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Healthcare &9642; Pharma &9642; Biotech

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December 11-12, 2017
Cambridge, MA

December 11-12, 2017 Royal Sonesta Boston Cambridge, MA

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Agenda | AI Application Summit

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DAY ONE | Monday, December 11, 2017


Registration and Breakfast in the Innovation Zone


Chair's Opening Remarks —

Thriving in the New Data-Driven Healthcare Market

  • What are the goals of this data-driven revolution and how far are we from starting to achieve them?
  • Will a completely new business paradigm emerge for next-generation health systems to optimize operations and patient care?
  • Is there room for disruptive entrepreneurs alongside existing companies to succeed in data-driven drug discovery and development?
  • Roll up your sleeves and get ready to discuss what is working and what is getting in the way of flourishing in the world of MoneyBall Medicine

Harry Glorikian
General Partner, New Ventures Funds;
Author, MoneyBall Medicine: Thriving in the New Data-Driven Healthcare Market


Pharma/Biotech AI Application Landscape

  • Defining the spectrum of technologies and platforms comprising AI in the life sciences
  • The key drivers driving life science AI adoption
  • Examining the maturity of AI technologies and their paths forward

Alan S. Louie, Ph.D.
Research Director, Life Sciences, IDC Health Insights

Hospital/Health System AI Application Landscape

  • Innovations and possibilities of diagnostic AI tools
  • AI for healthcare/hospital operational efficiency
  • The challenges and hurdles of adopting AI in healthcare

Daniel Faggella
Founder & CEO, TechEmergence

AI Investment Landscape


Artificial Intelligence in Healthcare: From Drug Discovery to Digital Health

  • Using AI to discover novel therapeutic targets and build a pharma pipeline
  • AI guided clinical development and patient stratification
  • Data driven AI approaches in predictive analytics and population management

Slava Akmaev, Ph.D.
Senior VP and Chief Analytics Officer, BERG


Innovation Zone Break and One-to-One Meetings with Analysts


Provider & Biopharma Data Collaboration: Overcoming Challenges to Realize Opportunities


Daniel Faggella
Founder & CEO, TechEmergence

  • Data is the raw material that drives scientific discovery and data quality has a significant effect on the conclusions that one can draw — How do you ensure and measure data quality from disparate sources?
  • Data interpretation requires complete and accurate metadata — What are the implications of this for biopharma and providers?
  • Reproducible research demands release of both data and open source software — Is this possible to achieve?

John Quackenbush, Ph.D.
Professor of Biostatistics and Computational Biology
Dana-Farber Cancer Institute and Harvard TH Chan School of Public Health

  • Building towards a global Patient / Data / Research Ecosystem where all benefit
  • Patient engagement and empowerment
  • Data services unfettered by institutional siloes
  • Research access that benefits society, not just industry
  • Ecosystem attributes that overcome the growing data privacy, regulatory and other non-technical barriers
  • Ecosystem that is agile, scalable and reusable

Sandy Farmer, Ph.D.
Executive Director, WRD Genome Sciences & Technologies, Pfizer Inc

  • Before you can apply the power of data science, you must have a strategy for defining what is data
  • Many biotechnology companies don’t start with defining their informatics environments as their initial focus is on the science itself, not the storage and usability
  • We are developing a corporate wide data strategy which connects 1) real world evidence and data 2) genetic variation in the patient population to 3) molecular interactions with the protein target

Georgia McGaughey, Ph.D.
Senior Director, Global Modeling & Informatics Group, Vertex Pharmaceuticals


PANEL DISCUSSION Successfully Navigating AI Vendor Management and Partnerships

  • Given the current immaturity of AI solutions in healthcare — Where does one start with vendor management and partnership?
  • Will there be a "one size fits all" for a platform play? Or do we need to evaluate best of breed solutions?
  • How will rampant M&A activity in the AI market impact vendor management and partnerships?
  • What security implications are there for AI in highly regulated industries such as healthcare and life science?


