Artificial intelligence (AI) has become one of the world’s most disruptive technologies. Intelligent systems frequently populate headlines and board conversations, but the medical industry requires more than just buzzwords. AI in healthcare needs to demonstrate real-world success before medical organizations can implement it at scale.
Despite how new this technology is, it’s already seen use in hospitals and other medical businesses across the globe. These AI case studies can help others in the industry get a better picture of how AI could help them. Here are five of the most significant examples:
Consolidating Patient Information
One of the greatest advantages of AI in healthcare is its ability to make sense of large, unstructured data sets. Hardin Memorial Health in Elizabethtown, Kentucky, capitalized on this by using AI analytical software to consolidate patient records.
The hospital’s radiology center used a ready-made AI program to extract relevant insights from electronic health records (EHRs). The program – IBM’s Watson Imaging Patient Synopsis – would analyze patient records to find relevant information, compile it, and show it to radiologists in a single window. With this quick access to comprehensive information, radiologists could get a better picture of patients’ conditions and risks in less time.
Hospitals could apply off-the-shelf synopsis programs like this in other areas, too. Getting a quick, total picture of patient health can help streamline care in virtually any process. However, keep in mind that doctors should always double-check these records if different actions could cause severe consequences, as AI is still imperfect.
Improving Diagnoses and Prognoses
Similarly, some healthcare systems have seen success using AI to inform more accurate diagnoses. Mayo Clinic has started using AI models to analyze readings and health data to highlight conditions even when symptoms aren’t noticeable.
Mayo Clinic’s cardiovascular AI screening tool proved 93% accurate in identifying ventricular dysfunction risks. By comparison, mammograms are typically accurate 85% of the time. These risk predictions can help doctors make more informed decisions about further tests and treatment steps.
It’s important to note that this AI tool doesn’t have the last say in patient care. Medical staff should use systems like this to assist them, not automate diagnoses entirely. Mayo Clinic uses artificial intelligence screening to help doctors make the right medical decision faster, not do their jobs for them.
Drug Discovery
Another notable application of AI in healthcare is drug discovery. One of the reasons Moderna was able to produce a COVID-19 vaccine so quickly is because it leveraged AI to streamline the process. The company automated up to 90% of preclinical quality control checks, analyzing data with AI to find which solutions showed promise and which wouldn’t work.
Dave Johnson, Chief Data and AI Officer at Moderna, notes that you can’t automate the entire drug discovery process. However, AI can perform much of the repetitive, data-heavy preclinical work. Machine learning algorithms can run simulations of how various drugs interact and analyze past performance, narrowing the field of possible solutions before moving to clinical trials.
These algorithms are highly sophisticated and, as such, don’t typically come as an off-the-shelf solution. Medical organizations must either hire in-house data science professionals or partner with AI companies to capitalize on them. When they can do that, they can find promising drugs far faster than manual processes.
Fraud Prevention
Experts predict that AI could add $13 trillion to the global economy by 2030, and its financial opportunities don’t end at bringing in more money. AI can also help reduce losses by preventing fraud. Artificial intelligence case studies in the medical and healthcare industries highlight this possibility.
Dental insurer DentaQuest used data mining and analytics software to review insurance claims and highlight potential fraud cases. The ready-made solution analyzes more than 50 indicators of potential fraud, alerting workers when a case raises suspicion. Investigators can then look into these claims further to determine whether they’re fraudulent.
Insurance fraud is common throughout the medical industry, but anti-fraud software has become common in financial sectors. By implementing this software, medical organizations can give their claims teams more information and streamline research, helping them work more efficiently and precisely.
Providing Social Care for Patients
Some hospitals have even started using AI in patient care. While these systems may not be ready to automate operations yet, they can provide social care to improve patient mental health. Changi General Hospital (CGH) in Singapore is a leading example of this AI use case.
CGH employs more than 50 robots, some of which have AI functionality to interact with patients. These intelligent robots hold conversations, play memory games, and help hospital staff with group therapy. This social interaction proved effective enough for CGH to reduce its use of sedatives for dementia patients.
Compared to medical artificial intelligence software, these intelligent robots can be expensive. As a result, they may not be an ideal solution for every hospital, but those with the appropriate budget and high mental healthcare costs could benefit from them. As with every other use case, though, these machines must help human workers, not replace them entirely.
Medical Artificial Intelligence Case Studies Show Promise
These AI case studies highlight how intelligent technology can improve many aspects of medical care. Successful cases also share a common theme: AI is another tool to help healthcare professionals, not a replacement for them.
Given the limitations of this technology, a slow, careful approach seems like the best path forward. If healthcare organizations understand what AI can and cannot do, they can apply it to impressive outcomes.
