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Banking and financial services may have led the way in leveraging artificial intelligence to transform their business. And the healthcare industry is not far behind–investing in massive improvement and innovation. AI is speeding advancement in drug research. With the goal of providing better and faster diagnoses and results in greater efficiency for business processes. Healthcare is truly one of the industries in which AI stands to have the greatest impact. We all stand to benefit from it.

Healthcare Practitioners Employing AI for Accurate Diagnosis

AI allows for more accurate diagnoses by getting practitioners clean data quickly. That 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. Between 10 to 15 percent of diagnoses are incorrect and average price associated with misdiagnosis is $386,849 per claim.

Historically, a healthcare professional would have to sort through terabytes of data to diagnose a patient. Now healthcare providers can leverage AI for calculations and physicians can confirm the results. Scientists in China and the U.S. are experimenting with AI in medical diagnoses. AntWorks conducted a test showing how our platform can be used to detect breast cancer. Pharma giant Bayer, partnered with tech companies to better diagnose and formulate treatments using AI. PathAI and Lunit are healthcare startups using AI to predict and diagnose faster and more accurately.

Health insurance companies can utilise AI to automate claims processing

Insurance organisations are using AI to improve their operations. For example, an insurer reads a patient’s email and attached claim. Then claims adjustor opens their health claim system and inputs the patient’s policy number. The insurance company see if the patient is eligible based on the claim. The insurer can send a patient email stating, "Thank you for your claim. Out of $2,000, you're eligible for $1,762.26. Your check will be mailed 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). The platform automates the claims process end-to-end. IAPs can ingest, extract and analyse data from claims. Business rules are created to understand patient eligibility and coverage. And they can provide that information to the insurer, its partners and patients.

Aetna enlists AI to settle health insurance claims. Cigna and Humana are using chatbots to quickly address policyholder questions. And Mercer uses our IAP to optimize and personalize quotes for new policies and renewals. AI has also provided a powerful tool in fraud detection which helps insurers keep premiums steady.

Pharmaceutical Companies Can Use AI to Speed Drug 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. Reports indicate using AI platforms would save pharmaceutical companies 3-5 percent, approximately 50 billion dollars.

Back in 2007, a robot identified the function of a yeast gene. A more advanced robot discovered triclosan–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 “built” a drug used to treat patients with obsessive-compulsive disorder.

Greater 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. Another hurdle, AI must gain user trust, which will continue to be built with ethical and responsible use of the technology.

With the right data, there’s enough 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 organisations as NASA.

Imagine, if your doctor advised you to purchase a $149 thermal camera on Amazon. She might tell 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 we are accustomed to.

Enter the need education. Those providing and using AI need to educate patients and healthcare providers that they are in safe hands.  For example, for the first 250,000 patient cases, you could have the AI engine provide diagnoses and have a doctor do the diagnosis as well.

As a basis of comparison, present both to the patient and/or practitioner, and this would validate AI is just as good at diagnosing illness as human physicians.

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 unique from banking requirements.

Everybody Wants Faster, Better Results

Reducing time to diagnose and improving patient care with better outcomes are the goals of leveraging automation technology. Those goals are now achievable with AI, fractal science and IAPs. The future for what this technology will bring to patient care and better outcomes could be transformational. We’ve barely scratched the surface of what’s possible.

The original article was published on Forbes.com  

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