Lynne Dunbrack
Research Vice President, IDC Health Insights


Mark H. Michalski, MD
Executive Director, MGH & BWH Center for Clinical Data Science

John M. Baldoni, Ph.D.
SVP, InSilico Discovery Performance, GSK Pharmaceuticals

Chris Belmont
Former CIO, MD Anderson Cancer Center


Lunch in the Innovation Zone and One-to-One Meetings with Analysts


PANEL DISCUSSION The Human Side of AI — Getting from Technological Euphoria to Clinical Impact

  • Hear from industry and clinical thought leaders about the realities and near term impact of AI, machine, and deep learning
  • Real-world case studies from early adopters of AI technologies will cover how these technologies areimpacting care delivery and drug discovery
  • Discuss the challenges to clinicians, regulators, and patients as these new technologies take root in healthcare
  • Learn how to prepare your organization for the cultural shifts that AI will force


Armaghan “Rumi” Naik, Ph.D.
Director and Head of Design Sciences, FluNeXt, Sanofi Pasteur


Michael S. Blum, MD FACC
Associate Vice Chancellor for Informatics, Chief Digital Technology Officer, Director, Center for Digital Health Innovation;
Professor of Medicine, Cardiology, University of California, San Francisco

Sameer Badlani, MD, FACP
VP & Chief Health Information Officer, Sutter Health


How AI Augments Human Intelligence and Enables New Business Models in Biopharma

  • AI as a way to augment human intelligence: The importance of combining state-of-the-art AI capabilities with exceptional domain expertise
  • Examples of how AI can dramatically improve productivity throughout the entire Biopharma ecosystem (from drug discovery/development to commercialization)
  • How AI can enable a broad range of business models, from traditional software licensing to unconventional partnerships between AI players and a wide range of organizations
  • Qrativ as an example of a novel type of partnership between an AI player (nference) and a hospital system (Mayo) to discover and develop treatments for diseases with unmet need

Murali Aravamudan
Founder and CEO
nference and Qrativ (Mayo Clinic - nference Joint Venture)

Qrativ’s Murali Aravamudan on “What’s Possible” for AI in Drug Discovery


Innovation Zone Break and One-to-One Meetings with Analysts


PANEL DISCUSSION Data Access & Usability

  • Breaking down data siloes to get answers to unlock the value of the data faster
  • Creating an enterprise platform that leverages your data wherever it is
  • Using AI technologies to link Real World and other data types to achieve interoperability


Murali Aravamudan
Founder and CEO
nference and Qrativ (Mayo Clinic - nference Joint Venture)


Jennifer K. Pai, ScD, MHS
Director, Data Sciences Strategy & Platforms
Merck & Co., Inc.

Unlock Hidden Insights Trapped in "Dark Data" Using Natural Language Processing

  • The impact of not knowing what is in the dark data and why you should care
  • Getting better outcomes by using all of your data
  • Implementing augmented intelligence to ensure physicians are operating at the top of their license

Michael H. Antonoff, MBA
Executive Director, Enterprise Business & Data Services,
Office of the Chief Information Officer,
University of Texas, MD Anderson Cancer Center


Lessons Learned from Boston Children’s — When Hacktivists Target Your Hospital

  • Convey risks all healthcare and life sciences entities now have with reliance on digital technology, including AI, in the age of cyberattacks
  • Enumerate specific risk areas and approaches to mitigation, especially considering risk to routine operations, and not just to data loss
  • Understand critical importance of investment in cybersecurity technology and personnel, and of organizational focus on it

Daniel Nigrin, MD, MS
Senior VP & Chief Information Officer,
Boston Children's Hospital;
Asst. Professor of Pediatrics, Harvard Medical School

Hear Daniel discuss the risks all healthcare and life sciences entities now have with reliance on digital technology, including AI, in the age of cyberattacks: AI in Healthcare IT Security – Why Hospitals are Targets


