Cambridge Healthtech Institute’s 3rd Annual

Artificial Intelligence in Clinical Research
( 臨床研究中的人工智慧 )

Machine Learning and AI to Advance Clinical Operations and Data Management


Artificial intelligence (AI) and machine learning (ML) have propelled many industries toward a new, highly functional and powerful state. Now they are starting to make their way into the clinical research realm advancing clinical operations, as well as data management. Many pharmaceutical companies and larger CROs are starting projects involving some elements of AI, ML, and robotic process automation in clinical trials. CHI’s 3rd Annual Artificial Intelligence in Clinical Research conference is designed to facilitate the discussion and to accelerate the adoption of these approaches in clinical trials.

Arrive early and attend Part 1 (Wed - Thurs): Clinical Data Strategy and Analytics

Final Agenda


11:30 am Registration Open

12:30 pm BRIDGING LUNCHEON PRESENTATION: Practical Applications of AI in Patient Data Analytics

Srinivasan Anandakumar, Senior Director, Clinical Analytics, Saama Technologies

1:00 Coffee and Dessert Break in the Exhibit HallBioTelResearch


2:00 Afternoon Plenary Keynotes

3:10 Booth Crawl & Refreshment Break in the Exhibit Hall. Last Chance for Exhibit Viewing


4:10 Chairperson’s Remarks

Balazs Flink, PhD, Head, Clinical Trial Analytics, R&D Business Insights, Bristol-Myers Squibb Co.

4:15 AI Applications in Trial Planning and Execution

Flink_BalazsBalazs Flink, PhD, Head, Clinical Trial Analytics, R&D Business Insights, Bristol-Myers Squibb

BMS have started a journey on Digital Health and Innovation a few years ago. The company has tested a lot of emerging technologies and ideas, especially to advance clinical trial design, execution and market access. This presentation will concentrate on a few use cases in these areas sharing experiences with AI and highlighting opportunities for future evolution.

4:50 AI in Drug Development and Clinical Trials

Khan_FaisalFaisal Khan, PhD, Executive Director, Advanced Analytics and Artificial Intelligence, AstraZeneca

This talk will focus on the myriad of AI applications in the drug development process, with a focus on designing and executing clinical trials. The discussion will be motivated by examples and will cover issues, such as data processing and preparation, design of robust AI solutions, challenges in scaling and productionalizing pilot prototypes, and much more.

5:05 Presentation to be AnnouncedIQVIA_NoTagline_NEW

5:35 Quantifying the Value of Enrollment Acceleration Strategies

Swank_DavidDavid Swank, Technical Director, Strategic Options and Assessments, R&D, Bristol-Myers Squibb Co.

How do you deploy your limited resources (head count and cash) to maximize the value of your portfolio? This presentation will discuss how to quantify the overall time to launch for event driven studies, and how potential acceleration efforts can be valued. The key drivers of study value will be discussed along with helpful rules of thumbs and questions you should be asking so your organization make informed resourcing decisions.

5:50 Clinical Pitfalls in Using AI for Decision Support

Kakarmath_SujaySujay Kakarmath, MD, MS, Lead Scientist, Data Science and Artificial Intelligence, Partners Healthcare Pivot Labs

Traditional metrics used to evaluate the performance of AI solutions suffice to establish a proof-of-concept. For real-world applications, however, these metrics are far from sufficient in establishing clinical utility. The Data Science and AI team at Partners Healthcare Pivot Labs invests a great deal of time thinking about the right questions, working out potential pitfalls and developing best practices in evaluating AI solutions for healthcare. This presentation will share insights obtained from real projects.

6:15 Networking Reception

7:15 Close of Day


7:15 am Registration Open

7:45 Breakfast Presentation to be Announced

8:15 Session Break


8:20 Chairperson’s Remarks

Chair to be Announced, Saama Technologies

8:25 CO-PRESENTATION: Intelligent Machines Take on Clinical Data Management

Rao_PrasannaPrasanna Rao, Head, AI & Data Science, Data Monitoring and Management, Clinical Sciences and Operations, Global Product Development, Pfizer

Howard_AshleyAshley Howard, Associate Director, Asset Lead – Oncology, Pfizer

In this session, we will discuss how Artificial Intelligence will transform Clinical Data Management. The human versus machine battle has already started in other industries. How can we leverage machines to not just perform repetitive data cleaning tasks, but take on higher complexity tasks in tandem with humans?

8:55 Accelerating Clinical Database Set-Up Using Machine Learning & AI

Patnaik_ArunArun Patnaik, Director, Clinical Data Management, Data Operations, Novartis

Have you ever wondered how to get the best experience for end users while defining data collection and reporting requirements during the study start-up phase? Does this process take too long in your company? How can technology be used to drive down timelines, improve quality, increase standardization and downstream impact? This presentation will give insights into the work being done at Novartis to achieve all these using historical data.

9:15 AI/ML – Will it Revolutionize Clinical Data Management?

Kendall_FrancisFrancis Kendall, Senior Director, Biostatistics & Programming, Cytel

This talk will look at AI/ML within the context of how it could revolutionize Clinical Data Management. It will outline the assumptions of how the scope of Clinical Data Management will widen and what factors will need to be in place to allow for the effective application of AI/ML. Finally, it will provide a few case studies on how AI/ML is being applied in Clinical Data Management.

