Ajay Nyamati

One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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🥂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

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💰Tempus AI Raises a massive $410 Million in IPO ! Rock solid entry at Nasdaq with shares jumping 8% - strong market push for AI in medicine 🔬 Key numbers of Tempus AI Tempus provides AI and data analytics tools for precision medicine across oncology, neuropsychiatry, radiology, and cardiology. More than 7,000 physicians in 2,000 hospital systems, including 65% of all U.S. academic medical centers, use Tempus' platform. The company collaborates with major pharma companies like Pfizer, GSK, and AstraZeneca. 💉 Impressive Growth and Collaborations Tempus employs about 2,300 people and generated $532 million in revenue in 2023. The company raised $1.3 billion over nine funding rounds, with a valuation of $8.1 billion in 2023. Tempus acquired AKESOgen, Highline Sciences, and Arterys over the past 5 years. Investors include Google, Baillie Gifford, Franklin Templeton, NEA, and T. Rowe Price. 🧪 Transforming Healthcare with AI Founded in 2015 by Eric Lefkofsky, Tempus aims to advance precision medicine using AI. The mission is to ensure patients receive the right drug at the right time. With a vast library of multimodal data, Tempus provides AI-enabled solutions to physicians, facilitating personalized patient care and the development of optimal therapeutics. Tempus' capabilities span drug discovery and genomic sequencing, initially focusing on oncology and expanding to other medical fields.

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On Tuesday 2 July Ankur Kalra and I are hosting a webinar on 3 key questions to evaluate AI investment opportunities in healthcare: 1. What part of your investment's AI lifecycle is defensible?  2. How does generative AI influence your investment strategy?  3. How should you assess Leadership Capability in the AI team? We'll also be talking about what breakthroughs in AI seem imminent in 2024 - what should you look out for. Ankur who is based in the US is the founder and CEO of Hop Labs, a machine learning consultancy focusing on reproducible ML research and scalable ML systems in healthcare, life sciences, and other industries. Prior to Hop Labs, Ankur was a senior technologist at Amazon, and was involved in the launch of Amazon Prime and Amazon Instant Video. Our virtual event is for: ➡️ Investors with an interest in AI in healthcare ➡️ Innovative companies deploying AI and seeking investment in 2024 ➡️ Advisers working with investors and companies seeking funding If you'd like to join us pls register using the link in the comments.

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AI in Healthcare is a hot topic. But what is the reality of it today vs a few years from now? This is what I wanted to dive into with our good friend Ankit Jain. This was a great conversation on AI and the Trough of Disillusionment. Trough of Disillusionment is a phase in the Gartner Hype Cycle that occurs when interest in a new technology wanes after initial hype and expectations are not met. Internet, cell phones, etc all went through this phase of failed ventures until the real use cases started to be come common place. It's hard to remember that the internet went through this. We had a wave in early 2000 and then it all broke when it wasn't being adopted as fast or just wasnt ready for real prime time. I think we are in this phase right now with AI being a hot topic and people can see the potential. Yet, full scale practical changing of my entire world or job isn't there yet (for most things). check it out... https://lnkd.in/gjg6w_m2

