Artificial Intelligence (AI) is being heralded as the solution to a litany of problems facing the world today. In the realm of healthcare, AI is being touted as a possible remedy to the structural and physical challenges that face healthcare providers as world populations get older, and new diseases and old illnesses continue to appear and plague society. Just how effective can AI be?
With it being predicted that spending on AI in healthcare is likely to see a 48% increase and with tech research company Gartner assuming that 75% of healthcare organisations will have invested in AI potential by 2021, it is important to know just how AI can be used within healthcare. Machine learning has the potential to provide data-driven clinical decision support (CDS) to physicians and hospital staff, paving the way for increased revenue potential. With algorithms being used to give automated insights for healthcare providers. This can help foster preventative medicine and new drug discovery, something that can be seen in IBM Watson’s ability to pinpoint treatments for cancer patients and Google Cloud’s Healthcare app that makes it easier for health organisations to collect, store and access data.
Researchers at the University of North Carolina Lineberger Comprehensive Cancer Centre have used IBM Watson’s Genomic product to identify specific treatments for over 1,000 patients. The product performed a big data analysis to determine treatment choices for people with tumors who were showing genetic abnormalities.
Similarly, Google’s Cloud Healthcare app includes CDS offerings and other AI solutions that help doctors make informed clinical decisions regarding patients. AI used in the Cloud app takes data from users’ electronic health records through machine learning which enables insights for healthcare providers. 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 return visits and to shorten the amount of time patients are in hospitals.
So, we’ve seen two of the very practical applications of AI in the healthcare sector. Now let us look at some of the more theoretical benefits of increased AI usage in healthcare.
AI innovations could help less developed countries cut down on the healthcare inefficiencies plaguing their countries. The digital infrastructure that AI brings and the ability to bring it to the masses in developing countries through things such as mobile phones or the internet can help patients comprehend the symptoms and receive necessary treatment. Many apps have or are being developed to enhance the collaboration between national and international healthcare organisations to provide quick assistance to people. This can be found in things such as the Ada App, based on intelligent technology, which is currently available in 140 countries which helps increase accessibility to medical guidance for poor people.
AI can accumulate and store people’s data in a single place, this can thus be utilized to see into previous and current health problems for patients. The comparison of disease details enables the physicians to make a more accurate diagnosis. A well known application called Verily, developed by Google specialises in forecasting both noncontagious and hereditary genetic diseases. Such an app enables health experts to anticipate potential threats and avoid them in the future by taking appropriate measures today.
Another useful trick that AI brings to the table is that it can shorten the time and ease the effort needed to examine and diagnose patients. AI has the potential to identify the biomarkers that can detect certain illnesses in the human body. The algorithms ensure the possibility of automating the bigger part of the manual work in specifying these biomarkers. They also reduce the number of expensive lab tests that are currently done to assess a risk, which explains the 88% increase in the number of organisations that have implemented an AI strategy.
AI can also prove to be a big help during surgery. An AI surgical system allows for the performing of the tiniest and most accurate movements. Consequently, complex operations are conducted with minimal pain, blood loss and a low risk of side effects. Furthermore, it has been found that after such surgeries, patients tend to recover much quicker. The implementation of Antibacterial Nanorobots helps clear the patient’s blood from infections either before or after being operated on. Finally, AI helps empower surgeons with real time information concerning the patient’s on the dot condition.
However, as with everything there are disadvantages to adopting AI in healthcare usage.
Two of the biggest concerns are the lack of personal involvement that can spring from robot usage in surgeries. Robots can be completely logical, and feel no sympathy toward patients, some believe that this can be a disadvantage as humans are able to build trust between themselves, something that robots will most certainly lack. Furthermore, the greater use of robots in healthcare due to their lower cost, and greater efficiency will mean that there will undoubtedly be increased unemployment amongst healthcare workers. This will have negative consequences for society as a whole, especially during the current pandemic.
Another concern around AI in healthcare is that the possibility of a defective diagnosis still exists. An accurate diagnosis depends on various data being collected from millions of people who have experienced similar symptoms. To get the appropriate comparison, the AI database should contain sufficient information about the patients of a particular group. If there is a lack of information about a person from a certain background, the AI can provide an inaccurate diagnosis. Which could lead to the wrong treatment being given, which could have fatal consequences.
Then there is the major concern around how AI treats minorities. History has shown that AI cannot fully understand human nature, or indeed it may pick up the prejudices of those who have programmed it. This can have fatal consequences, such as recommending a patient with a low income attend a facility that is beyond their means, which could mean that the patient stops their treatment. Or, a patient being unable to get proper treatment due to the AI adopting a stereotypical attitude toward the patient due to its programming.
Ultimately, as has been highlighted, AI brings a lot of benefits and negatives to healthcare. How these balance out will depend on the professionals using the services on offer, and the interests of the providers. A long and interesting road awaits.
Vivek Rajkhowa 
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