Session 7: Generative AI

Dive into the dynamic world of Generative AI at this insightful session. Our experts will guide you through its present and future, from foundational concepts to cutting-edge applications. Beginning with a comprehensive introduction to Generative AI and its evolving landscape, the session explores the opportunities, costs, and risks for large language models, images and videos.

Concluding with a panel discussion, our speakers will exchange ideas, providing you with a comprehensive understanding of Generative AI. This session promises invaluable knowledge for shaping the future of AI-driven creativity. Join us for this opportunity to stay at the forefront of technological advancement.

Chairs: Joan Buenconsejo, BMS; Pritibha Singh, Novartis

Dr. Joan Buenconsejo Dr. Joan Buenconsejo is the Vice President and head of Cardiovascular and Neuroscience Biostatistics at Bristol-Myers Squibb. In addition to leading a team of talented statisticians who support the development of innovative therapies for patients with CV and neurological diseases, she is also co-leading a BMS-wide workstream that aims to foster trial design innovation and optimization. Dr. Buenconsejo has over 20 years of drug development experience, having worked at the Food and Drug Administration and AstraZeneca before joining BMS. While she calls statistics as her “bread and butter” skillset, she is also passionate about applying novel tools and technology to generate insights, simplify workflows, and reduce patient burden in clinical research. She is also an advocate of using innovative trial designs to accelerate the development of promising new therapies. Dr. Buenconsejo holds a Ph.D. and an MPH in Biostatistics from Yale University.

Dr. Pritibha Singh Pritibha Singh is a global leader with extensive cross-sector experience (Tech space, Pharmaceutical, Biosimilar, Academia, FMCG, and Banking) across diverse geographies (NZ, AU, UK, DE, CH). A blend of expertise in Drug Development (Psychology, Statistics, and Business). Proven track record in driving change, stakeholder collaboration, and managing organizational complexity. Most recently, working on data42, a novel technology platform that brings data from Research, Development and Late-Phase onto one platform to enable advanced Data Science analyses. With 17 years of drug development experience, I am completing a Doctorate at ETH Zurich, specializing in sustainable strategies for decentralized clinical trials.

Exploring the Impact of Generative AI on Statistical Programming in Pharmaceutical Clinical Trial Reporting: A Comprehensive Overview and Analysis

Francis Kendall, Head of Statistical Programming, AstraZeneca

Francis Kendall Francis Kendall is an accomplished senior leader with extensive experience in the field of biometrics and drug development. With an MBA qualification and a people-focused leadership style, he has successfully led teams and implemented strategic change initiatives in multicultural environments. His notable roles include serving as the Global Head of Oncology Statistical Programming at AstraZeneca, where he manages a team of 110 employees and oversees key initiatives such as automation strategies and the design of a new A&R system. Prior to that, he held positions at Cytel, Roche, Pfizer, Novartis, Nycomed, Sandoz UK, and Hoechst UK, where he made significant contributions to statistical programming, data analysis, and strategic planning. Francis holds various qualifications, including being a Chartered Manager, Chartered Statistician, and member of professional organizations such as PHUSE and the Chartered Management Institute. He has also published articles in prestigious journals, demonstrating his expertise in artificial intelligence, machine learning, and clinical development.


The widespread integration of Generative AI technologies across various industries has significantly influenced statistical programming in the realm of Pharmaceutical Development. This presentation provides a comprehensive overview of statistical programming practices within Pharmaceutical Development for clinical trials. It delves into specific instances where Generative AI is and can be employed in these processes, offering insights into its applications. Additionally, the talk sheds light on key concerns and challenges that may hinder the smooth adoption of Generative AI in this domain, providing a nuanced understanding of the promise and pitfalls associated with its implementation.

Utilizing Large Language Models for Clinical Development: Insights on Clinical Text Mining

Kyubum Lee, Principal Data Scienctist, Amgen

Dr. Kyubum Lee Kyubum Lee, PhD, is a biomedical data scientist currently serving as a Principal Data Scientist at Amgen. He earned his bachelor's degree from Korea University in Seoul, South Korea, majoring in both Computer Science and Biology. He went on to receive his master's degree in Bioinformatics and a PhD in Computer Science from the same institution, with a focus on machine learning and text mining.

