Kalilur Rahman

💊 🔬 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. 💬 Share your experiences and thoughts on LLM Evaluation below! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #llmops #aws

20

🚀 📈 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. 📢 Subscribe to my newsletter. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #aws

15

3 Comments

💎 Scaling Generative AI: More Technical Insights from the Field (Part 4) 🚀 In our ongoing series on technical considerations for scaling Generative AI (GenAI) solutions (Part 1 - https://shorturl.at/CHFXb; Part 2 - https://shorturl.at/Lrhh5 & Part 3 - https://shorturl.at/QFSYU), we continue to draw on real-world experiences to highlight additional key factors for success: At the forefront of GenAI transformation is RAG (Retrieval Augmented Generation). RAG frameworks enable the seamless integration of large language models (LLMs) with domain-specific knowledge bases, allowing for accurate and relevant information retrieval and generation. While some RAG implementations cater to simpler tasks such as question-answering or summarization, others are designed to tackle complex, domain-specific challenges that warrant advanced RAG frameworks. To ensure maximum flexibility and prevent redundant efforts, early adopters are starting to realize the benefit of providing a library of RAG implementations for their teams to choose from, based on their specific use case. This approach empowers various lines of business (LoBs) to leverage the power of GenAI without duplicating efforts, fostering collaboration and knowledge sharing across the enterprise. This library of ‘ready to digest’ RAG frameworks allows different teams with unique requirements and use cases to deploy and tailor their RAG setup. For instance, the customer support team at one of my customers needed a RAG framework optimized for quickly retrieving relevant knowledge base articles of approved messaging, and health care provider engagement history to provide accurate and up-to-date responses. In contrast, their marketing team requires a RAG setup that curates the right message in alignment with the regional regulations, language and customer insights to generate compelling content. By having multiple RAG frameworks, the enterprise is ensuring each team has access to the most relevant solution and can fine tune their solution for desired outcome, leading to improved efficiency, accuracy, and overall performance across various business functions. Here are a few RAG frameworks being actively deployed by early adopters - Fusion-in-Decoder (Fusion-RAG), Retrieval-Augmented Language Model Pre-training with Knowledge Graphs (Rank-RAG), Document Reader (Dr-RAG), Memory-Augmented RAG Models. I will dive deeper into the details of these RAG frameworks in a future post. 💬 Do share your GenAI experiences and technical learnings on the path to production in the comments below! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #aws

11

🏥 🔬 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 experiences and insights on tracking performance of GenAI systems in the comments below! 📢 Subscribe to my newsletter for insights on GenAI adoption: https://lnkd.in/g3bdneR7 #genai #ai

14

🎁 Gen AI is transforming the Healthcare industry 🏆“Transforming Patient Care: Generative AI Innovations in Healthcare and Life Sciences (Part 1)” by Dan Sheeran https://lnkd.in/dDxeJhbD ✔Industry Impact: A survey revealed that while 75% of health system executives believe generative AI can reshape the industry, only 6% have an established strategy, hindered by lack of expertise and resource constraints. ✔Generative AI Innovations: Over the past year, major companies like Pfizer and Merck have utilized generative AI to enhance scientific content generation, reduce manufacturing errors, and streamline clinical documentation, resulting in significant cost savings and improved operational efficiency. ✔AI Developments: AWS has made considerable investments in generative AI, expanding GPU offerings, launching custom AI chips, and introducing platforms like Amazon Bedrock for model customization, as well as specific applications like Amazon Q and AWS HealthScribe. ✔Collaborations and Partnerships: AWS has deepened collaborations with partners like NVIDIA and Anthropic to enhance drug discovery and offer advanced language models on their platforms. ✔Future Trends: The author anticipates further integration of generative AI into healthcare workflows, emphasizing the importance of a robust data strategy to enhance the effectiveness and adaptability of AI technologies. ❤Take a look at the article "Smart Hospital: The AI Wave of Digital Transformation" https://lnkd.in/dh-mB2D3 Amazon Web Services (AWS) #DigitalTransformation, #SmartHospital, #SmartClinic, #PatientCentered, #ValueBased, #DigitalFirst, #AIEnabled, #TechnologyAdvanced, #HyperConnected, #AI

