AI in Health
December 4-5, 2018 | Download Brochure
Artificial intelligence in the healthcare industry is predicted to save $150 billion annually for the US. As such, AI is being rapidly deployed in many areas of the healthcare landscape. This event will primarily focus on the Providers, attracting
CIOs, CTOs, IT and Informatics Experts along with senior Physicians and Clinicians from the leading US hospitals who will share their experiences of using AI in the clinical care and hospital operations environment.
12:00 pm Keynote Exhibit Hall Presentation (Sponsorship Opportunity Available)
11:00 am - 2:00 pm Concession Stand Open for Lunch in the Exhibit Hall
Sandy Aronson, Executive Director of IT, Partners HealthCare Personalized Medicine
1:15 Healthcare Platinum Sponsor Presentation (Opportunity Available)
1:40 CO-PRESENTATION: Disruptive Innovations for Payer Decision Support: How Artificial Intelligence Can Drive Positive Change in Healthcare Delivery
Suzanne Belinson, PhD, MPH, Executive Director, Clinical Markets, Office of Clinical Affairs, Blue Cross Blue Shield Association
Jelani Akil McLean, EdD, MPA, Managing Director, Clinical Platforms, BlueCross BlueShield Association
Artificial intelligence (AI) is transforming the way payers engage with the healthcare market. The use of AI is leading to improvements in healthcare delivery through consistent technology evaluation, efficient decision-making processes, and effective communication with providers and consumers of care.
2:00 Integrating Algorithmic Generated Techniques into Clinical Care and Decision Making
Sandy Aronson, ALM, MA, Executive Director of IT, Partners HealthCare Personalized Medicine
It is a very exciting time in healthcare. New types of data that could improve the way we care for patients are increasingly becoming available. AI techniques pioneered in other industries are opening the promise of enhanced clinical decision support based on higher dimensional data than humans can consider. But at the same time clinical processes are resistant to change. Broadly taking advantage of advances inherently involves altering care delivery paradigms. This talk will focus this “last mile” problem and focus on infrastructure being constructed to help address it.
2:40 AI for Infection Detection and Prevention
Erica S. Shenoy, MD, PhD, Associate Chief, Infection Control Unit, Massachusetts General Hospital; Assistant Professor of Medicine, Harvard Medical School
AI has tremendous promise for improving our ability to detect and potentially prevent hospital-acquired infections. This talk will focus on Clostridium difficile, which results in 500,000 infections each year and 30,000 deaths in the US, and our team’s approach to early detection using machine learning.
3:00 Refreshment Break in the Exhibit Hall with Poster Viewing
3:45 Examining the use of AI for Imaging in Clinical Care
Aalpen Patel, MD, Chairman, System Radiology at Geisinger Health System, Pennsylvania
In recent years, deep learning has revolutionized the field of computer vision. In ImageNet competition, deep learning models are now outperforming humans in object detection and classification. In medical imaging, deep learning has been used in variety of image processing tasks such as segmentation and in recent years, for diagnostic purposes such as diabetic retinopathy and skin cancer detection using large medical datasets. More recently, we have published a paper describing DL based identification of intracranial haemorrhage on CT scans of the head and using it to prioritize the list for interpretation. We believe that using large clinical grade, heterogenous data set is extremely valuable in generalizing and translating to clinical tools. This is just the beginning – combining all the -ologies, -omics with imaging will lead to insights we have not had before.
4:05 Developing and Translating AI-Enabled Applications for Healthcare
Katherine Andriole, PhD, Director of Research Strategy and Operations, MGH & BWH Center for Clinical Data Science; Associate Professor of Radiology, Harvard Medical School
Interest in AI-enabled healthcare applications is growing, but there is a gap between demonstrating proof-of-concept and widespread clinical adoption. The MGH and BWH Center for Clinical Data Science is focused on the full lifecycle of research and development of machine learning applications for healthcare, through to clinical integration and translation. Example AI applications in imaging will be discussed including their promise as well as current limitations.
4:25 INTERACTIVE PANEL DISCUSSION: How are Organisations Leveraging AI to Drive Operational Intelligence?
