A Traverse of the Elements of AI/Machine Learning Across Healthcare

AI and machine learning are poised to create a paradigm-shift in many areas of the healthcare sector. A recent Accenture report predicted that the AI/Machine Learning healthcare market could see an ELEVEN-FOLD INCREASE in value in less than a decade – growing from just $600 million in 2014 to $6.6 billion in the next three years, and creating $150 billion in annual savings for the US HEALTHCARE ECONOMY by 2026! It may not be overly ambitious to say that we are, in fact, on the brink of a revolution that will change every area of the healthcare and life science industries.

“Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world.”

Atul Butte, M.D., Ph.D., Director, Institute for Computational Health Sciences, UCSF and Chair AI/Machine Learning Track, PMWC 2019 SV

The pharmaceutical industry is engaging AI to incorporate analytical solutions to accelerate drug discovery and development, the clinical sector looks to expedite diagnosis and treatments, the clinical trials sector hopes to better stratify patients based on their genetic make-up. With that, we see a shift towards prevention, personalization, and precision medicine. The PMWC 2019 Silicon Valley program traverses the various aspects of AI/Machine learning in healthcare which include:

  • An overview and discussion of their successes and failures
  • Applications in pre-clinical, drug design, discovery and prediction of drug development success
  • Challenges of applying AI on biomedical data
  • Ethical, privacy, and policy implications
  • Infrastructure, platform, and large data demands of AI and machine learning
  • FDA regulation of algorithms for decision support
  • AI in precision imaging to assist clinical decision-making and outcomes predictions
  • AI revolution in clinical trial design and patient selection
  • NGS knowledge extraction applications
  • Real-world evidence-based diagnostics and genomics-guided precision medicine


“We’re already seeing examples of progress, examples where an AI-enabled approach has brought novel, relevant insights into drug development – and I’m confident that we’ll see more and more of this as the technology teams robustify and add relevant drug development domain expertise.”

David A. Shaywitz, M.D., Ph.D., Senior Partner, Takeda Ventures, Inc. and “Big Data Revolution in Drug Development: Reality or Hype?” session chair

“Data-driven innovations are transforming every facet of our lives – most notably, healthcare and the life sciences. Big data and advanced analytics are powering the next great industrial revolution. The promise and potential are enormous: from novel medicines to better prediction of disease to selection of better interventions. Big data approaches have also been the subject of much hype and skepticism.”

Aris Baras, M.D. MBA, VP and Head, Regeneron Genetics Center

“Machine learning has demonstrated remarkable capabilities for solving hard problems, when given enough data; the synthesis of biology and technology has provided us with an array of amazing technologies to produce biological data at scale. Together, these open the door to a new paradigm for biological discovery and for tackling challenges in human health.”

Daphne Koller, PhD, Founder & CEO, Insitro

In the face of such rapid and ground-shaking changes, there will always be new challenges. When it comes to AI and Machine Learning, there are still legal, ethical and regulatory questions that remain unanswered.

“AI and machine learning show great potential to improve health and health care; however, the promise of AI won’t be achieved if we don’t attend to the ethical, legal and social implications at the front end and, continuously thereafter.”

Camille Nebeker, Ed.D, M.S., Director, Connected and Open Research Ethics, UCSD, School of Medicine

Looking forward to seeing many of you at the upcoming PMWC 2019 Silicon Valley conference on January 20-23 at the Santa Clara Convention Center, CA.

The PMWC team