?? Weekly Dose of GenAI #5 ??

Welcome to the fifth edition of Weekly Dose of GenAI Adoption newsletter! This newsletter delves into the rapid integration of generative AI within the healthcare and life sciences sectors. Discover real-world insights that will empower you and your team to accelerate adoption of these transformative technologies in your organization. This week we dove into some interesting topics such as Importance of Responsible AI in Healthcare and Life Sciences, Bridging Data Governance Gaps for Successful GenAI Adoption, and a 3-part series, that covered KPIs for LLM evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output quality.

?????? The Importance of Responsible AI in Healthcare and Life Sciences

Generative AI (GenAI) holds immense potential to improve patient outcomes, streamline processes, and drive innovation in the development of therapies and therapeutics. However, the application of GenAI in these sectors demands a responsible approach to ensure patient safety, data privacy, and ethical use of technology. Here are three key considerations for healthcare and life sciences leaders embracing GenAI:

1. ?? Data Security and Privacy: Healthcare data is sensitive, and ensuring patient privacy is critical. Responsible AI practices must prioritize robust data security measures, anonymization, and compliance with regulations like HIPAA, HITECH, HITRUST, and GDPR. Starting with a GxP eligible/compliant framework for your enterprise GenAI platform would allow for accelerated adoption and alignment with established corporate policies.

2. ?? Explainability and Trust: When leveraging GenAI for diagnostics or treatment (or for that matter any) recommendations, understanding how AI algorithms make decisions is crucial for healthcare professionals to trust the results. Explainable AI (XAI) models can help demystify AI decision-making and enable more informed choices. Validating the AI model's performance on external, real-world datasets (zero-shot test data) is crucial to ensure the model performs well in actual clinical practice, beyond just the training data. Similarly, ensuring the training data or knowledge base (RAG data corpus) used to develop the GenAI application is unbiased and representative is essential to mitigate potential biases in the model's outputs.

3. ?? Ethical Use and Equity: Industry leaders must prioritize the ethical application of GenAI to prevent biases that may negatively impact patient care or exacerbate health disparities. Designing AI systems with fairness, accountability, and transparency will promote equitable healthcare delivery. However, good intentions are not enough! Enterprises will need a GenAI platforms that provide a unified toolset of capabilities that can help govern and audit ethical use across the complete value-chain of such engagements.

?? Bridging Data Governance Gaps for Successful GenAI Adoption

As healthcare and life sciences firms increasingly attempt adopting generative AI (GenAI) technologies to unlock new opportunities, many face a common challenge: the lack of robust data governance and strategy.

?? Why Data Governance Matters for GenAI adoption:

1. ???? Data Quality: GenAI models rely on high-quality data to generate accurate results. Effective data governance ensures that data is clean, consistent, and reliable, optimizing AI performance. We are talking about the data that can be leveraged for LLM fine-tuning, few-shot learning, and even in your RAG (retrieval augmented generation) knowledge base.

2. ?? Privacy and Security: Robust data governance protects sensitive information, ensuring compliance with regulations and mitigating the risks associated with data breaches and unauthorized access. You don't want to leak PII data to your LLM or to your RAG repos!

3. ?? Ethical AI: By establishing clear guidelines and policies around data usage, organizations can foster ethical and responsible AI practices and prevent unintended consequences like bias and discrimination.

Strategies for Improvement:

1. ?? Develop a Comprehensive Data Strategy: Align data management with organizational goals and establish clear policies around data collection, storage, and usage.

2. ?? Invest in Data Quality Initiatives: Implement data cleansing, validation, and standardization processes to ensure high-quality inputs for GenAI models.

3. ?? Strengthen Data Security Measures: Implement access controls, encryption, and other security measures to protect sensitive data and maintain compliance with regulations.

4. ?? Foster a Data-Driven Culture: Encourage collaboration between data management, IT, and business teams to promote data literacy and responsible data practices across the organization.

?? Don't have alignment, budget or time for a comprehensive data strategy? Adopt a phased iterative implementation, focusing on low-cost initiatives like standardizing data formats, leveraging open-source tools, establishing a data governance council, and prioritizing quick wins.

By addressing data governance gaps, organizations can lay a solid foundation for successful GenAI adoption, unlocking new possibilities and driving meaningful business outcomes. As always, I'd encourage you to connect with your technology and GSI partners. Collaborating with them can help you better understand the programs and acceleration mechanisms they offer to support your business goals.

?? ?? KPIs (Key Performance Indicators) for Generative AI Applications in Healthcare and Life Sciences: Large Language Model (LLM) Evaluation

As generative AI (GenAI) applications permeate the healthcare and life sciences industry, it's crucial for senior executives to track performance and drive continuous improvement. A successful GenAI adoption strategy would warrant tracking KPIs for all stages of the application design - LLM Evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output Quality.

In a 3 part series, I will cover KPIs for LLM evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output quality. Here are a few Key Performance Indicators (KPIs) to consider as you venture into LLM (Large Language Model) evaluation:

1. ?? Domain Understanding: Test the base LLM against a domain-specific test dataset to ensure it understands healthcare and life sciences concepts in a zero-shot testing scenario. Why is this important? You want to make sure the base LLM can understand and interpret the user queries to decide the best tools, knowledge base, and responses. Here are the detailed steps you will need to take -

> ?? Start with domain-specific test dataset: Curate a diverse dataset containing relevant healthcare and life sciences examples, terminology, and use cases. Include edge cases and uncommon scenarios to challenge the LLM's understanding.

> ?? Conduct Zero-Shot Testing: Test the base LLM's performance on the curated dataset without any fine-tuning or task-specific training. This evaluation will reveal the LLM's baseline understanding of the domain and identify areas for improvement or specialization.

