Artificial intelligence is driving massive improvement and innovation in the healthcare and life sciences sectors. AI is expediting advances in drug research and discovery. It’s allowing for better and faster diagnoses. And it’s enabling far greater efficiency in business processes.
That’s noteworthy considering so many of us stand to benefit from it. Healthcare is truly one of the industries in which AI stands to have the greatest impact.
Practitioners Can Leverage AI For Faster Diagnosis
AI allows for more accurate diagnoses by getting practitioners clean data quickly.
That clearly addresses a significant pain point in the healthcare arena. Misdiagnosis is estimated to cause up to 80,000 hospital deaths each year and results in billions of dollars in wasted medical spending.
Traditionally, a person would have to crunch terabytes of data to diagnose a patient. Now businesses can leverage AI for number crunching, and human beings can validate the results.
Scientists in China and the U.S. are experimenting with AI in medical diagnoses. My company did a test showing how our platform can be used to detect breast cancer. Bayer, Lunit and PathAI are applying AI to medical diagnosis, too.
Health Insurance Companies Can Employ AI To Automate Claims Processing
Insurance businesses can use AI to improve and automate their operations as well.
Let’s say an insurance company reads the patient email and attached claim. Then the company opens its health claim system and inputs the patient’s policy number. That way the insurance company can see if the patient is eligible for the claim. The insurer then sends a patient email saying something like, “Thank you for your claim. Out of $2,000, you’re eligible for $1,762.26. Your check will go out in 22 days.” This would work in a similar matter in cases in which insurance companies work directly with healthcare providers.
This process can be automated thanks to AI-based integrated automation platforms (IAPs). They automate the claims process end to end. IAPs can ingest, extract and analyze data from claims. They can leverage business rules to understand patient eligibility and coverage. And they can provide that information to the insurer, its partners and the patients.
Pharmaceutical Companies Can Use AI To Accelerate Development
AI is also accelerating the delivery of new findings in drug research. That will enable the pharmaceutical industry to provide doctors and patients with better treatments, faster.
The pharmaceutical industry generated $1.2 trillion in worldwide revenue in 2018, and it’s poised to grow by 160% between 2017 and 2030. However, reports indicate that drug discovery and development have declining success rates and a stagnant pipeline.
AI could help drive improvement on these fronts.
For example, in 2007 a robot identified the function of a yeast gene. A more advanced robot discovered that a common ingredient in toothpaste offered the potential to treat drug-resistant malaria parasites. A machine learning algorithm helped identify a new antibiotic compound. And AI “created” a drug used to treat patients with obsessive-compulsive disorder.
Broad AI Adoption Hinges On Data, Trust, Education and Ethical AI
Having the right platform is just part of the equation to get to widespread AI adoption. AI also needs to have the right data. In addition, AI must gain user trust, which will continue to be built with ethical and responsible use of the technology.
By the right data, I mean enough representative data to get accurate results.
But that doesn’t necessarily mean you need giant datasets. Fractal technology uses relatively small data sets to train the AI engine. It is based on a deterministic science and has been proven by such organizations as NASA.
Imagine, however, that your doctor advised you to buy a $149 thermal camera on Amazon. She told you to take a selfie of your breast and run the image through our platform to get results. You’d probably prefer to go through the painful yet more familiar experience of having a mammogram. Choosing the mammogram might not provide a better experience or results, but it’s what’s known.
That’s where the need for education comes in. Those providing and using AI need to educate patients and healthcare providers that they are in safe hands. They can do that by demonstrating this fact.
For example, for the first 250,000 patient cases, you could have the AI engine provide a result, and you could have a doctor do the diagnosis as well.
You could present both to the patient and/or practitioner, and they would then see the AI is just as good at diagnosing illness as humans.
In other words, the right data will yield accurate results. And when people learn about that accuracy, they will trust the technology. Adoption will increase, and more people will benefit.
Everyone also benefits from ethical AI, which allows for greater accountability, traceability and sustainability. Ethical AI can work to define which AI use cases are and are not acceptable. And it can set rules for specific application requirements. That’s important in healthcare, which can involve life-or-death decisions. The application requirements for AI in healthcare are obviously unique from banking requirements, for example.
Everybody Wants Faster, Better Results
Whatever the sector, reducing time and enhancing customer delight and outcomes are the goals of automation. Those goals are now achievable with AI, fractal science and IAPs.
The future for what this technology will bring to patient care and outcomes stands to be truly transformational. We’ve barely scratched the surface of what’s possible.