AI in Healthcare & Pharma Summit
2023 Agenda
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08:00
REGISTRATION & LIGHT BREAKFAST
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09:00
Morning Sessions Begin
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09:00
Opening Remarks and Welcome
Naz Makkinejad, PhD - ML Solutions Engineer - Snorkel AI
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09:15
Productionizing GenAI for Healthcare with AI Data Development
Naz Makkinejad, PhD - ML Solutions Engineer - Snorkel AI
We’ve all heard some form of ‘your data is your differentiator’ but in the world of off-the-shelf Generative AI models, where does your data drive the most value? All of us who have worked with AI for sometime are used to the mindset that data is most important in building a model. Now you can just grab a model pre-trained by OpenAI, Google, Hugging Face etc and start generating predictions. And these predictions can be large chunks of generated content! Where does my data actually add value in this new world?With Generative AI your unique data is just as important (if not more) than traditional AI but in different ways for healthcare. Join me to learn where your data can be used and how it should be prepared, managed, and applied. -
10:00
PANEL: Investing in the Future: Strategies for Funding and Scaling AI Projects in Healthcare
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Panelist
Kausheek Nandy - Head of IT - Boehringer Ingelheim Canada
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Panelist
Sooah Cho - Partner, Health & Life Sciences Tech Partner - SignalFire
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Panelist
Sofia Guerra - Vice President - Bessemer Venture Partners
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Moderator
Piyush Puri - Principal ML Success Manager - Snorkel AI
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10:45 Networking Break in the Exhibit Hall
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11:15
AI in Ultrasound: The Value in Imaging Centers, Home Care and Rural Settings
Arinc Ozturk - Faculty & Instructor - Harvard Medical School and Massachusetts General Hospital
- AI in ultrasound practice and approved technologies will be covered in the first segment of this talk.
- Current challenges in ultrasound practice and the role of AI enhanced ultrasound in addressing these challenges will be covered on the second segment.
- The future role of AI ultrasound in home care and rural settings will be covered on the third segment of this talk.
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11:45
Is Synthetic Data the Key to Better Enterprise ML and Faster Innovation in Healthcare?
Hamish Teagle, PhD - Machine Learning Engineer - Synthesized
Most enterprises benefit from economies of scale as they grow. However, with size comes more operational bottlenecks in the handling of data – for reasons relating to security, regulatory compliance, and corporate structure. Many enterprises sacrifice data agility for lower risk, which hinders innovation and strategic goal setting.
In this talk, we will explore several real examples of how synthetic data can be used in Healthcare and Pharma to address issues of data quality and access without sacrificing data agility.
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12:15
Effective Deployment of Computer-Aided Diagnosis Systems for Medical Imaging
Harpreet Grewal - Attending Radiologist, Radiology Associates of Florida and Assistant Professor of Clinical Radiology - Florida State University College of Medicine
- How can semi-supervised learning techniques be utilized for image classification in radiology?
- What are the advantages and limitations of semi-supervised learning compared to traditional supervised learning in radiology image classification?
- How can unlabeled data be effectively leveraged in combination with limited labeled data to improve the performance of image classification models?
- What are the common semi-supervised learning algorithms and methodologies employed in radiology image classification?
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12:40
Lunch & Networking in the Exhibition Area
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Afternoon Sessions
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2:00
Towards Automated Clinical Trial Design and Protocol Optimization with Machine Learning
Dr. Rahul Kashyap, MD, MBA - Medical Director Research - Wellspan Health
- How can high-throughput screening (HTS) techniques be accelerated and optimized using AI in the context of drug discovery?
- What are the challenges and limitations of traditional HTS approaches, and how can AI help overcome them?
- How can AI algorithms and machine learning models be applied to analyze and interpret large-scale HTS data for identifying potential drug candidates?
- What are the different strategies and methodologies for integrating AI with HTS workflows to improve the efficiency and effectiveness of drug discovery?
- How can AI-driven virtual screening techniques be utilized to prioritize and identify promising compounds for further experimental validation?
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2:30
Bring Humanity to AI in Healthcare
Bingcan Chen - Executive Director, Growth Data & Analytics - CVS Health
Understand what challenges we are facing today in Healthcare and what role AI should play to transform Healthcare. -
3:00
Understanding AI Bias in Healthcare
Ankit Virmani - Senior Cloud Data Architect - Google
- How does bias in AI affect healthcare decision making?
- How can we measure bias?
- How can we think about mitigating it?
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3:30 Afternoon Break in the Exhibit Hall
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4:00
Using AI to Identify Root Causes of Health Inequity Among Patient Groups
Anemone Kasabeh - Lead Data Scientist - UHS
Marginalized patient populations continue to experience healthcare disparities based on racial and cultural identities, and socioeconomic segregations. There is a pressing need to dismantle the healthcare privileges of those different patient communities within the same geographic population. The goal of this research is to bridge the gap between healthcare access and different marginalized patient groups.
