Artificial intelligence (AI), machine learning (ML), deep learning and cognitive computing are just a few of the hot buzzwords that are engulfing every industry whether that be manufacturing, finance, or healthcare. In fact, Gartner’s 2016 hype cycle for emerging technologies puts machine learning at the peak of inflated expectations.
Regardless of what people have to say about AI, one thing is clear: it is here to stay and has the potential to transform several aspects of the “human ecosystem”. The healthcare industry is not immune to this hype. In fact, I believe that AI has a major role to play in transforming the entire industry in a significant way. I am not saying that we will have robots replacing doctors, but AI can certainly partner with doctors in delivering superior care that can result in better patient outcomes.
In a 2016 study by Frost & Sullivan, the market for AI in healthcare is projected to reach $6.6 billion by 2021, a 40% growth rate. The report goes on to say that clinical support from AI will strengthen medical imaging diagnosis processes and that using AI solutions for hospital workflows will enhance care delivery. Frost & Sullivan also reports that AI has the potential to improve outcomes by 30 to 40 percent and at the same time cut the costs of treatment by as much as 50%.
The rapid commercialization of machine learning and big data has helped bring AI to the forefront of healthcare and life sciences and is set to change how the industry diagnoses and treats disease. There are some very clear use cases where AI has a major role to play. Some examples include in the field of diagnosing diseases whether that is cancer, diabetes, or some other chronic conditions. For example, AI and machine learning can help improve cancer diagnosis and treatment.
Lots of pioneering work by several organizations is enabling computers to detect signs of cancer earlier than humans are currently capable of, thereby enabling doctors to use all the huge quantities of patient data available to make more personalized treatment decisions. The potential for AI-enabled machines to quickly learn and understand new medical functions, and then critically provide doctors with the necessary information to diagnose problems, could be monumental. The potential application of AI in healthcare could even grow to possibly predict future illnesses even before they manifest, improving the quality of services for patients.
I can think of several use cases in the pharma world where AI could play a major role. Drug discovery/manufacturing and clinical trial research come top of the mind. The use of machine learning in preliminary (early-stage) drug discovery has the potential for various uses, from initial screening of drug compounds to predicted success rate based on biological factors. This includes R&D discovery technologies like next-generation sequencing. Precision medicine, which involves identifying mechanisms for “multifactorial” diseases and in turn alternative paths for therapy, seems to be the frontier in this space. Much of this research involves unsupervised learning, which is in large part still confined to identifying patterns in data without predictions.
Machine learning has several useful potential applications in helping shape and direct clinical trial research. Applying advanced predictive analytics in identifying candidates for clinical trials could draw on a much wider range of data than at present, including social media and doctor visits, for example, as well as genetic information when looking to target specific populations this would result in smaller, quicker, and less expensive trials overall.
ML can also be used for remote monitoring and real-time data access for increased safety; for example, monitoring biological and other signals for any sign of harm or death to participants.
I am certain that AI has a major role to play in, and potential to transform many aspects of, the healthcare eco-system, but as noted in the Gartner hype cycle, we are still in very early infancy of AI’s capability. The technology is still being validated, and challenges, including cost, access to data, and simply understanding how computers reach conclusions, abound. But if this plays out as it could, then we could see major transformation across the continuum-of-care.
I am certainly very excited about AI and its potential and would love to hear back from you on your thoughts around this topic. Contact us to continue the conversation.