Dr. Lee completed his postdoctoral fellowship at the National Center for Biotechnology Information (NCBI) at the National Institutes of Health (NIH) and Moffitt Cancer Center. During his postdoctoral training, he led numerous cross-functional projects in collaboration with other biomedical researchers from various institutes, including the CDC, EBI, SIB, and NCI. His projects primarily utilized machine learning to extract valuable information from extensive text datasets, such as over 30 million biomedical articles in PubMed and PMC, as well as biomedical image and genomic data.

Currently, at Amgen, he is part of the Center for Design and Analysis, applying machine learning and artificial intelligence to enhance clinical development and the design of clinical trials.


Large Language Models (LLMs) are revolutionizing the fields of text mining and natural language processing. LLMs can be used to understand and analyze biomedical text from sources such as, PubMed, and proprietary internal documents. LLMs have extensive background knowledge by training on a large volume of biomedical text data. For these reasons, LLMs do not need to be trained on a specific training set, making them more efficient and invaluable in performing biomedical tasks.

In this talk, I will discuss the practical applications of LLMs for text mining within the realm of clinical development, and highlight both their strengths and limitations. Additionally, I will share our experiences and insights we gained from using LLMs to identify biomarkers in clinical trial text data, and discuss their role in advancing clinical development.

Large Language Models and their Applications in Drug Development

Shams Zaman, Senior Director of Data Science & Statistical Methodology, BMS

Shams Zaman Shams Zaman is Senior Director of Data Science & Statistical Methodology. He is leading data driven advance analytics in Cell Therapy and Hematology areas within GBDS supporting various stages of drug development and post approval activities. His team is also developing state-of-the-art clinical natural language processing (NLP) solutions to generate insights and competitive intelligence from clinical documents. Before joining BMS, he was a fellow at FDA and a research scientist in AI lab Philips Healthcare Research. He worked heavily on developing several NLP driven products and clinical decision support (CDS) algorithms from electronic health record (EHR) data in his previous positions.


Recent advancements in generative AI have pushed the boundaries of what is possible in terms of generating realistic and creative content. These advancements have been driven by the development of large-scale language models and improved training techniques. One significant advancement is the development of models with unprecedented size and complexity. Models like OpenAI's GPT-3, with billions of parameters, have demonstrated remarkable language generation capabilities. These models can understand and generate human-like text, making them useful in a wide range of applications, from chatbots to content generation. Another notable advancement is the improvement in fine-tuning techniques. Fine-tuning involves training a pre-trained language model on specific tasks or domains, allowing it to specialize in particular areas. This approach has led to better performance on various natural language processing tasks, such as text classification, sentiment analysis, and question answering. Techniques like prompt engineering and few-shot learning aim to improve the models' ability to generalize and adapt to new tasks with minimal training examples. This has made generative AI models more versatile and applicable to a wider range of real-world scenarios.

Generative AI, particularly in the form of large language models, has found several applications in drug development. These models have the potential to accelerate various stages of the drug discovery and development process. One key application is in analyzing and extracting insights from vast amounts of scientific literature. Generative AI models can process and summarize research papers, clinical trial data, and patents, helping researchers identify potential drug targets, understand disease mechanisms, and explore new therapeutic approaches. By automating the analysis of this information, these models save time and provide valuable insights. Another application is in the design of new molecules. Generative AI models can generate novel chemical structures and predict their properties, aiding in the discovery of potential drug candidates. These models can also suggest modifications to existing molecules to improve their efficacy, safety, or pharmacokinetic properties. By accelerating the molecule design process, generative AI can help researchers identify promising candidates more efficiently. Generative AI can also assist in predicting drug-drug interactions and adverse effects. By analyzing drug databases and medical literature, these models can provide insights into potential risks and help optimize treatment plans. This can enhance patient safety and improve the effectiveness of drug therapies. In summary, generative AI has emerged as a valuable tool in drug development. By assisting in literature analysis, molecule design, prediction of drug interactions, and optimization of drug formulations, these models have the potential to accelerate the discovery and development of new therapeutics, ultimately benefiting patients and improving healthcare outcomes.