15

👉🏼 The Digital Technology in Clinical Medicine: From Calculators to ChatGPT 🤓 Somarajan Anandan 👇🏻 https://lnkd.in/gtRmmq8z 🔍 Focus on data insights: - Utilization of digital technology in clinical medicine - Impact of digital information technology on healthcare delivery 💡 Main outcomes and implications: - Advancements in clinical medicine due to digital technology - Need for balance between technology, clinical expertise, and humanistic values 📚 Field significance: - Regulatory push for improvements through clinical trials - Projects like "World Health Organizations Guidelines for Digital Interventions" and "Food and Drug Administration's Digital Health Center of Excellence" highlighting efforts in digital health services 🗄️: [#digitaltechnology #clinicalmedicine #healthcaredelivery]

1

✍ Crafting an Effective AI Council Charter in Healthcare & Life Sciences Continuing our ongoing discussions on AI Council for GenAI acceleration (post 1 - https://lnkd.in/eW2QhTKR; post 2 - https://lnkd.in/egbJmTps), let’s dive deeper into what might be literally the first task for AI Council i.e. establishing and maintaining a well-defined AI Council Charter. A well-crafted charter not only mitigates risks but also drives responsible AI innovation. The healthcare and life sciences sector faces unique challenges, including bias in AI algorithms, data privacy concerns, and the complexities of regulatory compliance. Addressing these challenges, as part of their charter, is critical for the success of the AI Council. Drafting a charter as the AI Council's first deliverable sets the tone for expectations and its effectiveness in leading GenAI adoption. This process may benefit from engaging a partner or consultant for a smooth experience. Below, I am sharing my mental model on charter development, giving insight into what it takes to deliver. As a new AI Council of diverse internal leaders, expect to go through norming, storming, and forming stages while drafting a charter. For a common understanding, I recommend members review key documents from government and NGO entities, such as FDA's AI & Medical Products white paper, White House's AI Bill of Rights, WHO's AI health regulations, and EU's AI Act. Give members ample time to familiarize themselves with these materials. Next, invite them for collaborative charter-defining workshops, ideally offsite to avoid distractions. During this 2-4 day event, use design thinking to gather inputs on purpose, scope, goals, challenges, roles, responsibilities, deliverables, KPIs, structure, and alignment with medical/scientific rigor. This iterative process should allow time for idea generation, sharing, grouping, and prioritizing. Using prioritized ideas, facilitate a session for participants to collaboratively draft the charter, combining key elements identified. Define the team's Purpose, Goals, Roles, Responsibilities, KPIs, Deliverables, and Structure. Allow ample time to ensure alignment and commitment from your cross-functional AI Council. Next, seek council feedback on clear, measurable KPIs (e.g., AI implementations, compliance rates) to gauge effectiveness. Align on audit cadence and PMO support. Consider additional measures: communication strategy, surveys, ethical incident analysis, impact assessments, and benchmark comparisons. These ensure the charter evolves with the AI landscape, remaining a living document. 💬 Have you defined your AI Council's charter? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #AIinHealthcare #ResponsibleAI #LifeSciences