PANEL DISCUSSION Balancing Security, Privacy and Compliance with AI enabled Accessibility and Usability of Data

  • What are the legal and ethical issues to consider around allowing AI and related technologies into decision making in the healthcare field?
  • Data mining rules — Who has the right to analyze what type of data and what rules need to be in place regarding anonymizing the collected data?
  • What are the most critical issues to consider when creating governing policies around access rights to data?
  • What level of privacy and security protection must be maintained when data is transferred to a third party?
  • Beyond who owns the data — Who has the right to know what the data has revealed through the use of AI technologies?


Jennifer S. Geetter, Esq.
Partner, McDermott Will & Emery LLP


Eric Perakslis, Ph.D.
Senior Vice President, Informatics Strategy and Professional Affairs, Takeda Pharmaceuticals

Joseph Blankenship
Senior Analyst, Forrester


How much can you learn in five minutes? Quite a bit. Immediately prior to the Innovation Zone Reception, hear these companies deliver quickhitting presentations on the latest products, new releases, exciting changes, and more. Each presenter will speak for 5 minutes and then will be available to answer questions during the Innovation Zone reception.


Using Machine Learning to Predict Drug Binding Targets and Side-Effects

  • Machine learning has the potential to dramatically accelerate early-stage drug development by focusing costly and tedious experimentation
  • OneThree has developed a platform of 3 machine-learning algorithms to predict: 1) gene essentiality, 2) binding targets of small molecules in development, and 3) clinical level side effects (such as heart attacks or neutropenia)
  • Platform has benchmarked up to a 90% accuracy and has led to novel discovery on compounds in Phase II development

Neel S. Madhukar, Ph.D.
CEO, OneThree Biotech


AI in Pharma — Beyond the Hype

There is a massive interest in leveraging AI in Pharma. The good news is that Pharma companies can achieve transformational results with AI; However, many Pharma companies do not have the required data foundation to get started and to make use of the technology. Richard Wendell, CEO of tellic, has led this journey as a Chief Data Officer in the Fortune 500. He will speak about how:

  • Traditional approaches to constructing this data foundation can take years and create risk
  • AI technologies enable pharma companies to accelerate the creation of a strategic Pharma data asset, resulting in faster time to value and strategic success with AI

Richard Wendell
Chief Executive Officer, tellic LLC


Limitations of Generic AI-Based Solutions and Need for “Life Science Language Processing”


  • Narrow use cases
  • Algorithms catering to specific need
  • Not leveraging data across pharma & clinical settings
  • Too much fiddling with changing technologies rather than solving pharma & healthcare issues with innovative AI
  • Larger AI missing in current environment

Need algorithms to understand LS language holistically

  • Examples
  • Case study

Gaurav Tripathi
Chief Technology Officer, Innoplexus AG


Close of Day One — Networking and Reception


DAY TWO | Tuesday, December 12, 2017


Breakfast in the Innovation Zone and One-to-One Meetings with Analysts


Chair’s Remarks


How Pharma R&D Makes These Key Business Themes Smarter by Leveraging AI:

Anthony Rowe, Ph.D.
Director, Business Technology Leader – Immunology TA, JPC, JHML, ImmunoScience COE
Janssen Research and Development IT

Getting to Validation — Use Cases From the Front Line — Precision Medicine

  • Scientific underpinnings of microbiome associations to wellness
  • AI/ML technologies used to model and analyze data
  • Applying knowledge-based reasoning and machine learning to medical science producing personalized wellness recommendations

Guru Banavar, Ph.D.
Chief Technology Officer, Viome

Changing the Therapeutic Landscape for Neurodegenerative Diseases: Pairing Machine Intelligence & Cognitive Computing with Big Data to Create Stories