Adam Landman, MD, Vice President and CIO, Brigham and Women’s Hospital
Karim Botros, Chief Strategy and Innovation Officer, The MetroHealth System
James D. Murray, VP, Clinical Informatics and Interoperability, CVS Health
Mary Margaret Jacobs, Director, Capacity Management, The Johns Hopkins Hospital
5:15 - 6:30 Networking Reception in the Exhibit Hall with Poster Viewing
1:00 Keynote Exhibit Hall Presentation (Sponsorship Opportunity Available)
11:00 am - 2:00 pm Concession Stand Open for Lunch in the Exhibit Hall
David Ledbetter, Data Scientist, Children’s Hospital Los Angeles
2:00 Healthcare Platinum Sponsor Presentation (Opportunity Available)
2:25 Meshing AI and Human Cognition: Managing Risks of Ethics and Bias
John Mattison, MD, CIO, Kaiser Permanente
We are in an exponential trajectory towards meshed thinking in carbon (our brains) and in silico (our machines). There are inherent biases that characterize either form of cognition. The science of cognitive bias in carbon is well defined but managing those known biases are non-existent to primitive at best. The science of machine bias is very nascent at best. The notion of meshed cognition and hence meshed bias is completely terra nova, and should become the focus of deep research by multi-disciplinary teams that span cognitive bias, machine bias, and the future of computer human interfaces as it represents the future of meshed bias and potential mitigation strategies.
2:45 Avoiding Hype and False Conclusions About AI in Medicine: Key Concepts and Examples
Mike Zalis, MD, Associate Professor of Radiology, Harvard Medical School
With advances of machine intelligence in healthcare, key stakeholders risk suffering from an inflation of expectations and misunderstanding of capabilities. This talk will summarize key conceptual underpinnings of machine learning methods and discuss academic and industry implementation examples of AI in healthcare. The goal of this talk is support participants in adroit critical thinking as they face potential applications, initiatives, and products involving AI in healthcare.
3:05 Refreshment Break in the Exhibit Hall - Last Chance for Viewing
3:20 INTERACTIVE PANEL DISCUSSION: AI and Advanced Algorithms in Healthcare from the Investors Perspective
- Investing in AI start-ups in the healthcare industry
- Real world applications of AI in the healthcare industry
- Regulatory hurdles for integrating AI in the US
- Impact of AI on future jobs in the healthcare industry: Will the AI doctor see you now?
- Viable business models and opportunities to unlock value through AI in healthcare for patients
- How are emerging partnerships forming between integrated health systems, R&D and real-world evidence arms of Pharma?
- The key for AI to work in healthcare is to have clean data to run through the algorithms
- For companies that are in the AI of healthcare space, it is important to have a use case for the product versus just having a great algorithm
Navid Alipour, Co-Founder and Managing Partner, Analytics Ventures
Josh Kellar, Partner and Managing Director, Boston Consulting Group (BCG)
Dipa Talati Mehta, Managing Director, Sandbox Industries
Jonathan Gordon, Director, NYP Ventures at New York-Presbyterian
4:20 Multi-Channel ChatBots Strategy from an End User Perspective
Sharad Gupta, Director of Enterprise Architecture, Blue Shield of California
AI-powered ChatBots are increasingly becoming viable solutions for customer service use cases. The objective of this session is to provide technology leaders with a framework to ensure long-term strategic investment in ChatBots. Following are the three takeaways from this session: The “What”: ChatBot and its anatomy The “Why”: Business benefits of ChatBots and common business use cases The “How”: Key considerations in making strategic investments in ChatBots. In the healthcare industry, consider the use of ChatBots for a number of customer service use cases, such as health benefits inquiry, claims inquiry, and finding a doctor. Technology leaders must approach investment in ChatBots as strategic investments and follow the “think strategically and act tactically” mindset. Strategic investment in ChatBots requires an architecture strategy, implementation roadmap, and key considerations around delivery channels, build vs. buy, data ownersion, and data science competency.
4:40 Late Breaking Presentation
5:00 Close of AI World 2018