15

🔍⚖️ Practitioners Guide To Responsible AI - Part 4 Continuing our exploration of the Responsible AI framework from last week (https://lnkd.in/eQSphAeF), today we will focus on the next layer of abstraction i.e. Model Usage, Infrastructure, and Deployment. As enterprises move from data preparation to model deployment, new challenges emerge in ensuring responsible AI practices. Enterprises have to prepare and address for - 1. Secure Model Hosting: Protecting AI models from unauthorized access or tampering. 2/ Access Control: Implementing robust authentication and authorization mechanisms. 3/ Prompt Security: Safeguarding against malicious inputs that could manipulate model behavior. 4/ Monitoring and Logging: Continuously tracking model performance and user interactions. 5/ Adversarial Attack Prevention: Defending against attempts to exploit model vulnerabilities, amongst other things. In practice, this means implementing stringent security measures and monitoring systems (at scale). For example, let’s say a life sciences firm deploys AI models to analyze real-world data integrating their electronic health records and claims databases for NLP analysis. These models and their GenAI applications will need strict access controls to ensure only authorized personnel can access them, the sensitive health data they process, and the outcome they generate. The models and the insights they generate are also valuable assets that need protection. Finally, all data access and model predictions must be logged for regulatory compliance and to detect any potential misuse or bias in the system. Responsible use of AI means that the enterprise would need to think through all this, and implement this in a fashion where operations does not become the choke point for the enterprise. And btw, you cannot bank on prompt engineering to solve for this! Why? Because LLMs are not deterministic! So, we cannot rely on them for security. Implementing responsible AI at scale requires a comprehensive security and governance approach. On AWS, for example, organizations would need to implement secure VPCs, use dedicated accounts for different environments, and configure fine-grained access controls for services like Amazon Bedrock and the various foundation models. User-level permissions enforcing least privilege would need to configured using IAM and SSO. Also, AWS Control Tower for secure multi-account setup, and AWS Config and Security Hub for monitoring and compliance would be warranted. This multi-layered approach helps secure AI models and data throughout their lifecycle. 💬 How is your organization addressing the challenges of secure model deployment and usage? In our next post, we'll explore the final layer of our framework: Model Consumption & Output Management. 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #ResponsibleAI #AIinLifeSciences #HealthcareAI #AIEthics #AIGovernance #AISecurity

6

🤖 AI Agent Workflows: Revolutionizing the Industry (Part 4) 🚀 This is part 4 in the series of diving deeper into Andrew Ng’s Agentic Design Patterns (part 1 - https://lnkd.in/gERzwA8N; part 2 - https://lnkd.in/easeNzhg, part 3 - https://lnkd.in/e-N8F_jT). Today we will discuss the Planning Design Pattern - enable an LLM to determine and execute a sequence of steps to achieve a specific goal. The Planning Agentic Design Pattern finds its roots in the Chain of thought prompting technique, introduced by Wei et al. (2022). While still an evolving field of study, the planning design pattern dictates that we let LLM autonomously decide the optimal sequence of steps required to accomplish a complex task. By breaking down the task into smaller, manageable steps, the LLM can execute each step efficiently, leading to better overall performance.   Key Aspects of Agentic Planning to keep in mind: 1/ Autonomy: The LLM makes decisions about what steps to take without predefined workflows. 2/ Adaptability: The plan and execution adapt in real-time to new information and challenges. 3/ Unpredictability: Each run may produce different plans and outcomes based on the specific context and LLM's decisions.   Let’s see the planning design pattern in action by leveraging it to determine the ‘Next Best Message for HCPs’ in the Pharmaceutical Industry. In the pharmaceutical industry, providing personalized and relevant content to Healthcare Professionals (HCPs) is crucial for fostering engagement and driving product adoption. The Agentic Planning Design Pattern can help create a dynamic, adaptive process for determining the most suitable message for each HCP.   Without utilizing the Agentic Planning Design Pattern, the process of determining the "Next Best Message" for HCPs might look like this: 1/ Gather data on the HCP's preferences, prescription history, and engagement with previous messages. 2/ The LLM generates a list of potential messages or content pieces, considering the HCP's preferences, industry trends, and regulatory guidelines. 3/ The LLM selects a message from the list without a systematic approach to ranking or prioritizing options.   By incorporating the Agentic Planning Design Pattern, the workflow becomes dynamic, adaptive, and autonomously executed at runtime. See attached illustration to understand how it may play out. Please note that while Planning is a powerful capability, it is a rapidly evolving concept and unpredictable i.e. you cannot predict how the LLM would plan each iteration because variation in the context (signal/error/pattern) could lead it to a completely different execution path. 💬 How do you envision leveraging planning design pattern for your Agentic workflows? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws

