Three Hot Healthcare AI Categories

Posted on September 26, 2018 I Written By

Anne Zieger is veteran healthcare branding and communications expert with more than 25 years of industry experience. and her commentaries have appeared in dozens of international business publications, including Forbes, Business Week and Information Week. She has also worked extensively healthcare and health IT organizations, including several Fortune 500 companies. She can be reached at @ziegerhealth or www.ziegerhealthcare.com.

The way people talk about AI, one might be forgiven for thinking that it can achieve magical results overnight. Unfortunately, the reality is that it’s much easier to talk about AI application than execute them.

However, there are a few categories of AI development that seems to be emerging as possible game-changers for the healthcare business. Here’s five that have caught my eye.

Radiology: In theory, we’ve been able to analyze digital radiology images for quite some time. The emergence of AI tools has supercharged the discussion, though. The growing list of vendors competing for business in this nascent market is real.

Examples include Aidence, whose Veye Chest automates analysis and reporting of pulmonary modules, aidoc, which finds acute abnormalities in imaging and adds them to the radiologist’s worklist; CuraCloud, which helps with medical imaging analysis and NLP for medical data and more. (For a more comprehensive list, check out this Medium article.)

I’d be remiss if I didn’t also mention a partnership between Facebook and the NYU School of Medicine focused on speeding up MRI imaging dramatically.

Vendors and industry talking heads have been assuring radiologists that such tools will reduce their workload while leaving diagnostic in clinical decisions in their hands. So far, it seems like they’re telling the truth.

Physician documentation: The notion of using AI to speed up the physician documentation process is very hot right now, and for good reason. The advent of EHRs has added new documentation work to physicians’ already-full plate, and some are burning out. Luckily, new AI applications may be able to de-escalate this crisis.

For example, consider applications like NoteSwift’s Samantha, an EHR virtual assistant which structures transcription content and inputs it directly into the EHR. There’s also Robin, also which “listens” to discussions in the clinic rooms, drafts clinical documentation using Conversational Speech Recognition. After review, Robin also submits final documentation directly to an EMR.

Other emerging companies offering AI-driven documentation products apps including Sophris Health, Saykara, and Suki, all of which offer some isotype of virtual assistant or medical scribe functions. Big players like Nuance and MModal are working in this space as well. If you want to find more vendors – and there’s a ton emerging out there – just Google the terms “virtual physician assistant” or “AI medical scribe.” You’ll be swamped with possibilities.

My takeaway here is that we’re getting steadily closer to a day in which simply approve documentation, click a button and populate the EHR automatically. It’s an exciting possibility.

Medical chatbots: This category is perhaps a little less mature than the previous two, but a lot is going on here. While most deployments are experimental, it’s beginning to look like chatbots will be able to do everything from triage to care management, individual patient screenings and patient education. Microsoft recently highlighted how companies can easily create healthcare chatbots on Microsoft Azure. That should open up a variety of use cases.

The hottest category in medical chatbots seems to be preliminary diagnosis. Examples include Sensely, whose virtual medical assistant avatar uses AI to suggest diagnoses based on patient symptoms, along with competitors like Babylon Health, another chatbot which offing patient screenings and tentative diagnoses and Ada, whose smartphone app offers similar options.

Other medical chatbots are virtual clinicians, such as Florence, which reminds patients to take the medication and tracks key patient health metrics like body weight and mood, while still others focus on specific medical issues. This category includes Cancer Chatbot, a resource for cancer patients,  caregivers, friends and family, and Safedrugbot, which helps doctors who need data about use of drugs during breastfeeding.

While many of these apps are in beta or still sorting out their role, they’re becoming more capable by the day and should soon be able to provide patients with meaningful medical advice. They may also be capable of helping ACOs and health systems manage entire populations by digging into patient records, digesting patient histories and using this data to monitor conditions and send specialized care reminders.

This list is far from comprehensive. These are just a few categories of AI-driven healthcare applications poised to foster big changes in healthcare – especially the nature of the health IT infrastructure. There’s a great deal more to learn about what works. Still, we’re just steps away from seeing AI-based technologies hit the industry hard. In the meantime, it might be smart to consider taking some of these for a test run.