Artificial intelligence (AI) benefits numerous industries by providing insights that would take too long or may otherwise be impossible to get through other methods. That additional information is crucial in the health care sector, where professionals regularly make critical decisions while diagnosing a patient, treating their ailments or helping them manage chronic symptoms. Here are some fascinating ways that applying AI in health care brings benefits to everyone involved.
Tackling Antibiotic Resistance
Antibiotic resistance is an ongoing problem in medicine. It happens when the drugs developed to target germs no longer kill them, and they continue to flourish. That’s particularly problematic in hospitals, which are exposed to higher-than-average numbers of pathogens. Plus, they often treat many older or immunocompromised individuals.
Statistics indicate that antibiotic-resistant bacteria and fungi affect more than 2.8 million people in the United States every year and kill 35,000 of them. However, AI could help health care facilities understand which pathogens will most likely become resistant.
Current methods of learning those details center on genetic sequencing. However, that process can take a while, and it’s not available everywhere.
A Duke University project involves using AI for a different method. Researchers gathered more than 200 bacterial strain samples and put them into identical growth environments. They then measured how the respective population density of each one increased over time.
They used that data to train a machine-learning algorithm, which eventually achieved a 92%-98% accuracy rate for identifying each strain based on growth data alone. That information then enabled the algorithm to tell correctly up to 75% of the time how resistant a strain was to antibiotics.
Reducing Manual Note-Taking and Review Tasks
Another AI health application involves natural language processing (NLP), which relates to computers analyzing text and speech. If a person uses a voice-recognition system on their smartphone, NLP is working in the background.
Due to the immense amount of information associated with the healthcare field, NLP can bring better results by minimizing labor. In some instances, people use NLP to assist with studying patient records to determine provider success rates. For example, this type of AI showed how often endoscopists at a Virginia hospital found non-cancerous tumors.
Another NLP application studied 38,664 patient feedback responses from two hospitals. The AI categorized them into topics, subtopics and sentiments. The results showed that 16.7% of the content related to matters requiring monitoring. However, 15.3% of the responses identified things for facility managers to celebrate.
This example shows how managers at health care organizations can use AI to find patterns and groups within large batches of data. This gives them a more accurate picture of what people think and where room for improvement exists.
Many NLP tools also help physicians record notes faster by recognizing what they say and documenting it in an accessible format. Those options are even more robust than most voice-recognition tools for nonprofessional use because they recognize complicated medical terminology and drug names.
Enabling Remote Patient Monitoring
Monitoring patients remotely has become progressively more popular over the years, especially as AI applications become more accurate and can pick up on how a patient’s condition changes. Remote care proved especially worthwhile during the COVID-19 pandemic. For example, it reduced a provider’s potential exposure to the virus and gave more data than manual spot checks allowed.
Patient monitoring devices with AI features are often streamlined for greater usability, yet they include complex sensors and other high-tech components. As devices become more compact, engineers must also determine the best ways to keep them cool. Some of the primary goals are to target high heat fluxes without causing further temperature increases. Microchannel liquid cooling is one possible solution.
Moreover, designers must build AI monitoring devices to work with limited or no intervention from a user. When someone is recovering from an illness, they don’t have the time or energy to deal with technical issues from an AI monitoring device. However, many of the options on the market now are user-friendly for everyone involved.
For example, some items automatically transfer data to a patient’s medical team, even while they recover at home. Plus, these AI applications compile real-time information to give scores that help professionals. The numbers allow providers to determine someone’s stability or deterioration, enabling them to make data-backed, timely decisions. Similar products used in hospital settings make it possible to check on more patients despite lower staff numbers.
Minimizing Drug Interactions
Before people are prescribed new drugs, their doctors ask them about any medications they’re taking now and any past allergic reactions. Patient responses to those queries help physicians decide whether they will likely suffer any drug interaction-related side effects. The risk of that complication rises with the number of medications people take.
The matter also gets more complicated because combinations of certain over-the-counter and prescription drugs may cause long-term organ damage. Many consumers don’t question the safety of products available in their local pharmacies, but adverse reactions can still occur with those items.
However, researchers recently developed an artificial neural network to serve as a warning system. Training it required getting information from sources like the Food and Drug Administration (FDA) and other regulatory agencies. Anyone who’s ever read a medication brochure knows side effects can range from the relatively minor ” like tiredness or a runny nose ” to catastrophic complications that result in death. The researchers accounted for that and prevented alert fatigue when creating the algorithm.
They made it only warn people about the highest-priority dangers from interactions, such as hospitalization, disability and death, as well as any that required interventions to address. The team hopes to combine the drug interaction data with patients’ individual genomic information for even more personalization.
Expect More AI Health Innovations
These examples are only some of the many effective ways to apply AI in health care. Artificial intelligence is not a foolproof technology, so medical professionals must not assume it won’t ever make incorrect conclusions.
However, as more researchers investigate how to combine AI with human judgment and experience, people should see further results. They will illustrate how the technology can promote better condition management, diagnoses and more appropriate treatment decisions.