10

UAE: Pioneering the Future as the Global Hub for AI in Healthcare In recent years, the UAE has rapidly positioned itself as a global leader in Artificial Intelligence, with a particular emphasis on revolutionizing healthcare. As a senior healthcare professional with a passion for real-world evidence, innovation, and digital business development, I am thrilled to witness and contribute to this transformative journey. The UAE's strategic vision is clear: harnessing the power of AI to enhance healthcare outcomes, streamline operations, and create patient-centric solutions. This vision is being realized through substantial investments in AI infrastructure, fostering a robust ecosystem for startups and innovators, and establishing collaborations with leading global technology firms and educational institutions. Key initiatives, such as the UAE's National Strategy for Artificial Intelligence 2031 and the establishment of the AI Lab by the Dubai Health Authority, are testaments to the nation's commitment to integrating AI into the healthcare sector. These initiatives aim to reduce the burden of chronic diseases, predict and manage health crises, and ultimately, improve the quality of life for citizens and residents. Prominent healthcare organizations like M42 and PureHealth are at the forefront of this AI revolution. M42, known for its cutting-edge technology solutions, is driving significant advancements in healthcare delivery through AI integration. PureHealth, the UAE’s largest healthcare network, is leveraging AI to enhance patient care, optimize operations, and deliver personalized health solutions. The UAE's forward-thinking approach is also evident in its regulatory framework, which is designed to support the safe and ethical implementation of AI technologies. This balanced approach ensures that innovation thrives while patient safety and data privacy remain paramount. As we move forward, I am excited about the endless possibilities that AI brings to healthcare. From predictive analytics and personalized medicine to advanced diagnostic tools and automated workflows, AI is set to redefine the healthcare landscape. The UAE's proactive stance not only sets a benchmark for other nations but also attracts global talent and investments, further solidifying its position as the epicenter of healthcare AI. In conclusion, the UAE's journey to becoming a global hub for AI in healthcare is a remarkable example of visionary leadership, strategic investment, and a relentless pursuit of excellence. Organizations like M42 and PureHealth are pivotal in this journey, showcasing the UAE’s commitment to pioneering healthcare innovation. I am honored to be part of this journey and look forward to the groundbreaking innovations that lie ahead. #UAE #AI #HealthcareAI #Innovation #DigitalHealth #FutureOfHealthcare #AIinHealthcare #GlobalLeadership #Vision2031 #M42 #PureHealth

6

𝗧𝗵𝗲 𝗙𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗔𝗜 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲: 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻𝗶𝘇𝗶𝗻𝗴 𝗖𝗮𝗿𝗲 𝗮𝗻𝗱 𝗢𝘂𝘁𝗰𝗼𝗺𝗲𝘀 Artificial Intelligence is not just a buzzword—it's transforming healthcare at every level. As we look to the future, #AI is poised to reshape everything from patient care to medical research, making the industry more efficient, precise, and personalized. Here’s how AI is revolutionizing healthcare: 𝗘𝗮𝗿𝗹𝘆 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝗶𝘀 & 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 🧠 – AI algorithms are becoming increasingly adept at detecting patterns in medical data, enabling early detection of conditions like cancer, heart disease, and neurological disorders. With predictive models, healthcare providers such as Aster DM Healthcare can intervene earlier, improving patient outcomes and reducing healthcare costs. 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗠𝗲𝗱𝗶𝗰𝗶𝗻𝗲 💉 – AI is unlocking the power of #genomics and precision #medicine, allowing treatments to be tailored to individual patients based on their genetic makeup. This personalized approach can lead to more effective therapies and fewer side effects. 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄 & 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆 ⚙️ – From automating administrative tasks like scheduling and billing to streamlining clinical workflows, AI is helping healthcare providers focus more on patient care and less on paperwork. AI-powered tools can also assist doctors with decision-making by quickly analyzing vast amounts of medical data. 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗦𝘂𝗽𝗽𝗼𝗿𝘁 📊 – AI is becoming an essential partner for clinicians. By analyzing patient data, AI can provide actionable insights and help #clinicians make more informed decisions, reducing errors and improving the quality of care. 𝗥𝗲𝗺𝗼𝘁𝗲 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗧𝗲𝗹𝗲𝗺𝗲𝗱𝗶𝗰𝗶𝗻𝗲 🌐 – With the rise of wearable devices and AI-powered telehealth platforms, patients can receive continuous monitoring outside of traditional clinical settings. This allows for real-time intervention and improved chronic disease management, making healthcare more accessible and proactive. 𝗗𝗿𝘂𝗴 𝗗𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝘆 & 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 💊 – AI is transforming the drug development process by predicting which drug formulations are most likely to succeed, speeding up clinical trials, and ultimately reducing time-to-market for life-saving drugs. The potential for AI in healthcare is immense. As technology continues to advance, the role of AI in improving patient care, increasing efficiency, and lowering costs will only expand. In short, AI is not just a tool for healthcare—it’s a game changer that will shape the future of the industry for generations to come. What are you most excited about when it comes to AI’s role in healthcare? #AIinHealthcare #HealthTech #ArtificialIntelligence #PersonalizedMedicine #HealthcareInnovation #Telemedicine #DigitalHealth #FutureofHealthcare #uaehealthcare #india #saudi PureHealth Johns Hopkins Aramco Healthcare (JHAH)

