The healthcare industry plays a critical role in our society, so it’s no surprise that it has been actively adopting the latest technologies. However, it hasn’t always been easy as healthcare institutions have had to find ways of protecting sensitive patient data while optimizing the system.
One way they’ve been able to accomplish this is by leveraging innovative machine learning (ML) algorithms that can operate without human intervention. This means that large healthcare-related datasets can now be processed without breaching any regulations or confidentiality contracts.
This has the potential to transform the industry as we can now use these models to better analyze and understand risk factors, diagnoses, and coefficients of causation. This, in turn, will lead to better clinical decision making and enhance patient care.
Several startups have already popped up to bring ML to healthcare, these include Ayasdi, Digital Reasoning Systems, Nervanasys, and Sentient.ai (among others).
Check out a related article:
So how will ML change healthcare? What can we expect in the near future? Let’s take a look.
ML in the Rapid Detection of Cancer
ML algorithms are already proven to be just as good as dermatologists in detecting cancer. This could be a game-changer for almost 5.4 million new cases of cancer that are identified each year in the U.S. alone. In this scenario, the early detection of melanoma will be key to improve survival rates.
When ML algorithms are employed during the examination process, it will combine visual processing with deep learning (DL) algorithms to help doctors identify the problem.
ML can also be used to help identify cancerous tumors on mammograms. Researchers at the Mayo clinic took it up to another level by leveraging artificial intelligence (AI) and ML to diagnose brain tumors without performing a biopsy. This means that DL algorithms are capable of uncovering features that undetectable in regular MRI scans.
ML in the Discovery of New Drugs
ML in drug discovery has been around for a while, especially with IBM’s health applications. More recently, we saw Google jump into the drug discovery business and a host of startups are currently raising funds to make their mark with the help of ML.
While ML can certainly accelerate the whole process of discovering new medicines, it can also help scientists better understand and design new drugs accordingly. But these new initiatives aren’t free of objections, so it will be interesting to see how things evolve going forward.
Check out a related article:
ML in Automatic Recommendation or Treatment
While it might take some time before we see it in action, autonomous treatments are a real possibility. This is because the legal constraints surrounding the idea of putting the lives of patients in the hands of AI will demand long trials to prove its safety, viability, and superiority to current treatment methods.
Once authorized, we can expect to see things like insulin checking pumps working autonomously to monitor blood-glucose levels and inject insulin as needed. This has the potential to enhance the quality of life of diabetics as they won’t have to interrupt their daily lives to inject insulin.
This can even be extended to devices adjusting the dose of antibiotics or painkillers by tracking data about their blood, diet, sleep, and stress levels. This also has the potential of saving lives as human beings are notoriously bad at adhering to a medication regimen.
It’s still early days for AI and ML in healthcare, so it will only get better in the future to provide more benefits to diagnosis and prevention initiatives. If you haven’t already experienced AI in healthcare, you soon will.
While deployment might be slower than most of us would like, pretty soon we can all expect it to play a significant role behind the scenes making healthcare more efficient and cost-effective.