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I caught up with Nikhil Krishnan of Out-Of-Pocket fame to get his take on the current state of the healthcare technology landscape. Up first: his current thoughts on what's exciting and where there are opportunities for AI-driven solutions. First half of our Q&A below 👇 — 🟩 Q: When it comes to AI, what area are you most excited about? Patient-facing solutions. The AI admin and back-office space has quickly become saturated, and incumbents will always have a distribution advantage here. Which is why I’m actually surprised there aren’t more players entering the patient-facing side. I think the traditional healthcare companies are too nervous to actually go into that patient-facing area. And it's also a regulatory gray area, so if you actually get traction, you can probably help shape how this stuff ultimately gets regulated. So right now, I think the greatest opportunities are for patient-facing AI tools. 🟩 Q: Where specifically do you see whitespace to develop patient-facing solutions? I think there’s still a lot of opportunity on the patient-facing automation side. A great example actually is automated lab results. There has been an issue for the last 3 years that lab results automatically get released to patients, and they see something's abnormal. The patient is trying to figure out what’s going on and what they've read. Part of this is explaining the result, which is something Elaborate is working on, and in some cases, actually, it's really useful for the patient to get that notice immediately with follow up steps, like scheduling a diagnostic. Another area is AI that can autonomously make workflow changes within a local system. Viz.ai is working on this automated workflow piece in the context of situations like stroke care coordination and hypertrophic cardiomyopathy. This requires not only automation in terms of being able to understand the context of multiple datasets, but also make the requisite workflow changes (e.g. escalating to the right specialist). These are particularly useful in time-sensitive areas. 🟩 Q: Any other AI whitespaces? Monitoring of AI models. It's one of the areas that's still unknown in terms of how do you give peace of mind to your buyer? It becomes really hard to actually evaluate and audit a lot of these AI algorithms without some form of data exhaust. I feel like one of the things that will happen is you'll see some form of certifying entity emerge that can give a gold standard to hospitals. 🟩 Q: What else are you thinking about as it pertains to AI? I do think that pricing is one of the more interesting components here. For example, in AI scribing, how you think about pricing changes a lot depending on if you’ve got a human in the loop or not. Because adding a human layer goes from near zero marginal cost to cost scaling linearly. One of the ways that companies will be able to differentiate themselves — especially from incumbents — is the pricing model.

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🚀 Upscaling Generative AI as an Enterprise Platform 🥌 In my experience, adopting Generative AI (GenAI) transcends a mere application choice; it represents a strategic enterprise-wide platform decision that demands careful consideration and alignment with organizational goals. As enterprises embark on their GenAI journey, it's vital to treat scaling efforts with the same strategic approach as previous enterprise-wide initiatives, like cloud adoption. To successfully scale GenAI, consider forming a steering committee that brings together cross-functional stakeholders. This committee will oversee strategic planning, resource allocation, and change management, ensuring a holistic approach to adoption. Have you been part of a steering committee for enterprise-wide initiatives? If so, you will relate with this approach. Looking back at cloud adoption a decade ago, we can draw parallels to today's GenAI landscape. Just as organizations needed to re-architect their IT infrastructure and embrace new workflows for cloud integration, scaling GenAI requires a similar mindset shift and strategic planning. We should start by looking at business processes. Engage with stakeholders from various LoB (lines of business)—clinical, IT, and business—to foster cross-functional collaboration and gather diverse input. Just like we had to explain the business benefits of cloud to everyone during cloud adoption, scaling GenAI warrants similar motion. Educate & Communicate. Don't make assumptions. Adopt an agile approach to development, testing, and deployment, allowing for continuous learning and improvement as you scale GenAI applications. There is so much to be done. My preferred execution model is Scaled Agile Framework (SAFe) Program Increment planning. The goal is to align everyone on a shared vision, prioritize features, identify dependencies, and create a detailed plan for the upcoming Program Increment (PI), which is a time box of 8-12 weeks. Lastly, don't forget the importance of data governance, quality and privacy. Address any challenges and comply with regulations to build trust and pave the way for successful adoption. I have witnessed numerous organizations postponing implementing a comprehensive data strategy and robust data governance practices during previous transformation initiatives, such as cloud adoption. I would strongly advise against repeating these oversights, as they can hinder long-term success and impede the realization of desired outcomes. 💬 Do share your experiences and insights on GenAI adoption 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 #cloud #data