8

🥂Celebrating Visionary Leaders and Encouraging Action 🚀 McKinsey estimates that Generative AI (GenAI) could generate $60B-$110B a year in economic value for pharma/medical products by boosting productivity, across drug compound identification, development & approval processes, and optimizing product marketing strategies - https://lnkd.in/efJ9Wkrx Many leading healthcare/life sciences organizations are starting to prioritize GenAI education and enablement for their teams. Over the last few week, I have had the pleasure of interacting with a few such visionary leaders who emphasized the importance of up-skilling their teams on GenAI. These leaders were happy to carve out half a day of their and their team’s time to understand GenAI, see multi-modal genAI in action, discuss GenAI hype vs reality, review GenAI adoption journey, and discuss areas where LoB and IT can start adoption exploration for their first MVP (minimal viable product). I couldn't agree more with this forward-thinking approach, and would like to share some action items to inspire leaders, across industries, to follow suit! Ask for your technology and/or GSI partners to conduct ‘Innovation w/ GenAI’ workshops with your cross-functional teams of LoB (line of business) and IT professionals on the fundamentals of GenAI and its various applications within your industry. Make sure these workshops are NOT designed as presentations or marketing services, but an interactive dialog where your teams can ask questions, see real world applications on how GenAI is being used, discuss sensitive topics, understand what a typical GenAI journey looks like, and get to understand the change management aspect of the adoption journey. Next, allow for collaboration between IT and business professionals to exchange ideas and explore GenAI-driven solutions for optimizing tasks and workflows for their most time consuming manual processes. I like to facilitate such sessions using design thinking constructs to allow for collective ideation, grouping, and voting to identify the best candidates for an MVP (minimal viable product). Important side note – I discourage POCs now because the technology has been proven and now being adopted at scale across industries. It’s time for action and accelerated adoption, so start with a MVP and iteratively develop/scale your GenAI application. Finally, provide a safe environment for experimenting with GenAI tools and techniques, allowing teams to develop solutions and evaluate their potential benefits. By setting time/scope/budgetary guardrails you can control the blast radius for safe experimentation and foster a learning culture for accelerated adoption. 💬 How are you enabling your cross-functional teams on GenAI adoption? 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

8

The next few years in healthcare AI are poised to be very exciting due to significant advancements across various aspects of the field. We’re standing on the brink of transformative change, where AI augments clinical expertise and enhances patient care. As we navigate this journey, it’s crucial to embrace a mindset of continuous learning and innovation. By working together and exploring new ideas, we can thoughtfully integrate technology and human expertise to improve healthcare outcomes. What advancements in healthcare AI are you most excited about? Let’s discuss! 🚀✨

