We are currently shaping the agenda for the AI Innovations for Life Science and Healthcare Summit East. If you’d like to be involved in this process and/or get involved as a speaker, panelist or roundtable moderator, please contact Aimee Gutzler at or at 917-242-3890.

Conference Day One Plenary Sessions | Thursday, June 13, 2019
Registration Opens and Continental Breakfast
Welcoming Remarks
Leverage Big Data for a Healthier World
  • Understand the implications of the data deluge for the world’s largest biopharmaceutical company
  • Learn about the drivers behind the data growth, the potential it may hold for our ability to improve health and well-being at every stage of life
  • Examine Pfizer’s partnerships with health systems and tech to deliver better care, improve outcomes, and ensure sustainable success
Josh Raysman, Head, AI Center for Excellence, PFIZER
Address Pharma’s Big Data Problem and Even Bigger Text Problem
  • Provide a broad perspective on different Natural Language Processing (NLP) problems in Pharma and how that compares with other industries
  • Review different Deep Learning techniques that can be used to solve Pharma NLP problems
  • Outline how Saama went about solving Adverse Drug Event (ADE) extraction problem
Malaikannan Sankarasubbu, VP of AI Research, SAAMA TECHNOLOGIES
Leveraging Machine Learning/Deep Learning Insights From Other Industries Including YouTube to Address Healthcare Data Requirements:
  • Case Study: Machine learning imaging technology from YouTube translated to cell phenotyping

  • Case Study: AlphaFold — A review of DeepMind's research to use Artificial Intelligence for Scientific Discovery

