AI in healthcare & medicine is booming

 Artificial Intelligence (AI) is commonly known for its ability to have machines perform tasks that are associated with the human mind – like problem solving. However, what's less understood is how AI is being used within specific industries, such as healthcare.

What is Artificial Intelligence in Healthcare?

Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff – paving the way for an increased revenue potential. Machine learning, a subset of AI designed to identify patterns, uses algorithms and data to give automated insights to healthcare providers.

Examples of AI in Healthcare and Medicine

AI can improve healthcare by fostering preventative medicine and new drug discovery. Two examples of how AI is impacting healthcare include:

🔷IBM Watson's ability to pinpoint treatments for cancer patients, and Google Cloud's Healthcare app that makes it easier for health organizations to collect, store, and access data. 

🔷Business Insider Intelligence reported that researchers at the University of North Carolina Lineberger Comprehensive Cancer Center used IBM Watson's Genomic product to identify specific treatments for over 1,000 patients. The product performed big data analysis to determine treatment options for people with tumors who were showing genetic abnormalities. 

🔷Comparatively, Google's Cloud Healthcare application programming interface (API) includes CDS offerings and other AI solutions that help doctors make more informed clinical decisions regarding patients. AI used in Google Cloud takes data from users' electronic health records through machine learning – creating insights for healthcare providers to make better clinical decisions. 

🔷Google worked with the University of California, Stanford University, and the University of Chicago to generate an AI system that predicts the outcomes of hospital visits. This acts as a way to prevent readmissions and shorten the amount of time patients are kept in hospitals.

Benefits, Problems, Risks & Ethics of AI in Healthcare

Integrating AI into the healthcare ecosystem allows for a multitude of benefits, including automating tasks and analyzing big patient data sets to deliver better healthcare faster, and at a lower cost. 

According to Business Insider Intelligence, 30% of healthcare costs are associated with administrative tasks. AI can automate some of these tasks, like pre-authorizing insurance, following-up on unpaid bills, and maintaining records, to ease the workload of healthcare professionals and ultimately save them money. 

AI has the ability to analyse big data sets – pulling together patient insights and leading to predictive analysis. Quickly obtaining patient insights helps the healthcare ecosystem discover key areas of patient care that require improvement. 

Interested in more related Digital Health research?

In addition to artificial intelligence, Insider Intelligence publishes a wealth of research reports, charts, forecasts, and analysis of the Digital Health industry.

And here are some related Digital Health reports that might interest you:

1. The Digital Health Ecosystem, which explores the key trends driving digital transformation in healthcare and what we expect to see in the year ahead.

2. AI in Medical Diagnosis, which examines the value of AI applications in three high-value areas of medical diagnosis — imaging, clinical decision support, and personalized medicine — to illustrate how the tech can drastically improve patient outcomes, lower costs, and increase productivity.

3. The Digital Therapeutics Explainer, which explores the drivers lighting a fire under the DTx market, identifies the leading DTx market players, and unpacks the varied ways vendors reach their intended audiences

Advancing the use of Immunotherapy for Cancer Treatment

Immunotherapy is one of the most promising avenues for treating cancer.  By using the body’s own immune system to attack malignancies, patients may be able to beat stubborn tumors.  However, only a small number of patients respond to current immunotherapy options, and oncologists still do not have a precise and reliable method for identifying which patients will benefit from this option.

Machine learning algorithms and their ability to synthesize highly complex datasets may be able to illuminate new options for targeting therapies to an individual’s unique genetic makeup.

“Recently, the most exciting development has been checkpoint inhibitors, which block some of the proteins made by some times of immune cells,” explained Long Le, MD, PhD, Director of Computational Pathology and Technology Development at the MGH Center for Integrated Diagnostics.  “But we still don’t understand all of the disease biology.  This is a very complex problem.”

“We definitely need more patient data.  The therapies are relatively new, so not a lot of patients have actually been put on these drugs.  So whether we need to integrate data within one institution or across multiple institutions is going to be a key factor in terms of augmenting the patient population to drive the modeling process.”

Smart devices are taking over the consumer environment, offering everything from real-time video from the inside of a refrigerator to cars that can detect when the driver is distracted.

In the medical environment, smart devices are critical for monitoring patients in the ICU and elsewhere.  Using artificial intelligence to enhance the ability to identify deterioration, suggest that sepsis is taking hold, or sense the development of complications can significantly improve outcomes and may reduce costs related to hospital-acquired condition penalties.

“When we’re talking about integrating disparate data from across the healthcare system, integrating it, and generating an alert that would alert an ICU doctor to intervene early on – the aggregation of that data is not something that a human can do very well,” said Mark Michalski, MD, Executive Director of the MGH & BWH Center for Clinical Data Science. 

Inserting intelligent algorithms into these devices can reduce cognitive burdens for physicians while ensuring that patients receive care in as timely a manner as possible.