s performance, and to use its insights to make higher-level decisions.

AI Is Transforming Medicine

AI applications are capable of performing complex operations on extremely large sets of data – and thanks to global research databases and the widespread use of electronic health records, the healthcare industry is responsible for around 50 percent of all data stored in the cloud.

The availability of “big data” is a major factor driving the growing use of AI-powered applications in all areas of healthcare, including diagnostics, research and development, surgery and a variety of approaches to delivering highly individualized care tailored to each patient’s unique profile and needs. Here are five key clinical and research areas that are experiencing the greatest benefits of AI-powered platforms and tools.

Diagnostics

The medical applications of artificial intelligence are on full display in the area of diagnostics. With the computational power of the cloud and massive amounts of medical data from a wide range of sources, AI powered diagnostic programs can screen large data sets for specific disease markers much faster and more precisely than their human counterparts can – and they can spot abnormalities far smaller than the human eye can see.

For example, an AI designed to screen for cancer biomarkers can be trained with exposure to a large bank of stored imaging data to spot lesions, tumors and other cellular abnormalities. But once this AI diagnostic tool returns results, humans take over. Doctors or other healthcare professionals must interpret these findings to make decisions about appropriate treatments.

Drug Research and Development

Artificial intelligence applications are already an essential tool in the process of developing new treatments and drugs. A medication’s journey from initial research to market currently takes up to 10 years, but AI makes new drugs available in a fraction of that time. AI platforms process massive amounts of data to identify precise biomarkers for targeting, match drug candidates to these targets, and fast-track clinical trials. AI-powered apps can also be used to create virtual models for predicting the outcomes of various therapies under investigation.

Surgical Robotics

One of the most promising applications of AI in medicine is in surgery. Guided by sophisticated AI programming, robotic devices are able to perform an array of surgical procedures with far greater precision and consistency than human surgeons can. Under the direction of a surgical team, these robotic surgical “assistants” can map areas of the body and direct treatments such as radiotherapy and lasers, make precise incisions, and direct the movement of catheters in endoscopic procedures.

Gene Editing and Genomics

One of the newest frontiers of medicine is the field of genetic sequencing and editing. Understanding each individual’s genetic makeup makes it possible to create tailored therapies for a number of health conditions, including rare diseases, by making changes to DNA at the cellular level. Deep machine learning and AI applications can improve the performance of gene editing technologies such as CRISPR, which allow geneticists to tweak DNA sequences to alter genes associated with specific diseases, or edit in positive features for better crop yields.

Personalized Treatment Plans

AI technologies give healthcare professionals the tools to access massive amounts of data in the shortest possible times, and the insights to guide their decisions. That allows specialists in all medical fields to design highly customized treatments and therapies that target each patient’s specific circumstances, genetic makeup, and more. Once trained, AI platforms can quickly match each patient with an array of appropriate treatment options, and predict which show the most promise, based on DNA, imaging tests and other information.

AI systems in healthcare are expected to generate $6.7 billion in revenue by 2021, and they’re leading the way toward a new model of medicine – one that’s safer, smarter, and tailored to each patient’s needs.