Editor's note: What market forces or trends will shape the biopharma industry in 2018? In this series, BioPharma Dive's editors will take a stab at predicting how the year may unfold for AI, oncology and neuroscience. Stay tuned for more predictions to come.
"Alexa, remind me to take my medication at 4pm."
"Siri, contact my patient support advocate."
"Hey Google, tell me if mom took her pill today."
While not a reality yet, commands like these to favorite AI companions are well on their way. Expect to see pharmaceutical companies allying with tech giants to offer patient support programs as they try to tap into the latest trends.
Such programs can be a differentiator for a drug in a crowded marketplace. For example, the type 2 diabetes market is flooded with treatment options and most of them are virtually interchangeable. But companies that can help patients to take their medication or deal with their disease can get a leg up on competitors. Offering patients technological options that can make adherence and dealing with the burden of their disease easier may go a long way to improving patient quality of life — and increasing market share.
In 2017, we saw approval of the first digital pill — a form of the antipsychotic Abilify (aripiprazole) embedded with a digital sensor to track whether a patient has taken their medication. It's not a far leap to predict more digital pill approvals, and eventually caregivers could ask Amazon's Alexa or Google Home to ensure elderly parents or other chronically ill family members are taking their medicines as prescribed. Use of this AI tech could also help physicians see if patients are following orders.
Artificial intelligence is still in the early phases of the technology and the pharmaceutical industry is scrambling to determine the best uses.
But AI and machine learning are already being used in drug discovery to sort through tons of data to help companies better select drug candidates and find the targets that compounds are most active against.
Forms of AI are also being used to help improve clinical trial efficiency by sifting through data in tasks that would take human counterparts substantially longer. The technology can look at patterns in clinical trial data and potentially spot human errors. It can also find patterns that indicate misconduct at clinical trials sites — something that would likely require months of time, plenty of cash and a human visit to the site to determine otherwise.
Moving further along in the drug development process, AI is now just starting to be used to read radiology scans, and giving physicians more time and a better way to interpret the data.
Pharma companies are also using AI for more mundane things like repetitive accounting practices and compliance forms. Complying with clinical transparency sites like clinicaltrials.gov requires substantial manpower and thousands of hours. This task may be simplified by AI technology.
To be sure, there will likely be worries about privacy and other fears of the unknown among patients, physicians and others. Like self-driving cars, which still make people uncomfortable, pharma companies are going to need to ease patients into using this new technology.