February - 20199product adherence, clinical development and trials for new indications, and submission in other markets, to name a few.It would take years to understand use cases of AI for every single aspect of this complex industry, but the common theme is simple: Driven by precise data sets, AI will shorten the amount of time it takes to solve business problems and meet objectives. Let's explore some of the potential use cases.Improving Drug DiscoveryFirst, AI has the potential to find new therapies. Machine learning (ML) is making the drug discovery process cheaper, faster, and more optimal for all involved. Startups like Berg and Benevolent Bio have each developed their own AI platforms to analyze obscenely large amounts of biological and clinical data in order to discover new cancer and neurological therapies. Additionally, modern predictive analysis technologies have the potential to improve drug pipelines through computer simulationsData-Driven & Precisely Personalized Treatment PlansHighly personalized treatment plans are also on the horizon due to advances in AI and remote patient monitoring. Last year, AiveCor's Kardia band became the first FDA-cleared Apple Watch band, upgrading the Apple Watch to a medical device, AliveCor, recently named the number one Most Innovative Company in AI by Fast Company, "enables patients and their care teams to easily, quickly and inexpensively detect and manage possible abnormal heart rhythms." The band functions as an electrocardiogram machine and is 84 percent accurate at detecting one's normal heartbeat from a trial fibrillation, which can cause stroke. A cardiologist can now remotely monitor a patient versus seeing them once a year, allowing the physician to create precise treatment plans, which in turn affect pharmaceutical sales volumes and provide anonymized patient insights leading to more relevant therapy options.IBM Watson is also at the forefront of AI, optimizing patient treatment options based on medical history and information. Remote patient monitoring, better data flow, and predictive analyses are all allowing for optimized treatment plans and better outcomes.Staying on Therapy with AIDrug adherence is another area where AI is improving patient outcomes. AiCure's intelligent medical assistant uses a HIPAA compliant visual recognition platform to track patient therapy use. The product provides visual dose confirmation, interactive patient support, and visual diagnostic capabilities. A more basic but effective example is that many manufacturers are launching SMS interventions and ML enabled chatbots to adapt to patient needs, providing dosing day reminders and refill reminders, thus improving product adherence.Re-Thinking Traditional Governance ModelsOf course, before pharma can truly embrace AI in a mainstream way, we need to understand the regulatory considerations and potential hurdles. Data security, patient privacy, accuracy, and lack of infrastructure are just a few initial concerns. Regardless, AI is here and the industry must get comfortable with being uncomfortable. And from a regulatory body standpoint, we're off to a decent start. Because AI can increase datasets of relevant information that directly support the FDA's goals, cross-functional groups such as the Digital Health Unit have been created to understand AI's possibilities and obstacles. The Unit is comprised of AI experts, healthcare industry members, and members of the FDA.At the end of the day, AI has boundless potential to solve business problems and meet objectives within pharmaceutical organizations, thus improving patient outcomes in a much shorter time span. But the most important thing to remember is to not be distracted by the shininess and newness of AI. Always ask yourself: What business problem am I trying to solve, what outcome am I looking to achieve, and will AI get me there faster? More importantly, is this what's best for the patient? If the answer is yes, you're at an optimal starting point. You will also need to get used to new models internally if you're going to succeed. Consider a new approach to digital and technology governance internally that embraces agile approaches, testing ideas, and unlikely partnerships such as AI startups. Machine learning (ML) is making the drug discovery process cheaper, faster, and more optimal for all involvedRyan Billings
< Page 8 | Page 10 >