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💡📈 Building a Strong Business Case for GenAI Investment Few Healthcare and Life Sciences organizations have begun adopting GenAI across their operations, and are already seeing tangible benefits (https://shorturl.at/fgrPZ). Initial reported use cases are focused on efficiently extracting and searching for relevant information within notes, reports, and other documents, and creating personal employee productivity tasks (https://shorturl.at/beqR1). Adoption of GenAI is particularly high in the areas of operations, research, and administrative tasks. 🙄 While GenAI adoption is surging, many use cases continue to mimic the user experience of ChatGPT. It's almost as if the industry is being constrained by the first mover, rather than exploring the full potential of these powerful AI models. Still, most firms continue to struggle to convince stakeholders to invest in GenAI. As leaders, you need to be able to showcase its potential impact on your organization and balance implementation costs against the cost of maintaining the status quo. Here are key points to consider: 1. 💰 Cost Comparison: Analyze the upfront and ongoing costs of implementing GenAI versus maintaining current processes, highlighting potential long-term savings and benefits. 2. 📊 Return on Investment (ROI): Calculate the overall financial return from implementing GenAI, considering factors like cost savings, productivity gains, and revenue increases. Use this data to justify the investment. 3. 🏆 Value-Driven Metrics: Focus on metrics that demonstrate GenAI's impact on business value, such as increased customer satisfaction, improved decision-making, and enhanced product/service quality. 4. 📲 Scalability and Flexibility: Evaluate GenAI's ability to scale alongside your business and adapt to changing needs, enabling future growth and cost savings through automation and efficiency gains. 5. 🛡 Risk Mitigation: Assess potential risks and challenges associated with GenAI implementation, such as integration issues or data privacy concerns. Develop strategies to mitigate these risks and ensure a successful rollout. Five-Step Framework for Building a GenAI Business Case: 🕵‍♀️ Identify Opportunities: Pinpoint areas that can benefit from GenAI, focusing on tasks or processes that are time-consuming, repetitive, or require creative input. 📊 Estimate Impact: Quantify the potential benefits of GenAI, using key metrics to support your estimates. 🚧 Assess Risks and Challenges: Identify potential risks and challenges and develop strategies to mitigate them. 🗺 Develop a Roadmap: Create a detailed implementation roadmap that outlines the steps required to integrate GenAI into your organization. 🔍 Monitor and Evaluate: Measure the success of your GenAI implementation using key metrics and success indicators, adjusting your strategy as needed. 💬 Share your experiences on building a business case for GenAI! 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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🌐 The future of healthcare is being shaped by #AI, and your voice matters! #IPPOSI is forming a 'Citizens' Jury' to gather public insights on the role of AI in healthcare. What are your thoughts on integrating AI into this vital sector? #AIinHealthcare #PublicOpinion #HealthTech

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🔮 Embracing Uncertainty: Healthcare Leadership in the AI Era. AI might soon perform "80% of 80% of all jobs" stated Vinod Khosla, an early OpenAI backer at the The Wall Street Journal Tech Live Conference. AI is poised to revolutionize not just healthcare but nearly every aspect of our professional lives, how do we, as leaders, pivot, adapt, and most importantly, thrive? My new article “Embracing Uncertainty: Healthcare Leadership in the AI Era” explores this transformation. Read now: https://lnkd.in/dyMJ9wNP HIGHLIGHTS + Leadership authenticity, empathy, adaptability are taking center stage + Top Universities are onto something big targeted Healthcare AI courses + VR and AI-powered leadership development immersive experiences + AI State Laws Tracker & Presidential Executive Order AI Tracker  🔗 Read article: Embracing Uncertainty: Healthcare Leadership in the AI Era https://lnkd.in/dyMJ9wNP ~~~ A special thank you to California Association of Healthcare Leaders and American College of Healthcare Executives for their support and providing AI education for healthcare leaders. ~~~ Harvard Business Review Harvard University, Stanford University School of Medicine, Massachusetts Institute of Technology DeepLearning.AI, Google, Microsoft AI OpenAI Coursera, edX Google DeepMind Google Labs’ Transfr Monark #AI #Healthcare #AIHealthcare #HealthcareLeadership #DigitalHealth #AIInHealthcare #EthicalInnovation #openai #microsoft #google #mit #stanford #harvard #FutureOfHealthcare #CAHL #ACHE #AIInHealthcare #DigitalTransformation #Innovation ~~~