251

108 Comments

🌍 AI in Healthcare: Driving Patient-Centric Transformation & Bridging Inequality Gaps With healthcare generating over 2.3 zettabytes of data annually, AI’s role in transforming patient care and tackling health inequalities is immense. By using diverse data—like patient history, lifestyle, and environmental factors—AI helps healthcare providers deliver personalized, predictive care that’s more accessible and impactful. From improving patient outcomes to connecting traditionally underserved communities, AI can bridge gaps, support healthcare systems, and place patients at the center of care. The future is patient-first, inclusive, and data-driven! Read more in our collaboration with Custom Content by WSJ: https://lnkd.in/eWU9Cmnn #PatientCentricity #AI #HealthEquity #HealthcareTransformation #Innovation #SimonKucher

35

2 Comments

Unveiling the Next Frontier: AI Integration in Modern Society As AI (Artificial Intelligence) continues to evolve, experts foresee a future where machines simulate human knowledge, emotions, and even self-awareness. Emotion AI, in particular, holds promise for applications in healthcare, customer service, and advertising. However, each advancement raises new ethical and practical considerations, necessitating responsible implementation and ongoing evaluation. Applications in Supply Chain and Related Fields In supply chain management, AI offers unparalleled opportunities for predictive forecasting, inventory optimization, autonomous decision-making, risk management, and personalized customer experiences. Similarly, manufacturing companies benefit from AI-driven enhancements in production optimization, quality assurance, energy efficiency, and human-machine collaboration. Despite concerns around data security, AI is gradually gaining traction in industries like food and beverage manufacturing and precision aerospace engineering. Revolutionizing Agriculture and Healthcare In agriculture, AI aids in crop planning, disease detection, and livestock monitoring, revolutionizing traditional farming practices. In healthcare, AI facilitates early disease detection, personalized patient care, and streamlined administrative tasks, promising improved outcomes and efficiency gains. However, public skepticism persists, highlighting the need for transparent communication and robust data privacy measures. Transforming Retail and Beyond Retailers leverage AI for customer service automation, demand forecasting, inventory management, and logistics optimization. Amazon's use of AI in warehouse operations exemplifies its potential to streamline processes and reduce costs. Yet, widespread adoption hinges on addressing societal concerns and ensuring AI's alignment with human values and interests. Overcoming Adoption Challenges While fears of job displacement and data reliability linger, history suggests that technological advancements create new opportunities for skill development and innovation. Companies must prioritize data integrity and verification processes to build trust in AI systems and mitigate potential risks. Strategic evaluation and gradual adoption are key to harnessing AI's transformative potential while addressing societal and ethical considerations. Embracing the AI Revolution As AI matures, businesses must seize the opportunity to enhance efficiency, cut costs, and drive innovation. Embracing AI isn't just about staying competitive; it's about unlocking new frontiers of possibility, empowering people to achieve more, discover more, and propel industry forward. Failure to embrace AI would mean foregoing a crucial competitive advantage in an increasingly digital world. #aiintegration #airevolution #aibenefits