> ?? Iterative Improvement: Based on the zero-shot testing results, collaborate with domain experts to fine-tune the LLM for better performance. Regularly update and test the model against new examples and emerging trends in healthcare and life sciences.

2. ?? Accuracy: Assess the quality of predictions or outputs using relevant evaluation metrics (e.g. F1 Score, AUC-ROC), ensuring human experts review and validate the results. Work with your data science team to help you with accuracy evaluation.

?? In highly regulated industries, the ability to document, repeat, and govern the use of LLMs is critical to responsible AI deployment. If any LLM makes it to production and influences downstream business decision at your firm, the audit trail of evaluation strategy and process should be documented and be repeatable. As such, consider hardening your LLMOps (LLM Operations). Talk to your technology partner on how they can help automate your LLMOps pipeline.

?? ?? KPIs (Key Performance Indicators) for Generative AI Applications in Healthcare and Life Sciences: Prompt Engineering, and RAG (Retrieval Augmentation Generation) Evaluation

A successful GenAI adoption strategy would warrant tracking KPIs for all stages of the application design - LLM Evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output Quality. In this 3-part series, I will cover KPIs for LLM evaluation (https://shorturl.at/fjGTZ), Prompt Engineering & RAG Design, and GenAI Application Output quality.

RAG evaluation frameworks like RAGAs, ARES, ROUGE, BLEU, MLFlow, DeepEval, and Rageval help validate performance of Prompt and Retrieval Augmented Generation solutions. Each of these evaluation frameworks come with their own set of metrics and capabilities. Here are a few KPIs that these evaluation frameworks validate:

1. ?? Faithfulness: The degree to which AI-generated content aligns with verified facts and data. Measuring for faithfulness ensures reliability, accuracy, and trustworthiness of AI outputs. To test for faithfulness, we compare AI-generated content against trusted sources to assess factual correctness. Use diverse datasets with known facts and complex scenarios.

2. ?? Relevance: How well AI outputs match user queries and maintain coherence in multi-turn conversations. Relevance drives user satisfaction and utility of AI-generated content. To test for relevance, we conduct user studies or use automated metrics like BLEU or ROUGE for output relevance to queries. Assess conversation coherence using human evaluations or dialog-specific metrics.

3. ?? Noise Robustness: The system's ability to handle noisy or ambiguous inputs while maintaining consistent performance. Testing for this ensures real-world resilience and usability in various scenarios. To verify, introduce noisy inputs (e.g., misspellings, unclear queries) during testing to assess consistency in performance.

4. ?? Negative Rejection: The AI's ability to avoid generating harmful or unwanted outputs. Helps promote user safety and trust in AI systems. To validate, develop test cases that could lead to harmful outputs, verifying the AI avoids such behavior.

5. ?? Information Integration: The system's ability to combine data from multiple sources to generate comprehensive and accurate outputs. Information integration enhances utility and depth of AI-generated content. To test, design scenarios that require information synthesis, verifying that outputs accurately integrate the provided data.

6. ?? Counterfactual Robustness: The AI's ability to generate accurate outputs in hypothetical or unconventional scenarios. Helps demonstrate versatility and adaptability, enabling use in a wide range of situations. To test, create hypothetical scenarios to test the AI's ability to produce accurate outputs under various conditions.

?? ?? KPIs (Key Performance Indicators) for Generative AI Application Output & Performance Evaluation in Healthcare and Life Sciences

A successful GenAI adoption strategy would warrant tracking KPIs for all stages of the application design - LLM Evaluation, Prompt Engineering & RAG Architecture, and GenAI Application Output Quality. In this 3-part series, I am covering KPIs for LLM evaluation (https://shorturl.at/fjGTZ), Prompt Engineering & RAG Design (https://shorturl.at/sDIR2), and GenAI Application Output quality.

In the healthcare and life sciences industry, leveraging generative AI (GenAI) applications can significantly enhance patient outcomes and research initiatives. To ensure your GenAI applications deliver value, consider tracking and evaluating some of these Key Performance Indicators (KPIs):

1. ?? User Engagement: Monitor end user interactions with your application, assessing active users, session duration, and retention rates. High engagement signals a valuable, user-friendly application.

2. ? Response Time: Digital transformation and the widespread adoption of social media have raised user expectations from all software applications. Measure the time it takes for your application to respond to user queries, striving for faster response times to improve adoption.

3. ?? Successful Completion Rate: Track the number of queries successfully completed by your application, ensuring your application delivers accurate, reliable information to support healthcare and life sciences professionals in their work. Add thumbs up/down buttons to solicit feedback per inference.

4. ?? Error Rate: Minimize errors to maintain trust and reliability. Regularly log and monitor failed queries to ensure optimal performance.

5. ?? Personalization: Measure the application's ability to adapt its responses to individual users' needs and preferences. Tone and output format style flexibility that is tailored to individual's needs, will lead to better stickiness and adoption.

6. ?? Scalability: Evaluate your application's capacity to handle increasing demand, enabling seamless adoption across the user base. Well architected review is a must!

7. ?? Regulatory Compliance: Ensure your GenAI solution adheres to industry regulations and GxP guidelines. Monitoring compliance KPIs is essential for product safety, efficacy, and overall quality.

8. ?? Human-in-the-Loop Testing: Incorporating human expertise during testing & validation phases will help refine application performance and build confidence for real-world healthcare and research use.

9. ?? Workflow integration: Incorporating the application within existing clinical workflows will encourage purpose-built scope, higher adoption, and opportunities to enhance/evolve the application rapidly.

?? Share your thoughts on GenAI Adoption below!

?? 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