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I am grateful to the Forbes Tech Council for featuring me in their latest expert panel on “Innovative Data Management.” AI is unlocking vast human potential in health plan operations by streamlining manual tasks, and provider data management serves as a compelling case study for this evolution. The downstream impacts of AI’s utilization in areas like roster automation, mining provider legal contracts and improving directory accuracy are profound and hold significant implications for the entire healthcare industry. As roster ingestion becomes fully automated and directory accuracy improves, we are witnessing firsthand how AI creates space for humans to engage in higher-order thinking. Generative AI is only going to accelerate this trend, and I am proud that HiLabs is part of that innovation

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Brilliant and Timely: Why Data is the Top Priority in the Life Sciences AI race? Verbatim from Health Catalyst Capital (HCC): Over the past several decades, the life sciences industry has faced a troubling decline in R&D productivity. The cost to bring a new drug to market has increased from about $400 million in the 1980s to over $2 billion in the 2010s, adjusted for inflation. This cost increase is largely due to increasingly complex biology and the inefficiency of the traditional drug development process that relies on a “trial and error” approach. AI stands out as a potentially transformative technology for life sciences companies, capable of analyzing vast amounts of complex data and predicting experimental and clinical outcomes. NVIDIA CEO Jensen Huang has even stated that digital biology will be one of the most significant revolutions in human history.   And yet, the life sciences industry is plagued by a problem preventing it from unlocking the full potential of AI, a problem that other parts of the healthcare ecosystem know all too well: siloed data. According to a McKinsey survey, the lack of integrated data sources was cited by pharma leaders as the largest hurdle to scaling digital and analytics capabilities. Furthermore, nearly half (48%) of senior decision-makers in pharmaceutical companies surveyed by Aspen Technology stated that data silos derailed the efficiency of cross-functional collaboration in their organization.   Data is often siloed within different departments, systems, and repositories in life sciences companies. Without an integrated infrastructure, it’s difficult to pinpoint existing data, access it quickly, and leverage it across the organization, which often stifles decision-making and innovation. Data is the cornerstone of AI models and remains a top priority in the life sciences AI race. A key strategic differentiator is the ability to generate, acquire, and control large datasets from diverse sources for training models in areas such as omics, imaging, and health records.4   Before life sciences companies can achieve digital and analytics transformation, they must embrace data transformation, which is where DataOps comes in. Thanks for sharing Charles Boorady! Check out Unipr to learn how you can lead the charge by using Operations AI to raise R&D productivity in Life Sciences!

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Will Generative AI Transform Healthcare? Uncover the Opportunities and Challenges. Generative AI is poised to transform healthcare, offering unprecedented capabilities in data management and patient interaction. As investments from both tech giants and innovative startups flood this emerging sector, the potential for groundbreaking developments is significant. However, this enthusiasm is tempered by considerable concerns. The readiness of AI systems, coupled with issues of bias and privacy, has prompted calls for a cautious approach. Healthcare professionals and industry advocates stress the importance of establishing stringent regulatory frameworks and conducting thorough testing to ensure that AI technologies are both safe and effective. For investors, the integration of AI into healthcare presents a unique dual opportunity: to lead the charge in technological innovation while addressing the ethical and practical challenges it brings. This careful balance of advancement and accountability is crucial in navigating the future landscape of healthcare technology. https://hubs.la/Q02t4NwV0 What are your views on AI’s role in modern healthcare? Join the conversation below and share your insights.

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Honored to have hosted an inspiring panel at our HLS Spring event, where five industry captains shared their insights on how to advance AI-led transformation in healthcare and life sciences. Our esteemed leaders, Aalpen Patel, MD, MBA, FSIR, Peter Slavin, MD, Neil Gomes, Manish Varma, and Shub Bhowmick, gave us the 10 commandments to drive impact and innovation. Their top 5 recommendations: 1. Embrace the transformative power of AI/Gen AI and push the envelope on innovation. 2. Democratize the use of AI and Gen AI platforms and ecosystems. 3. Let AI handle the mundane tasks while you focus on what matters. 4. Educate everyone on the possibilities and be an evangelist for the new Gen AI capabilities. 5. Focus on core business problems, measure value and innovate. accordingly. Their 5 life experiences: 1. Stay curious and question the status quo. 2. Take measured risks and play on your pitch. 3. Stay focused on core business problems and don't get distracted. 4. Have a multi-disciplinary approach to solving problems. 5. Don't take life too seriously and bring an outside-in perspective. Let's keep the conversation going and drive AI-chassis for Healthcare and Life Sciences. #Tredence #Beyondpossible #HealthcareAI #SettingAIChassis

