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. Deep learning, a subset of AI designed to identify patterns, uses algorithms and data to give automated insights to healthcare providers.

Examples of AI in Medicine and Healthcare

AI can improve healthcare by streamlining diagnoses and improve clinical outcomes. A critical part AI’s power in the healthcare industry is its ability to analyze a vast amount of data sets.

Digital health startup Thymia is a prime example. Founded in 2020, Thymia developed an AI-based video game that is meant to provide faster, more accurate, and objective mental health assessments. Patients on the platform are evaluated based on video games they enjoy, after which a baseline assessment is created. Then, AI looks at looks at thousands of anonymized facial features on video and studies audio to identify the likelihood and potential severity of depression. The platform offers continuous, remote monitoring for patients and clinicians to understand the conditions and treatments in real time.

Also leveraging big data is predictive analytics and market research firm Trilliant Health, which introduced SimilarityIndex | Hospitals—a data visualizer tool that benchmarks more than 2,000 US. hospitals. The tool’s machine learning-based SimilarityEngine lets users select a benchmark hospital and then view 10 similar peers in a visualization. Filters include number of readmissions, mortality rates, and hospital acquired conditions scores. Trilliant Health suggests that more accurate benchmarks allow healthcare executives to evaluate health systems based on evidence-based strategies compared with promotional top 100 hospital listings.

AI’s full prowess is demonstrated when it’s paired with other technologies such as robotics where it combines analytical power with physical adeptness. Genki Kanda at the RIKEN Center for Biosystems Dynamics Research, for example, developed a robotic AI system that could improve stem cell procedures used in regenerative medicine. The system was able to determine how to regrow functional retina layers from stem cells to improve vision after evaluating 200 million possible scenarios through trial and error. AI programs running on computers that crunch numbers much faster than humans could lead to much faster research breakthroughs in identifying new treatments and finding new methods to diagnose.

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23% of US healthcare executives believe that AI and ML is very effective at improving clinical outcomes, according to a survey conducted by Morning Consult.

Benefits of AI in Healthcare & Medicine

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. 

AI can automate administrative 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. 

With AI’s ability to process big data sets, consolidating patient insights can lead to predictive benefits, helping 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.