The proposed research will use information gathered to identify the root causes of health inequities among different patient groups using modern artificial intelligence (AI) methodologies. Then recommend sustainable and practical interventions based on the AI driven integrated frameworks. These interventions and solutions are intended to create more equitable access to medical care, and improve healthcare outcomes in our local marginalized populations. -
4:30
Panel: Navigating the Intersection of Data Bias, Health Inequities, and AI
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Panelist
Daniel Chertok - Data Scientist - Northshore/Edward-Elmhurst Health
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Panelist
Amy Boothe - Director of Analytics - United Health Services
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Panelist
Anemone Kasasbeh - Lead Data Scientist - United Health Services
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Moderator
Piyush Puri - Principal ML Success Manager - Snorkel AI
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5:15
Networking Reception
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08:30
REGISTRATION & LIGHT BREAKFAST
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Morning Sessions
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09:00
Opening Remarks and Welcome
Samir Aljabar - GTM Leader - Snorkel AI
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09:15
Reducing Nurse Burnout in the Era of AI: Opportunities and Challenges
Amir Tahmasebi - Director of Data Science, Digital Products - Becton Dickinson
- Unveiling AI’s transformative potential in healthcare: Tailored strategies to alleviate nursing burnout.
- Real-life success stories: How AI adoption can lead to a more manageable workload and improved nurse well-being.
- Building the future of nursing: Leveraging AI to foster a resilient, supportive, and efficient nursing work environment.
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09:45
Adjudication of Adverse Events Using AI
Saman Parvaneh, PhD - Director, Data Science & AI, Global Biometrics - Edwards Lifesciences
What are the challenges of traditional manual approaches for adjudication of adverse events?
How can AI support the adjudication of adverse events?
What are the potential applications of natural language processing (NLP) and machine learning in automating adverse event adjudication?
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10:15
The Convergence: AI-Powered Wearables Bridging Remote Patient Monitoring and Consumer Health
Florentina Precup - Advisor & Investor - digital health ventures
What are the benefits of using AI and IoT in remote patient monitoring compared to traditional methods?
How can wearable devices equipped with sensors and AI algorithms collect and analyze real-time patient data, such as vital signs, activity levels, sleep patterns, or medication adherence?
What are the challenges and considerations in implementing AI-powered remote patient monitoring, including data security, data integration, and user acceptance?
How can AI algorithms detect and alert healthcare providers to anomalies or critical events in patient data, enabling timely interventions and proactive care?
How can remote patient monitoring using AI and IoT improve patient outcomes by enabling early detection of deteriorating health conditions and reducing hospital readmissions? -
10:45 Networking Break in the Exhibit Hall
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11:15
Predicting Patient Outcomes with Machine Learning-Based CDSS
Anemone Kasasbeh - Lead Data Scientist - UHS
- How can machine learning-based Clinical Decision Support Systems (CDSS) be utilized to predict patient outcomes?
- What are the advantages of using machine learning in predicting patient outcomes compared to traditional methods?
- What types of data sources, such as electronic health records, imaging data, or genomic data, can be integrated into machine learning models for outcome prediction?
- How can machine learning algorithms be trained to analyze and interpret complex patient data to predict clinical outcomes, such as disease progression, treatment response, or adverse events?
- What challenges and considerations are in developing machine learning models for predicting patient outcomes, including data quality, feature selection, and model explainability?
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11:45
Navigating the Future: A Blueprint for Ethical and Responsible AI Governance
Besa Bauta - Chief Information Officer - The Jewish Board
In an era defined by rapid technological advancements, "Navigating the Future: A Blueprint for Ethical and Responsible AI Governance" delves into the pivotal intersection of artificial intelligence and governance.
This abstract invites participants to explore strategies and frameworks aimed at steering AI development towards ethical horizons. Addressing the challenges of bias, transparency, and accountability, the session seeks to cultivate a nuanced understanding of responsible AI governance. Attendees will embark on a journey to construct a robust blueprint for their organizations, that not only guides the ethical deployment of AI but also fosters inclusivity and social responsibility in the ever-evolving landscape of technological innovation. Furthermore, the session will explore principles of fairness, transparency, and ethical stewardship and current progress in making AI explainable.
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12:15
Bridging the Chasm Between the Potential and Reality of AI and Machine Learning for Health Systems
Kapila Monga - Head of Data Science - Bon Secours Mercy Health
Artificial Intelligence and Machine learning are no more just boardroom buzzwords but have become ubiquitous across organizations. We now do have active use-cases across the Healthcare industry where machine learning is being applied in all its glory. However, there still is a long road ahead for these use-cases to show tangible and ongoing return on investment.
Having said that, this elusive and moving target seems to show itself on occasions and presents us with a path to emulate after. In this session we will take a healthcare case study, and talk about the journey through machine learning value realization, how it can be repeated and how healthcare systems can benefit at scale, through the right use of Machine Learning and AI.
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12:45 Lunch in the Exhibit Hall
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2:15
ML Pipeline for Identification of Prognostic and Predictive Factors in Cancer Trial Data
Juliane Mantz - Associate Director, Biostatistics Oncology - EMD Serono, Inc
- How can machine learning be used to predict drug response and optimize personalized treatment plans?
- What are the advantages of using machine learning in predicting drug response compared to traditional approaches?
- What types of data sources, such as genomic data, clinical data, or biomarker data, are utilized in machine learning models for predicting drug response?
- How can machine learning algorithms be trained to analyze and interpret complex data to predict individual patient responses to specific drugs or treatments?
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2:45
end of the summit
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AI in Healthcare & Pharma Summit 2024
AI in Healthcare & Pharma Summit 2024
October 15-16, 2024
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