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Hype or Hope? Three Opportunities for AI to Transform Healthcare

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clairvoyantsoft logoFrom marketing to transportation to financial services – you’d be hard put to find an industry today that artificial intelligence hasn’t promised to disrupt or fundamentally transform in the near future. As with any technology trend – especially one so broad as AI – it can be hard to navigate through the noise and disambiguate where the true opportunities lie.

Healthcare is a term that’s almost as broad as AI, so when you combine the two, it can be a challenge to know where to start. Healthcare is a massive industry with multiple sectors ranging from services and facilities, to devices and equipment, to insurance, to pharmaceuticals. Multiple healthcare sectors and seemingly endless potential AI applications are a breeding ground for confusion.

In an attempt to cut through the hype, here are three promising areas where AI can realistically transform healthcare – a traditionally risk-averse industry that’s been often resistant to change.

Read More: How to Ensure AI Doesn’t Make Your Customers Hate You

Enhancing Efficiency of Day-to-Day Processes

 AI (with the help of Hollywood) stirs up images of robots indistinguishable from humans, or at a minimum, something sexy like flying taxis. The truth is that AI (or some subset like machine learning, which is what’s likely being used in most cases) has an opportunity to transform industries by enhancing the efficiency of the most mundane tasks. In healthcare, these regular and repeatable tasks include things like record keeping (electronic medical records), process management & scheduling, and prescription refills.

For process management, AI in healthcare can be used to reduce long hospital wait times and other logistical problems. For example, AI-powered systems could be used upon a patient’s arrival to route questions to a doctor or specialist with the best outcomes for a patient’s symptoms instead of just sending them to the first doctor available (especially useful in an emergency room where the problem isn’t obvious like a broken leg or some other injury).

One of the most common problems in healthcare today is staffing. While this is no different than any other industry from banking to food service, too few staff in healthcare doesn’t just result in a long wait to deposit a check or order a hamburger – it can compromise patient care and put people’s health and even lives at risk. Using both internal and external data, hospitals and other healthcare facilities could benefit from machine learning algorithms that leverage time series forecasting to uncover patterns in data to predict admission rates per day and time.

Read More: AI in Cybersecurity: Applications in Various Fields

Leveraging Data for Efficient Diagnostics

The area of AI-assisted diagnostics is still in its nascent stages, but does hold significant promise. Currently there are a lot of global AI initiatives in healthcare using advanced AI algorithms to process multiple health-related data points like images, videos, DNA data, lifestyle and diagnostic data to create different health management scenarios for each patient. Integrating advanced big data techniques to manage and process this data is where such initiatives can be accelerated and made effective.

For example, AI-powered algorithms are being used extensively for skin cancer diagnosis. Scientists at Stanford University have created algorithms that can visually diagnose with “inspiring accuracy” a potential for skin cancer using a database of nearly 130,000 skin disease images. Here are a few other examples of AI initiatives for diagnostics:

Generating effective outcomes around these areas requires management of massive quantities of data, including leveraging capabilities of the cloud and data engineering. Some of the AI models in this space are still evolving and will require significant additional input from healthcare specialists and data scientists to come up with right models that will deliver on the promise of AI in this industry.

Leveraging Data for Better Treatment

 While AI is helping to drive innovation in health diagnostics, the next stage is to leverage data to deliver better treatment – ideally getting to personalized medication for every patient – with significant benefits around reduced cost and much higher healthcare effectiveness. This includes everything from doctor-patient communication and AI support to doctors for better decision making, to the enhancement of health monitoring and management through multiple wearable devices.

Caring for patients remotely (telemedicine) has existed for awhile. However, with improved online video conferencing services, smartphones, and wearables, telemedicine has gotten a lot easier. Integrating AI capabilities into telemedicine platforms will help doctors recommend better treatment plans. For example, an algorithm could track every treatment for bronchitis and then ask patients how long it took them to get better. The platform could then adapt and recommend treatments based on past success rates.

For pharmaceuticals, it can normally take 10 or more years of trial and research before a drug is approved and brought to market. This time can  be reduced by combining existing research data with machine learning algorithms. Predictive modeling could be used to identify the types and combinations of chemicals for drug development. Once a patient is taking a certain drug, live data from the patient could be used and optimized in an AI system where the model is constantly adapting as the patient’s medical history changes based on multiple factors.

Read More: Smart Managers Know There’s More to Leading a Team Than Simply Being a Boss

Conclusion

As in every industry, AI in healthcare is evolving with lots of room for new applications that range from reducing paperwork to discovering new treatments and cures for diseases. With realistic expectations and strategic adoption by healthcare institutions and providers, increasing efficiency and reducing costs in day-to-day processes, diagnostics, and treatment – all to the effect of better patient outcomes – are areas in which for now, the hope will largely continue to outweigh the hype.

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