AI in healthcare, while having enormous potential, is also subject to obstacles that characterize the complex relationship between the two
Words by Dr. Ryan Grech and Dr. Dylan Attard, Clinical & Health Tech Advisors for MedTech World and two of the co-founders of Digital Health Malta.
No one said it was going to be easy, health tech when done right is hard. And perhaps a testament to this is the news released in February with IBM mulling the sale of Watson, the culmination of its AI strategy. Deliberations are at an early stage and for all we know, IBM decides against parting with Watson but if even a computing giant has second thoughts now and again then it’s ok for us mortals to grumble with our pals about how hard health-tech can be. In reality, it might be that IBM under the new leadership of Mr Krishna is looking to consolidate it’s already demanding business operations and focus more on hybrid cloud computing operations but deep down, the fact that it is not yet profitable despite having $1 billion in annual revenue might also play a role.
The complex relationship between healthcare and AI
Data is king for AI. We know that. Unfortunately, the data collected in the healthcare industry can be messy. Although there are agreed protocols to clinical data organization (such as SNOMED), most clinicians do not use these in daily practice and who can blame them? Given the visually appalling EHRs with dated UX, ever-increasing workload and bureaucracy (but that is a story for another time). Dealing with this kind of data will make it harder for AI and may easily induce unwanted biases.
Another issue is the complexity of medical data in that it is non-linear. Every condition has multiple compounding factors ranging from environmental to genetic factors. Some of this is even not understood by humans, so how can you teach it to a machine?
Another spanner in the wheel is data privacy and security. Yes, it is an issue in all industries, but healthcare data is certainly more sensitive than what kind of pizza toppings you love. As a result, access to the data is harder and the level of security that needs to be implemented is certainly costlier.
And what about the involvement of healthcare practitioners? This may be the most important barrier to successful adoption of AI in healthcare. A company will struggle to get its product to market if the clinicians are resisting it, especially if being force fed by management, and this may be due to several factors such as lack of trust or lack of education.
It’s not all grim
So yes, health tech is difficult and it’s not surprising that companies do struggle at times. The regulatory landscape is still evolving, patients are still getting used to the idea of AI and healthcare, in general, is known to be slow to adapt to change. Even so, I think that start-ups have an advantage over giants like IBM? Start-ups have a positive attitude with passionate and determined founders that understand the depth of the problem they are trying to solve. Moreso, start-ups tend to be focused on one issue in healthcare. IBM had perhaps set overly ambitious targets by trying to fight cancer and taking on chronic diseases. Start-ups can also be agile and flexible, constantly changing and evolving to accommodate both the regulatory landscape and the delivery of their products and services.
Undoubtedly, the opportunity for AI in healthcare is enormous and I believe that the future is bright. It is not only cool, but we have an opportunity to change millions if not billions of lives. We have an opportunity to finally have true health equality and the push needed for personalized medicine.
So for all start-ups out there how will I as a clinician feel comfortable using your service?
- Evidence, evidence and more evidence. Your product needs to work and not only in an artificial silo but it must be proven to work in my day to day work.
- Include us in the design of the product, we know what we need in the real world. We know what works.
- Make sure you get the right certification, put our mind at ease that it is safe.
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