  • Case Study: Deep Learning Technology use for Electronic Health Records

Zeenat Patrawala, MBA, MS, Partner Development, GOOGLE BRAIN
Networking Break
Conference Day One Tracked Sessions | Thursday, June 13, 2019
AI in Drug and Clinical Development
AI in Healthcare
Wearables and Digital Therapeutics
Track Chairperson Welcoming Remarks
Amit Gulwaldi, Senior Vice President, Clinical Innovations, SAAMA TECHNOLOGIES, INC.
Track Chairperson Welcoming Remarks
Evon Holladay, Chief Operating Officer, A HEALTHIER WE
Track Chairperson Welcoming Remarks
Dave Malenfant, Executive Vice President, Industry Liaison and Talent Development, Bio Supply Management Alliance (BSMA); Director, Outreach & Partnerships, Center for Supply Chain Innovation Texas Christian University
Transforming Clinical Development Using Artificial Intelligence and Machine Learning
  • AI/Maching Learning/Natural Language Processing simplified
  • Discuss choice of models, framework and fabric
  • Identify practical methodology and metrics
  • Hear examples of use case implementations
  • Consider a realistic lens of requirements for successful implementation
Prasanna Rao, Head, Artificial Intelligence and Data Science, PFIZER
Opportunities in Evolving Healthcare Landscape to Explore the Foundation of Specialty Clinical Innovation
  • Introduce disruptive technology to increase clinical outcomes and improve patient care
  • Review innovation performance and impact to measure its true successes within an organization
  • Gain a more profound patient population understanding that is converted back into the care continuum through artificial intelligence systems
Christine Sawicki, R.Ph., MBA, LSSMB, Senior Director of Product Development and Innovation, CVS HEALTH
Bridging the Gap Between Machine Learning and Wearable Technologies in Clinical Trials
  • Use exploratory digital health endpoints to better identify patients who will most benefit from therapies
  • Apply wearable technologies combined with AI algorithms to monitor and prevent adverse events and ensure treatment adherence
  • Hear about a cross-functional team of translational, clinical, regulatory, and marketing experts collaborated to rapidly develop and commercialized digital health tools
Networking Lunch
Machine Learning in Drug Development: Apply Artificial Intelligence to Develop Novel Therapeutics for Neurodegenerative Diseases
  • Hear an example ML platform to improve efficiency during the drug development process
  • Leverage an innovative partnership model to maximize the use and access to a development platform
  • Understand the impact on identifying novel treatments and parsing patient populations by combining ML experts and neuroscience drug developers
Irene Choi, Ph.D., Director, Drug Discovery, VERGE GENOMICS
MONARCSi Case Study: Machine Learning Advancement That Help Physicians Discern Connections Between Adverse Events and Therapeutics
  • Hear about the Machine learning-based tool called MONARCSi
  • This is a decision support tool that is a modification of the well-known Naranjo score that helps physicians and scientists assess whether a drug and an adverse event are related
  • Participate in a demonstration of the system
Bruce Donzati, Ph.D., Senior Group Director, Global Regulatory Pharmacovigilance Innovation Policy, Genentech, Inc
Digital Therapeutics: Software As Treatment
  • Discuss how machine learning can be leveraged to personalize treatment and engage patients
  • Hear how digital therapeutics can produce clinical outcomes comparable to or better than drugs
  • Understand how one digital therapeutics platform can be leveraged to target multiple indications (Click’s platform targets depression, insomnia, chronic pain, and more)
  • Review the regulatory pathway that is open for these apps to obtain drug-like labels
Christopher Jordan, Chief Technology Officer, CLICK THERAPEUTICS
Transform Drug Development With Model Training Predictive Analytics
  • Effectively harness HPC cloud resources for drug discovery
  • Hear an example of how a combination of artificial intelligence and chemistry are accelerating drug discovery
  • Protect proprietary data while leveraging shared, cloud-based HPC resources
  • Discuss how highly accurate computations can help companies save money, save time, and lower risk across all phases of drug development
Brian Martin, Head of AI, ABBVIE
Machine Learning and AI for Precision Medicine — Can It Be Explained and Validated?
  • Identify patterns of care (real-world treatment pathways and temporal patient phenotypes) and outcomes of care with ML/AI
  • Interpret the patterns and models from ML/AI through interactive visualization
  • Support clinical and patient decision-making using the models — opportunities to enable Precision Medicine
  • Validate ML/AI predictions with scientific research — can it be done with observation studies or RCT?
John Cai, Executive Director, Real-World Data Analytics, MERCK
Defining Clinical Standards for Digital Therapies
  • Review the impact on the behavioral change that digital health programs have had on managing chronic disease
  • Discuss the Software Precertification (Pre- Cert) Pilot Program, as outlined in the FDA’s Digital Health Innovation Action Plan
  • Understand the risk-based frameworks established by the Pre-Cert Pilot Program and the impact as a premarket review pathway for lower risk digital products

If you’re a solution provider interested in this session spot or excited to share a new product with our audience, please contact Christopher Summa at 917-932-0432 or

