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Session
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Day 1

  • What are imaging biomarkers and how can they be used for precision oncology? 
  • Challenges and opportunities for using real-world data to develop deep-learning imaging biomarkers 
  • Challenges and best practices for machine learning development at scale for software as a medical device 
  • Examples of Onc.AI imaging biomarkers to predict immunotherapy response and survival in advanced non-small cell lung cancer throughout the treatment journey 


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  • The presentation compares traditional and generative AI and their implications to the pharma industry, with a focus on commercial insights & analytics and the reporting of such insights. To do so, the presentation will have the following sections:
  • The Journey: Different types of AI and pharma examples
  • Technology Stack: Can we re-use previous technological investments in AI?
  • Use Cases: A commercial data integration, insights, and reporting case study with both discriminative and generative AI
  • Risks and challenges: New technology, new biases
  • Incorporating AI in organization: Excitement, fatigue, suspicion shapes the new AI landscape in pharma

Kaiwen Zhong - Associate Director, Strategic Data Products - Novartis

 


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Augmenting Mental Wellness: The Promise and Potential of AI in Neuropsychiatric Commercialization

  • Discover how AI is transforming diagnostics and treatments in neuropsychiatry.
  • Learn about the potential of commercial AI solutions in personalized mental health care.
  • Explore how AI integration can revolutionize scalable and effective mental health interventions. 

 


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  • What are the main challenges faced when implementing large language models (LLMs) in healthcare?
  • How do the limitations of LLMs affect their reliability and accuracy in medical applications?
  • What strategies can mitigate the risks and pitfalls associated with LLMs in healthcare settings? 

 


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  • How can AI drive further value from medical images beyond their initial indication?
  • How can patients benefit from using AI to extract biomarkers from images?
  • What are the barriers to implementing opportunistic screening in medical imaging in clinical practice?  

 


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  • What are the unique security challenges posed by generative AI in the healthcare sector?
  • How can we ensure compliance with healthcare regulations like HIPAA while leveraging generative AI technologies?
  • What advanced techniques can be implemented to protect AI systems against cyber threats?
  • Case studies demonstrating the successful implementation of secure generative AI applications, enhancing trust and reliability in AI-driven healthcare innovations. 

 


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  • Uncover how AI helps forecast comorbidity risks in individuals with Type 2 diabetes.
  • Gain insights into advanced methods for evaluating the likelihood of complications in diabetes care.
  • Examine how AI-driven approaches enhance the early identification and prevention of comorbidities in Type 2 diabetes.  

 


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Day 2

  1. Development of a deep neural network-based model for clinical management of patients with adnexal mass.
  2. Clinical Utility of the diagnostic test: Aid the physician in surgical consideration decision for adnexal mass risk.
  3. Understanding the reliability and accuracy of AI enabled diagnostic.

Speaker: 

Manjusha Roy Choudhury - Data Scientist - Aspira Women's Health

 

 


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Presentation: AI and IoT for Remote Patient Care: Unlocking New Opportunities

  • How are AI-powered wearables enhancing continuous health monitoring for patients?
  • What are the benefits of integrating IoT and AI in remote patient care systems?
  • What challenges and opportunities exist for using AI in home care and rural healthcare settings? 

 


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  • What does clinically responsible AI integration look like in practice?
  • What are the key principles that transform AI from a potential risk to a reliable healthcare tool?
  • How can we build AI platforms that stick? Three pillars of sustainable clinical value

 


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  • Discover how AI impacts patient awareness and engagement in healthcare.
  • Learn how AI influences patient understanding of medical information and decisions.
  • Explore the role of AI in building or eroding trust between patients and healthcare providers.
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