  • Therapies for neurodegenerative diseases suffer from high failure rates (99%) due to lack of efficacy
  • Big Data and Machine Intelligence are key to data-driven discovery
  • Discoveries must be transformed into stories of genes, molecules and disease to be actionable
  • New methods hold promise to turn data, knowledge into stories about mechanism, hypotheses

William Chen, Ph.D.
Head of Computational and Systems Biology, Biogen


Blockchain and Artificial Intelligence in Healthcare

  • AI in drug design and discovery
  • AI for biomarker development
  • Blockchain based and AI driven platform for healthcare data management, data monetization and analysis

Polina Mamoshina
Research Scientist, Pharma AI, a division of Insilico Medicine, Inc.;
Department of Computer Science, University of Oxford

The Learning Supply Chain: Integrating Blockchain and AI to Drive Immutability and Efficiency Across Networks

  • How Life Sciences and Biopharma organizations are adopting and implementing disruptive technologies to manage product diversion, counter illicit drug supplies while conforming to the mandates of the Drug Supply Chain Security Act (DSCSA)
  • Spanning the ‘Source’, ‘Plan’, ‘Make’ and ‘Deliver’ networks: Aspects of Machine Learning and how AI is being introduced to train the digital eco-system that supports these core functions of manufacturing and distribution

Arun Ghosh
Partner – Digital Enablement, KPMG


Innovation Zone Break and One-to-One Meetings with Analysts


The Power of Applications with Embedded Machine Learning for Healthcare

  • Explore machine learning's role in advances in diagnoses and treatment
  • Learn how to mitigate challenges of introducing machine learning to physicians and executives
  • Examine how to prioritize machine learning applications


Alan S. Louie, Ph.D.
Research Director, Life Sciences, IDC Health Insights

The Promise of AI in Healthcare: Pursuing a Master Algorithm for Precision Medicine

  • What problems are being solved by Machine Learning right now to move new treatments from early R&D, through clinical, into the hand of patients
  • New ways that traditional and emerging data are being combined and leveraged
  • How to implement new technologies & what questions to ask along the way

Iya Khalil
Co-Founder & Chief Commercial Officer, GNS Healthcare

Disruption in the Clinical Workflow — Not a Good Thing!

  • Evolution of the clinical workflow & current state
  • Why is automation of the clinical workflow the answer?
  • Move to where the puck will be — Automating workflow using AI and ML

Michael S. Blum, MD FACC
Associate Vice Chancellor for Informatics, Chief Digital Technology Officer, Director, Center for Digital Health Innovation;
Professor of Medicine, Cardiology, University of California, San Francisco


PANEL DISCUSSION Real-World Evidence (RWE) — Improving Patient Outcomes is the Goal

  • What is Real-World Evidence and why do we need it?
  • Differences between real-world data & real-world evidence and why this is important
  • Case studies of early adopters of RWE demonstrating value to organizations
  • What does the future hold for RWD, RWE and advancements in technology?


Alan S. Louie, Ph.D.
Research Director, Life Sciences, IDC Health Insights


Julie Locklear, PharmD, MBA
Independent Strategic Consultant; Former Vice President & Head, US Health Economics & Outcomes Research,
EMD Serono, Inc., a business of Merck KGaA, Darmstadt, Germany

S. Alandra Weaver, MPH
Director, Quality of Care and Clinical Research Programs, Crohn's & Colitis Foundation


Open Q&A and Discussion — You Take Over the Agenda for this 30-Minute Highly Engaging Session

Join AI Applications Summit speakers, IDC Analysts, and your peers in this final session dedicated to addressing your most important open issues and take this opportunity to challenge some of what you’ve heard to continue the discussion and debate. This session takes form in real-time featuring instant break-outs and small group discussions as well as general audience sharing. The “unconference” format is an opportunity to engage with speakers and ensures that everyone at the AI Applications Summit leaves with actionable insight and a more advanced AI strategy.


Close of Conference


Speaker Invites & Submissions
Event Partner Opportunities

Debra Chipman
Corey Lane Partners
[email protected]