AI and machine learning are changing our approach to medicine and the future of healthcare (GOOGL, IBM)

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

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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 analyze 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.

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As with all things AI, these healthcare technology advancements are based on data humans provide meaning, there is a risk of data sets containing unconscious bias. Previous experiences have shown that there is potential for coder bias and bias in machine learning to affect AI findings. In the sensitive healthcare market, especially, it will be critical to establish new ethics rules to address - and prevent - bias around AI.

Future of Artificial Intelligence in Healthcare

The use of AI in the healthcare market is growing due to the continued demand for wearable technology, digital medicine, and the industry's overall transformation into the modern, digital age.

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Tech companies, startups, and healthcare professionals are discovering new ways to incorporate AI into the healthcare market; and, the speed at which we improve the healthcare system through AI will only continue to accelerate as the industry dives deeper into digital health.

Want to Learn More?

The Digital Health Ecosystem report from Business Insider Intelligence details the rise of artificial intelligence in digital healthcare technology due to a market-wide shift emphasizing preventative care.

The report takes an in-depth look as to how AI could increase health revenue through cloud services that provide data-driven clinical decision support to physicians. Major points include how data-driven clinical decision support improves clinical patient flow, and why the Centers for Medicare & Medicaid Services is promoting AI solutions.

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