A generational shift has come to healthcare and pharma. Today’s practitioners are flooded with increasing amounts of data in their day-to-day practice. Navigating this flood of data takes significant time and effort. This often means less time for delivering the human element of care and addressing the real-life needs of their patients.
With AI innovations that push the limits of today’s technologies, providers and researchers are increasingly able to focus on the crucial human element of healthcare. In areas such as tele health, drug discovery, and disease diagnosis, we’re already seeing multiple use cases for AI applications, including:
- Exponential advances in computer vision technology that will allow practitioners to rely on AI to help identify conditions like diabetic retinopathy, multiple sclerosis, or dementia.
- In drug discovery, AI has the capability to accelerate time to insight with greater efficiency and accuracy than current methods. Increasingly intelligent deep learning recommendation models (DLRM) can help predict the viability of targets in the drug discovery pipeline.
- For tele health, innovations in recommendation models and conversational AI will allow AI to go beyond matching patients with practitioners and consider other variables, such as personal preferences for doctors, insurance coverage, location, and scheduling availability.