Networking Break
Risk-Based Process Optimization of Clinical Trials
  • Understand the implications of risk-based planning and scheduling of clinical trials with machine learning to improve trial efficiencies
  • Learn about the drivers of uncertainties, long cycle time and high costs associated with clinical trials for process optimization
  • Hear how Bayer is utilizing Machine learning (ML) to improve the core business and to transform to a data-driven enterprise
Kevin Hua, Senior Manager A.I./Machine Learning Development, BAYER
Opportunities in the Evolving Healthcare Landscape to Use Nutrition to Improve Health and Economic Outcomes Via Predictive Analytics
  • Using retrospective datasets to identify patients that will benefit from nutrition intervention
  • How large ACOs and IDNS can target the right patients at the right time for nutrition intervention using predictive analytics
  • How predictive analytics can improve health outcomes for patients with poor nutrition across the continuum of care
Jamie Partridge, Ph.D., MBA, Director, Global Scientific Affairs Global Health Economics Outcomes Research and Health Policy, ABBOTT NUTRITION
Moving Wearables From Monitor to Power Player in Patient-Centered Medical and Clinical Design
  • Understand the untapped potential of health wearable data for various use cases in a clinical and medical design
  • Identify the necessary missing pieces needed for applying intelligence and putting the data to work
  • Recalibrate the approach to data access, sharing, validation and utilization for use in clinical trials/research and precision medicine
Maria Palombini, Director, Communities and Initiatives Development, Emerging Technology, IEEE STANDARDS ASSOCIATION
  • Applications of Artificial Intelligence and Machine Learning in Drug Target Identification
  • Learn how AI/ML can help to address the problem of target identification in drug discovery
  • Understand the knowledge graph (KG) and applying machine learning (ML) to KG
  • Learn an example of using ML on biological knowledge discovery and drug target identification
Pankaj Agarwal, Senior Fellow, Computational Biology, GLAXOSMITHKLINE
Jin Yao, Ph.D., Computational Biologist, GLAXOSMITHKLINE
Platform AI: Using Artificial Intelligence for Predictive Assistance, Partnerships, and Consumer Engagement
  • Learn how to incorporate AI into digital assistants to keep patients on track
  • Model how to extend an AI platform to identify and go faster using strategic partnerships
  • Use internal and partner AI to advance population health by incorporating consumer point of view
Evon Holladay, Chief Operating Officer, A HEALTHIER WE
Gena Koufos, RN, M.S., MBA, Innovation and Digital Health Lead, AMAG Pharmaceuticals
Digitally Detecting Developing Diseases – Computational Intelligence Synergistically Applied to Medical Data and Life Data
  • Learn how BioEngine4D, BioTrillion’s health technology platform applying AI to data generated via Consumer smart devices, can early detect neurologic and respiratory diseases
  • Hear how novel digital biomarkers can be developed by mapping key diseases to digitally measurable expressions and can augment existing molecular biomarkers
  • Consider how common smartphones and smartwatches can effectively act as:
    • “A digital doctor” to continuously check for key disease indicators and catalyze earlier clinical diagnoses and intervention
    • “Clinical trials in a pocket” that disrupt
Savan Devani, Founder and CEO, BIOTRILLION
Day One Concludes
Conference Day Two Plenary Sessions | Friday, June 14, 2019
Registration Opens and Continental Breakfast
Chairperson Recap of Day One
Understanding the Impact of AI in an Organization
  • Discussing your companies starting point for AI and Technology
  • How to invest in AI in a way that not only reduces cost but drives product innovation, revenue growth, operational efficiency, and improved customer experience
  • Review how to embrace and capitalize from AI through new technologies and partnering with established startups to implement them
Andrew Eye, CEO, ClosedLoop
Kevin Hua, Senior Manager A.I./Machine Learning Development, BAYER
Brian Martin, Head of AI, ABBVIE
Prasanna Rao, Head, Artificial Intelligence and Data Science, PFIZER
Networking Break
Conference Day Two Tracked Sessions | Friday, June 14, 2019
AI in Drug and Clinical Development
AI in Healthcare
Wearables and Digital Therapeutics
Operationalize AI and Machine Learning From Theory Into Practice
  • Understand influences that could expedite the adoption and application of AI in drug development
  • Discuss the regulatory acceptance for AI technology across the industry and policies that could change the regulatory landscape for AI
  • Understand industry-wide challenges for the application of AI methods in the development across therapeutic areas
Michael Sachs, Principal Manager, GENENTECH
Rejuvenate Rural Healthcare Delivery Through Artificial Intelligence
  • Review specific examples of utilizing AI to craft and parse the largest clinical electronic medical record database
  • Explore case studies where AI was applied to build tools to improve healthcare delivery, emphasizing on rural healthcare markets
William D. Paiva, Ph.D., Executive Director, Center for Health Systems Innovation (CHSI), OKLAHOMA STATE UNIVERSITY
