Artificial Intelligence is becoming a significant part of our daily lives, with applications ranging from self-driving cars to delivery drones.
Its impact on the healthcare industry has been no less important, with the technology being integrated into procedures and diagnoses as well as helping physicians work better together through information sharing. Here are some ways how artificial intelligence is changing the health industry.
1) Enhanced working together through information sharing
Another way how artificial intelligence is changing healthcare is through the ability to share knowledge across an entire organization. Since AI systems tend to process data more quickly and accurately.
In particular, through appropriate NetSuite Implementations, AI systems can also be used for collecting and organizing medical records from numerous hospitals which would be harder for traditional methods without risking human error or loss of valuable clinical knowledge.
With that being said, a study from 2016 showed that medical errors in U.S. hospitals increased by 263% over 12 years leading researchers to conclude that one of the most important applications for AI in healthcare is related to interoperability standards which would allow patient records from different hospitals and providers to be shared more easily.
2) Better diagnosis
One of the most common applications for artificial intelligence in healthcare is related to early diagnosis of diseases, which can often be crucial for effective treatment. Using data from MRIs and CT scans, AI systems are able to process large amounts of information quickly and make comparisons that humans have a hard time achieving.
For example, in 2016, researchers at NYU Langone’s medical school developed an AI system that was able to predict the severity of brain bleeds in CT scans with 95% accuracy after analyzing almost 137,000 cases.
There are examples where advancements in data analysis allowed for earlier diagnosis of diseases as well – to give a couple of examples, a study from 2016 showed that artificial intelligence was able to detect diabetic retinopathy up to three years faster than traditional methods and researchers from the University College London used deep learning algorithms for breast cancer diagnosis in May 2017.
Currently, doctors rely heavily on patients’ self-reporting when diagnosing a disease or identifying early symptoms before a serious condition develops. Artificial intelligence can help solve this problem by better understanding the relationship between biology and behavior using data tools to analyze data from human genomes, biological processes, and neurological patterns.
With this information, the system can help identify early warning signs of diseases like Alzheimer’s or Parkinson’s before they become a major problem for patients by analyzing brainwaves and other sensor data from wearables.
3) Improved patient care
Another way how AI and IoT Tech is changing healthcare is through remote patient monitoring. These days, it is no longer rare for patients to be monitored remotely by doctors who are hundreds of miles away.
AI systems allow clinicians to monitor patients day and night through wearable devices or smartphone applications that monitor their state of health. For example, researchers at IBM developed algorithms that can predict the onset of heart attacks by analyzing data from wearables which are more accurate than traditional methods with zero false positives.
4) Digital Pharmacies
With AI-assisted drug discovery, pharmaceutical companies can reduce the time to develop a new drug from several years to a few months by analyzing data from academic literature, patents, and computer models.
Research companies such as Recursion Pharmaceuticals use artificial intelligence to uncover correlations that may point out promising new molecules for treating certain diseases or conditions.
That is done by searching through massive databases filled with information about genetic sequences and the structure of thousands of different compounds against those associated with particular diseases.
These potential drugs are prioritized using machine learning algorithms based on how well they match up to proteins linked with specific disorders. The system can learn from its mistakes through continuous monitoring and adjust its predictions based on new information gathered through continuing medical education marketing.
5) Makes clinical trials more successful
Clinical trials are expensive projects that take a lot of time and manpower before yielding results, but artificial intelligence technologies have shown themselves capable of expediting the process by speeding up data analysis and suggesting new directions, as well as automatically detecting the effects of different treatments.
For example, IoT technology and AI systems can help researchers find out how new drugs will interact with patients without even testing them on volunteers and they can reduce the number of unnecessary tests required in clinical trials by analyzing data from wearable devices.