Aritra Das co-authored this blog post with Sonal Singh.
The oncology market has become increasingly crowded and complex incredibly quickly. As an oncology market researcher, you might be feeling a lot of pressure to up the analytical sophistication in your research and capture nuanced decision-making, all while getting insights to your key stakeholders faster. Have you ever wondered how AI could help solve some of the burning challenges that you’re facing, and help you get robust answers to your questions quickly to help you keep up with the rapidly changing market? Here’s how AI can help.
Arup Das co-wrote this blog post with Shankar Viswanathan.
Looking back at 2017, artificial intelligence and machine learning made impressive progress when it comes to improving cancer diagnosis and treatment. Using deep learning, computers are scouring images to detect signs of breast cancer in mammograms earlier than humans are currently capable of. Using AI “random forests,” a learning algorithm, investigators are more accurately predicting which drug combination will work better in BRAF mutant melanoma. The AI-facilitated discovery of Berg Health’s BPM 31510, a pancreatic cancer drug, has entered human development clinical trials. AI is not only powering oncology drug discovery, faster detection and personalized treatment but also helping to improve oncology commercialization effectiveness and agility through analytics.