Use AI to Solve Complex Global Supply Chain Management Challenges
  • Synchronize the digital supply network
  • Become more dynamic, flexible, and efficient in planning and execution
  • Shift consumer expectation
Dave Malenfant, Executive Vice President, Industry Liaison and Talent Development, Bio Supply Management Alliance (BSMA); Director, Outreach & Partnerships, Center for Supply Chain Innovation Texas Christian University
Machine Learning and Artificial Intelligence for Life Sciences
  • Challenges in integrating machine learning and artificial intelligence for biomedical research
  • Overlaying biological knowledge onto data-driven approaches
  • Developing predictive correlates for responses to cancer therapy
Hari Singhal, Lead Data Scientist, ROCHE MOLECULAR SYSTEMS, INC.
AI Augments Clinical Trials With In-Home, 24/7 Patient Monitoring
  • Examine a WiFi-like box that uses ambient radio signals to monitor a patient’s gait falls, respiration, heart rate, sleep apnea, and sleep stages — all without putting any sensor on the patient’s body
  • Understand how the device can also track interactions with the caregiver, and activities such as toileting and eating; based on a new machine learning algorithm that analyzes the radio signals in the environment to learn digital biomarkers
  • Hear how the technology allows for redefining clinical endpoints and pushing clinical trials to the home
Che Ngufor, Ph.D., Associate Consultant I, MAYO CLINIC
Understanding Digital Transformation: The Time Is Now
  • How digital medicine platforms can enable next-generation patient-powered registries and real-world evidence
  • Discuss how the use of technology is partnered with medical societies and industry to create customized digital care and research plans
  • Review how technology enables the collection of real-world evidence from patient- and device-generated data for novel devices, drugs, and digital therapeutics
Ashish Atreja, M.D., MPH, Assistant Professor and Chief Innovation Officer, Medicine, ICHAN SCHOOL OF MEDICINE, MOUNT SINAI, NY
Networking Lunch
AstraZeneca’s Open Innovation Approach: Pushing the Boundaries of Scientific Collaboration Through Crowdsourcing
  • Examine AZ’s multipronged approach to collaboration to foster novel discoveries and speed the development of new medicines for patients in need
  • Determine best practices for collaborating within industrial-academic partnerships
  • Outline translational bioinformatics approaches to gain insight into novel connections between drugs and indications for the repositioning of discontinued compounds — a case study
Leslie Cousens, Associate Director, Translational Medicine and Emerging Innovations, ASTRAZENECA
Connectivity at Kaleida Health
  • Learn about the MyKaledia Mobile app for patients and their families
  • Hear about several tech initiatives at Kaleida Health that aim to improve health outcomes
  • Discuss how Kaleida Helath System is improving patient connectivity
Theo Kornyoh, Chief Technology Officer, KALEIDA HEALTH
Data Wars: The Struggle Between Interoperability, Security, and Progress
  • Best practices in decision management to ensure the subject matter expert is in control
  • Prioritize AI systems that are able to explain decision-making processes
  • Question why your data-driven, unbiased AI project can be derailed by people’s mindsets
  • Find the balance between innovative technology adoption and business goals
Kelly Stanton, Senior Data Scientist, POTRERO MEDICAL
Integrate Artificial Intelligence Across the Commercialization Value Chain
  • Review how ML and NLP are used to improve target identification, candidate selections and formulation development
  • Improve treatment adherence and increase patient recruitment efficiency in clinical trial design
  • Apply AI augmented reality systems to the manufacturing process/up-scaling and post-market surveillance
Ronald Dorenbos, Associate Director Materials and Innovation, TAKEDA
Identify and Overcome Innovation Adoption Hurdles in Healthcare to Utilize Mass Quantities of Data
  • Implement a cutting-edge health data strategy while maintaining compliance with complex privacy and security regulatory frameworks
  • Identify the compliance challenges arising from the use of big data sets, the development of analytics, and the implementation of machine learning
  • Understand the value of achieving positive public perception and patient buy-in concerning health data use
Justin Smith, Ph.D., Enterprise Director of Data Analytics, Enterprise Data and Analytics (EDA), SANFORD HEALTH
Utilize Machine Learning and Artificial Intelligence to Identify Different Types of Risk During the Clinical Trial
  • Understand how machine learning and artificial intelligence can aid in identifying different types of risk during the clinical trial
  • Adapt machine learning and artificial intelligence to better allocate resources and use insights to determine high-risk factors
  • Use both objective and subjective performance measures to determine the proactive actions to ensure the success of clinical trials
Alex (Wen-Yaw) Hsieh, Director — Clinical Development Quality, PFIZER
Conference Concludes

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