1

Large Language Models (#LLMs) are transforming #healthcare across various domains. 1. Clinical Support: Data Extraction and Summarization: LLMs streamline the workflow of healthcare professionals by extracting and summarizing vital information. Enhancing EHRs: LLMs integrate seamlessly with #EHRs, transforming raw data into structured summaries that aid quick and informed decision-making. Reporting and Decision Support: LLMs automate data processing tasks, generate comprehensive medical reports, and offer decision support. Clinical Documentation and Coding: LLMs improves coding for billing and real-time transcription of medical consultations. Information Integration and Semantic #Interoperability: LLMs ensures that data remains consistent and meaningful across different health systems. 2. Patient Care Personalized Education and Engagement: LLMs offer tailored health information, enhancing patient engagement and understanding of their health conditions. Care Pathways: LLMs refine the referral process and symptom checking, directing patients accurately through the healthcare system which optimizes care delivery. Medication and Treatment Adherence: Personalized reminders and communication help patients stick to their treatment regimes. Predictive Care: Monitoring data from wearable technologies enables LLMs to forecast health issues, allowing for preemptive healthcare interventions. Population Health Management: LLMs anticipate public health needs and help optimize resource allocation and disease management. 3. Operational Efficiency Streamlining Administrative Processes: Automating routine tasks such as scheduling and billing, LLMs enhance operational efficiencies. Data Management: Integration of EHRs by LLMs ensures data is up-to-date and reducing potential errors. 4. R&D Accelerating #Drug Development: By predicting molecular interactions and simulating chemical structures, LLMs accelerate the discovery and development. #Biomedical Research: LLMs process vast amounts of scientific literature to identify trends and knowledge gaps, directing future research initiatives. Clinical Trials: The generation of synthetic data for preliminary studies. 5. Medical Education and Training: Virtual Simulations: LLMs create realistic #medical #training scenarios, providing students with valuable practice without risk. Decision-Making Skills: LLMs provide immediate feedback and #decision support, aligning student choices with current medical guidelines. Customized Learning Experiences: Training is tailored to the individual’s progress and needs. Bridging Theory and Practice: LLMs help students apply theoretical knowledge in practical settings. Expert invitation for comment: Peter Lee Dean Coutinho Sigrid Berge van Rooijen Kerrie Holley Dr. D. Martin Alexander Gershon, MD, JD, MPH Imran Chaudhri Don Perugini Adlar Kim Patrick Lovelace Richard Braman Doug Johnston Xavier Urtubey, MD, MBA Dimitrios Kalogeropoulos, PhD

30

1 Comment

🚀 The Case for a GenAI Enterprise Platform 🏢 We have successfully transitioned from 16+ months of Generative AI (GenAI) POCs to organizations now moving GenAI workloads to production. As organizations increasingly leverage GenAI technologies to drive innovation, the need for a comprehensive GenAI enterprise platform becomes evident. Other than arresting budget dilution, such a platform can enable organizations to harness the full potential of GenAI while addressing critical aspects like reusability, governance, security, reliability, metering, LLMOps/MLOps - resulting in faster time-to-market and lower run costs. Let’s dive into the benefits of an Enterprise GenAI platform - First and foremost, a centralized GenAI enterprise platform will arrest budget dilution. Large enterprises are working with multiple vendors, across LOBs as they rush to get first to bring their GenAI applications to their end users. They are adopting tools, platforms, LLMs, open source libraries, and GSIs that are duplicating efforts. While divergence is a great tactic during the experimentation phase, it costs a lot and is not sustainable in the long run. One of my early adopters has the best mental model on this journey – they are allowing for divergence as long as everyone agrees to an end date and a plan to converge within a reasonable time frame to central IT policies and roadmap. Next, GenAI Enterprise Platform will encourage the development and sharing of reusable GenAI components, reducing duplication of efforts and fostering collaboration across teams. It allows for up-skilling of existing resources, develop talent pool, and hardens processes to support applications in production. GenAI Enterprise Platform will help enforce a centralized governance framework that will promote responsible GenAI app development, adhering to regulations, responsible ai policies, data security, privacy, and ethics. It will allow reliability engineers to work through and enable high availability, scalability, and performance. Will help centralize LLMOps/MLOps to streamline LLM deployment, evaluation, monitoring, and continuous improvement (not to mention, restrict access to unapproved models) One of the big lessons learnt from cloud transformation journey in the last decade, is the ability to meter consumption of resources by the enterprise - show-backs and charge-backs. Adopting centralized metering capabilities, budgets, and tagging will provide visibility into GenAI resource consumption, allowing organizations to monitor usage, optimize costs, and allocate resources efficiently. Not only will GenAI Enterprise Platform help avoid the problems we have conquered during cloud adoption, it will also help accelerate time to market and lower run-cost, allowing organizations to quickly adapt to market changes and capitalize on new opportunities. 💬 What are your thoughts on Enterprise GenAI platform? 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

9