Slow and expensive, drug discovery requires an average of ten years and nearly $3 billion to complete, from idea to prescription. To get life-saving drugs to market faster, the industry is working to integrate AI at every stage from research, to clinical trials, and beyond. Drug companies are building massive AI-driven knowledge graphs that reveal critical connections between proteins, molecules, and pathways—empowering them to improve analysis and make better, faster decisions about the viability of drug targets. Genomics data, which is growing at astronomical rates, is driving a revolution in personalized medicine but requires levels of AI compute power that go beyond the limitations of traditional technologies.

Many computer vision use cases in healthcare and life sciences, particularly those around cancer research and diagnosis, rely on how accurately a high-resolution image can be analyzed. The key to more accurate screening and diagnostics across a number of healthcare and pharma applications is the ability to use true-resolution images to train deep learning models without long run times, tiling, and down-sampling — bottlenecks and workarounds necessitated by conventional AI systems that also sacrifice accuracy. Next-generation AI technology is needed to drive real breakthroughs in medical imaging and improve patient outcomes.

COVID-19 advanced telehealth five years in a matter of months. It also exposed a need to make every aspect of telehealth smarter and more personalized. Post-pandemic, AI innovations in telehealth will continue to make virtual care a more mainstream, cost-effective part of the ecosystem. From conversational AI to accurate recommendation models that improve the patient journey—advanced AI models are driving better patient experiences. Accelerating delivery of virtual care, such as doctor matching, symptom checking, and “next-best” action recommendations, AI will be increasingly utilized to help patients navigate the continuum of care while personalizing and improving treatment.