The healthcare industry continues to evolve as machine learning and AI in technology become more prevalent. Business Insider Intelligence reported that spending on AI in healthcare is projected to grow at an annualized 48% between 2017 and 2023.
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 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.
Wearable healthcare technology also uses AI to better serve patients. Software that uses AI, like FitBits and smartwatches, can analyze data to alert users and their healthcare professionals on potential health issues and risks. Being able to assess one’s own health through technology eases the workload of professionals and prevents unnecessary hospital visits or remissions.
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.
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:
The Digital Health Ecosystem, which explores the key trends driving digital transformation in healthcare and what we expect to see in the year ahead.
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.
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.