?? Weekly Dose of GenAI #14 ??

Welcome to the 14th edition of Weekly Dose of GenAI Adoption newsletter! This newsletter delves into the rapid integration of generative AI within the enterprise. Discover real-world insights that will empower you and your team to accelerate adoption of these transformative technologies in your organization. The newsletter is a cumulation of daily posts from this week, packaged for easy weekend read.

This week, we delved into the critical aspects of developing an AI-ready data infrastructure in the healthcare and life sciences industry. We explored the fundamental importance of a robust data foundation and emphasized the necessity of enhancing data pipelines to accommodate diverse, multi-modal data sources. Our discussions highlighted the significance of addressing regulatory, governance, and compliance requirements at the point of data ingestion. Furthermore, we advocated for the implementation of specialized storage solutions, which can significantly boost data processing speeds and improve model inference accuracy. These advancements are crucial for creating more efficient and effective generative AI applications in the healthcare sector.

?? Building an AI-Ready Data Infrastructure: Importance of a strong data foundation for AI initiatives ??

Scenario: The POC you tested was successful, but the generative AI (GenAI) application you are planning to launch is not delivering expected results or taking long to find the expected data!

Does this sound familiar? If so, you may be dealing with ‘data spaghetti syndrome’, ‘AI Quicksand’, ‘Digital Landfill’, and ‘AI Mirage’.

Put in a polite and professional way – your organization lacks a strong data foundation to make the most of AI!

And before you feel too bad, let me tell you ‘you have company’ – most enterprises have this problem! When enterprises start with a POC, they take the time to curate the right data, from the right source, for the right use case and help prove the technology works. However, once they start to scale for production they simply lift data from their 'digital landfill' to the POC solution, expecting the technology to somehow work...

Yes, yes, yes - I know you have heard this before and yet were able to survive the last 2 decades of digital transformation and still standing tall and growing the business! You know why it worked well? Because the barriers to entry for anything AI/ML were high over the last 2 decades. With GenAI, the barriers to entry for anything AI/ML are now removed and almost all firms are now a AI/ML firm... As such, firms without 'data spaghetti syndrome' will give you stiff competition. Why? Because they will have better operational efficiency than you, their cost of doing business will be lower than you, so they will be able to offer lower price, higher performance, and faster time to market with services and solutions to 'your' customers.

Remember: AI is only as good as the data it's built on. Investing in your data infrastructure today will set the stage for AI success tomorrow.

A solid data infrastructure isn't just nice-to-have for AI, ML, and GenAI – it's a game-changer! It will supercharge your model performance, giving you results you can actually trust. Your cross-functional teams will be able to collaborate and innovate faster, and you'll see faster value realization.

Over the next few weeks, we will discuss the importance of strong data foundation for AI/ML/GenAI initiatives and dive deeper into every aspect of data.?? Share your thoughts on GenAI Adoption below!

?? ?? Multi-Modal Data Ingestion in Healthcare and Life Sciences

Did you know that the healthcare and life sciences industry is responsible for producing approximately 30% of the world's digital data volume, and it's growing faster than the media & entertainment industry?

This data is multi-modal in nature, encompassing various types of information from clinical trials to genomics and digital pathology. By strategically ingesting and integrating this wealth of structured, unstructured, and specialized data, organizations can empower transformative technologies like generative AI (GenAI) to drive groundbreaking breakthroughs across drug discovery, personalized medicine, and patient care optimization.

This innovation hinges on the availability of high-quality, multimodal data. However, the reality is that many organizations in these sectors are still grappling with the fundamental challenges of data collection and ingestion. Legacy systems, siloed data, and inconsistent data management practices can hinder the path towards a truly integrated, multimodal data ecosystem – a necessary foundation for powering GenAI applications.

Overcoming data heterogeneity, quality issues, and regulatory compliance - essential steps for effective data ingestion in healthcare and life sciences. Normalization, standardization using formats like FHIR, HL7, and DICOM, validation, and adherence to regulations like HIPAA, GDPR are crucial to building a robust, compliant data foundation. Mastering these data management fundamentals, grounded in industry standards, is key to unlocking the transformative potential of healthcare analytics and GenAI innovation.

The complexity of healthcare, rapid technological advancements, and new opportunities like GenAI necessitate regular reviews of data strategies. Here's a pragmatic approach to evolve your data collection and ingestion: 1/ Start with a focused GenAI MVP (minimum viable product). Design a tailored ingestion strategy to demonstrate value and build momentum for broader initiatives. 2/ Working backwards from the MVP, audit existing data sources and formats, including clinical trials, genomics, and real-world evidence. Assess data origins, quality, and accessibility. 3/ Identify strategically important data types for GenAI MVP. Prioritize these for initial ingestion review/evolution. 4/ Review data governance policies and appoint stewards to ensure quality, security, and compliance to align with expected standards. 5/ If required, explore modern integration platforms for agile data ingestion, bridging business needs and IT capabilities.

Inspecting the complete data pipeline, working backwards from a single use case, will help provide insights into your organization’s readiness and guide your budgetary decisions as you soon start to prioritize your initiatives for next fiscal year.

Optimizing Data Storage for GenAI in Healthcare & Life Sciences ??

Continuing our exploration and deep dive into building the right data infrastructure for generative AI (GenAI) (part 1 - https://lnkd.in/eJ9nrS4A ; part 2 - https://lnkd.in/eWqhpBSy ), I would like to share a perspective on the importance of specialized data storage to maximize GenAI's potential, specifically for the healthcare and life sciences industry.

But before we get into the weeds, let’s start with the big why i.e. why specialized storage matters and why specifically for GenAI applications – 1/ Healthcare data is multi-modal, including genomics, imaging, EHRs, and clinical notes, each requiring specific handling. 2/ Data scales rapidly per patient in GenAI applications. For clinical or research use, GenAI must respond quickly, explain connections, prompt for insights, and retrieve data efficiently. Inadequate storage may slow model inference. 3/ Healthcare data needs high security and compliance. Specialized storage solutions can meet these requirements while supporting AI capabilities.

Given my proximity to AWS, I'll share our specialized storage solutions for healthcare data: 1/ Genomics: Amazon HealthOmics for genomic data storage and analysis 2/ Medical Imaging: Amazon HealthImaging for DICOM file management 3/ EHR: Amazon HealthLake transforms unstructured EHR data for GenAI use 4/ Graph Databases: Amazon Neptune manages complex metadata relationships 5/ Semantic Search: Amazon OpenSearch and Kendra enable advanced vector search. These solutions optimize data processing and improve GenAI model accuracy in healthcare applications.

These specialized solutions can help healthcare organizations improve data processing speeds and model inference accuracy, leading to more efficient and effective GenAI applications in healthcare.

But why add this complexity to an already complex system? The promise of GenAI is improved patient care, cost savings, and ROI for healthcare, though the jury is still out. Better data management enables innovative GenAI applications like real-time clinical decision support that integrates EHR, imaging, genomics, and research data for context-aware recommendations. Imagine reviewing a patient's symptoms, lab results, radiology images, and genetic sequence, while identifying relevant research and treatment options during consultations.

?? Subscribe to this newsletter on GenAI adoption - Don't miss this essential update on the transformative impact of generative AI in the healthcare and life sciences industry. Each week, we dive into various adoption strategies and use cases, from AI-powered marketing to accelerating drug discovery. Learn about cutting-edge GenAI technology trends, including Amazon Bedrock solutions and novel design patterns. Discover how leading healthcare organizations are harnessing the power of large language models to unlock insights from contract data and enhance customer service.

Indy Sawhney - Follow me on LinkedIn

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