9:35 The Application of Intelligent Automation Technologies in Pharmacovigilance

Taylor_RobertRobert Taylor, Director, Safety Management, Global Regulatory Affairs and Clinical Safety, Merck

The application of intelligent automation technologies to PV processes has the potential to improve quality, consistency, and efficiency with the ultimate goal of improving patient safety. Automating areas where available data continues to increase can facilitate that goal by optimizing human resources within the areas of higher value to patient safety. To accomplish this, PV organizations must continually oversee the applicability, design, deployment, performance, validation, and updating of these technologies. Our proposed validation framework seeks to build on early seminal works while incorporating best practices from other industries and TransCelerate member companies.

9:55 Accessing Meaningful Subject Data and Clinical Insights Throughout the Clinical Trial ProcessAiCure

Michelle Marlborough, Chief Product Officer, Product Management, AiCure

Data are generated at nearly every stage of the clinical trial life cycle, from collection of baseline subject data at enrollment to the analysis of the data set. Accessing meaningful subject data and obtaining insights into real-time engagement make a major difference between probability of staying in treatment and discontinuation. 

10:25 Networking Coffee Break


10:55 Chairperson’s Remarks

Francis Kendall, Senior Director, Biostatistics & Programming, Cytel

11:00 Can Artificial Intelligence Identify Recurring Quality Issues? – A Case Study

O'Brien_FayeFaye O’Brien, Director, Performance & Metrics, AstraZeneca

Joann Frazier, Lead Analyst, Operations Excellence, AstraZeneca

Mining, Categorizing and Analyzing quality data through machine learning has the potential to improve clinical trial delivery processes.

11:30 In-Silico Patients

Wael Salloum, PhD, CSO & Co-Founder,

AI technologies can generate digital patients as a substitute for human subjects. Although this may sound like science fiction today, it definitely won’t in a few years. We have already achieved the first few milestones: synthesizing a digital copy of a patient journey from EHR records and building technologies to interrogate these digital replicas to generate clinical evidence. The future is patientless.

12:00 pm Transition to Shared Sessions


12:00 Chairperson’s Remarks

Prasanna Rao, Head, AI & Data Science, Data Monitoring and Management, Clinical Sciences and Operations, Global Product Development, Pfizer Inc.

12:05 Real World Evidence Generation for Clinical Decision Making: Can AI Solve the Challenges?

Bartels_DorotheeProfessor Dr. Dorothee Bartels, Clinical and Real World Data Strategist, X The Moonshot Factory

Real World Evidence Generation (RWE) is continuously gaining importance, but trust in results, transparency and reproducibility are challenging. The same is true for prediction models based on learning algorithms. Questions are arising: who may be the best expert in big data analysis; who may have the appropriate skill sets; and can clinical decisions be based on real world data analysed by either traditional or artificial intelligence (AI) methods? Complementary approaches using synergies from the epidemiological and AI field may boost the field of RWE.

12:25 Re-Skilling for AI/ML: Leveraging Your SMEs

Katan_NechamaNechama Katan, Associate Director, Data Monitoring and Management, Clinical Sciences and Operations, Global Product Development, Pfizer

AI/ML are very powerful tools for clinical trials. However, there is a gap between those that understand what AI/ML can do for the business and the business SME (subject matter experts) who really understand the business problems. Without strong SME engagement in solutions, technical solutions are often at risk. This talk will review successful case studies for developing “lego” employees/teams who help bridge the gaps between AI/ML technologist and the SMEs. We will discuss both the how and what that makes an AI/ML project successful in clinical trials.

12:45 PANEL DISCUSSION: AI Implementation: Technology, Data, People

Rao_PrasannaModerator: Prasanna Rao, Head, AI & Data Science, Data Monitoring and Management, Clinical Sciences and Operations, Global Product Development, Pfizer

Panelists: Balazs Flink, PhD, Head, Clinical Trial Analytics, R&D Business Insights, Bristol-Myers Squibb

Faisal Khan, PhD, Executive Director, Advanced Analytics and Artificial Intelligence, AstraZeneca

Arun Patnaik, Director, Clinical Data Management, Data Operations, Novartis

Malaikannan Sankarasubbu, Vice President, AI Research, Saama Technologies

It was proven that machine learning and AI can aid clinical development in various aspects. With evolving AI technology implementation challenges become more and more noticeable. This panel discussion will brainstorm the key pain points of AI implementation:

  • What is the best technology and how to work with technology providers?
  • How to make all data machine learnable and available for AI applications
  • How to solve “the people puzzle”

1:05 Transition to Lunch

WIRB_CopernicusGroup 1:10 SCOPE Send Off Luncheon Presentation to be Announced

1:40 Closing Remarks

1:45 SCOPE Summit 2020 Adjourns


Tuesday Evening, Wednesday Morning, Wednesday Afternoon Plenary Keynotes Featuring:

SCOPE’s 2020 Participant Engagement Award, in Memory of Jerry Matczak
Patient Perspectives as an Input to Feasibility and Clinical Trial Design
Digital Trends that Are Changing Clinical Research
Health Literacy Throughout Drug Development – Why It Matters to Pharma and to Patients
Implementing an Innovation Methodology to Accelerate Clinical Trial Innovation within Your Organization

For more details on the Plenary Keynotes
For more details on the Participant Engagement Award


Concurrent breakout discussion groups are interactive, guided discussions hosted by a facilitator or set of co-facilitators to discuss some of the key issues presented earlier in the day’s sessions. Delegates will join a table of interest and become an active part of the discussion at hand. Bring your pharma, biotech, CRO, site, hospital or patient perspective to each of the discussions below. To get the most out of this interactive session and format please come prepared to share examples from your work, vet some ideas with your peers, be a part of group interrogation and problem solving, and, most importantly, participate in active idea sharing: breakouts


Arrive early and attend Part 1 (Wed - Thurs): Clinical Data Strategy and Analytics

* 活動內容有可能不事先告知作更動及調整。

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