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Democratizing Access to AI in Healthcare and Life Sciences
Challenges in the Current Landscape
- Researchers often struggle to find and access the data needed to train models or make use of existing models
- Generating new data required for research is often a manual, time-consuming process
- AI is still too technical for most people, requiring expertise in areas like model training, software pipelines, and infrastructure scaling
AWS Initiatives to Address These Challenges
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General-Purpose AI and Generative AI Services:
- Amazon Bedrock, Amazon SageMaker, and other less well-known but important services
- Enabling customers to tackle data access, data generation, and AI democratization challenges
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Industry-Specific, Fit-for-Purpose Services:
- Developed over the past 5 years to address the unique needs of healthcare and life sciences customers
- Helping customers move faster in adopting AI and democratizing access to AI
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Getting Data to the Cloud:
- The first step for customers to take advantage of AWS AI and generative AI services
- Providing the elasticity, security, and availability needed for data science and AI at scale
- Example: Johnson & Johnson's cloud-first strategy and 5x increase in cloud adoption since 2019
Accelerating Innovation in R&D, Diagnosis, and Treatment
Target Identification
- Requires accessing and harmonizing data from multiple sources, a complex and time-consuming process
- Amazon Data Zone and Bedrock Agents help automate and streamline this process
- Example: Genentech's implementation of a research agent on AWS Bedrock
Lead Identification
- Generative AI and predictive models have significantly sped up key steps in the drug design process
- New models like ESM3 and Amplify offer unprecedented capabilities
- AWS HealthOmics helps orchestrate and automate AI biology workflows
Clinical Trials
- Modernizing data infrastructure and clinical datasets to leverage analytics and AI
- Merck's "Zero Gravity" program to simplify their current architecture and increase agility
Real-World Data and Patient Impact
- Challenges in identifying, licensing, and using real-world data as real-world evidence
- AWS and partner DataVan working to automate metadata cleanup and enable better access to real-world data
Advancements in the Clinical Environment
Electronic Medical Records (EMRs) and Medical Imaging
- Importance of moving EMRs and medical imaging data to the cloud
- AWS services like Health Lake and Health Imaging enabling advanced analytics and AI
Clinical Documentation
- Generative AI and LLMs helping to reduce the clinical documentation burden and improve clinical communication
- Examples of Pieces Technology's work with the Cleveland Clinic
Transforming Patient Access to Therapies
- Barriers to patient access: finding a doctor, affording treatment, and physically obtaining medication
- Lilly Direct: Lilly's initiative to address these barriers and provide a streamlined, accessible experience
Conclusion
- The impact of AI and cloud technologies in healthcare and life sciences is truly realized when it benefits patients
- Continuous progress in democratizing access to these transformative technologies is key to unlocking their full potential