Schedule: Health and Medicine sessions

CB Insights tracks over 3,000 AI startups across 25+ verticals. While every vertical has benefited from deep learning and better hardware processing, the bottlenecks and opportunities are unique to each sector. Deepashri Varadharajan explores what's driving AI applications in different verticals like healthcare, retail, and security and analyzes emerging business models. Read more.

Broader AI adoption and gaining trust from customers requires AI systems to be fair, interpretable, robust, and safe. Anand Rao synthesizes the current research in FAT (fairness, accountability, and transparency) into a step-by-step methodology to address these issues—illustrated with case studies from the financial services and healthcare industries. Read more.

There's significant interest in applying deep learning-based solutions to problems in medicine and healthcare. Eric Oermann and Katie Link identify actionable medical problems, recast them as tractable deep learning problems, and discuss techniques to solve them. Read more.

Artificial intelligence has shown great potential to revolutionize clinical medicine and healthcare delivery. However, incorporating these algorithms into clinical workflows involves a big challenge: convincing clinicians and regulators to trust a “black box” solution. Behrooz Hashemian explains how he's helping make deep neural networks interpretable to provide evidence for clinical decisions. Read more.

Clinical radiology currently faces several clinical issues: improving imaging efficiency, reducing risks, and developing higher imaging quality. Enhao Gong and Greg Zaharchuk explain how Subtle Medical's deep learning/AI solution addresses these problems by enabling faster MRI and faster PET and low-dose scans, providing real clinical and financial benefit to hospitals. Read more.