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An American psychiatrist turned entrepreneur (David Mou MD MBA), a French VC with significant international experience (Sandrine Egron), a Thai public servant focused on innovation (Dr Krithpatka Boonfueng), and I shared insights across geographies on connected health, its limitations and opportunities. Key takeaways from what I thought was a refreshingly honest conversation about an otherwise overhyped or overdemonised approach: - Benefits are highly dependent on the condition and use case - Telecare is not always about 'better care cheaper', but also about 'bringing care where there is none'. - It's not inherently less empathetic, but can serve different emotional needs for different patients. Some will like the anonymity of sound-only or text-only interactions. - Triage is key, a lot can be solved by self-service or low-touch telehealth solutions (like a video-based reminder of how to use an asthma spray or a refill of a prescription), reducing the burden on A&E departments and so on.  - Let's not just focus on the patient, let's focus on all people involved, including doctors.  - Geographical needs are very different and so are business models, but the core needs are obviously often very similar. #connectedhealth #telemedicine #digitalhealth  #HT24 health.tech conference Image © health.tech Conference

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💡 📖 Demystifying AI Decisions in Healthcare and Life Sciences: The Power of Explainable AI As healthcare and life sciences organizations increasingly adopt AI, ensuring trust and transparency in AI-driven decisions becomes crucial. Explainable AI (XAI) - a powerful approach that enables stakeholders to understand how AI models make predictions and reach conclusions. A recent research paper by Stephanie Baker and Wei Xiang (2023) explores how explainability creates trustworthy and socially responsible artificial intelligence. To learn more, access the article here: https://lnkd.in/e6TE4ADR Key benefits of XAI for healthcare and life sciences organizations include: 1. 🤝 Trust Building: XAI helps build trust in AI systems by providing visibility into the decision-making process, leading to increased confidence among patients, clinicians, and regulators. 2. 😇 Ethical AI Adoption: By ensuring AI decisions are explainable, XAI facilitates ethical AI usage, minimizing unintended consequences and potential biases. 3. ⚖ Regulatory Compliance: XAI enables organizations to demonstrate compliance with evolving regulations and guidelines, such as GDPR and FDA recommendations, which emphasize transparency and interpretability. Implementing XAI requires collaboration between data scientists, clinicians, and regulatory experts to develop interpretable models and user-friendly explanations. As generative AI becomes more mainstream within healthcare and life sciences organizations, business and technology leaders would benefit by analyzing which LLMs (prompts, design patterns, and platforms) assist with XAI, and if the outcomes of their applications aligns with their corporate Responsible AI policy. As always, learn by doing - talk to your technology and strategy partners to work with you on a POC (proof of concept) that aims to prove XAI that aligns with your responsible AI policy. 💬 Share your strategies for integrating XAI and promoting transparency in AI-driven healthcare and life sciences solutions below! #ExplainableAI #XAI #Healthcare #LifeSciences #Trust #Transparency #EthicalAI #GenerativeAI #HealthcareInnovation #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #lifesciences #patientcare #ai #clinicalresearch #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess 📢 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

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🏥 The Global Health Tech Boom: A VC's Playbook for US & India In the US, the consumer health tech market is expected to reach a whopping $400 billion by 2027, while India is experiencing a similar surge, with its consumer health tech market projected to hit a staggering $32 billion by 2025. As a VC actively investing in innovative health tech startups across both continents, here is what gets me excited: 🚥 Seed Funding ($0.5M-$5M): Focus on validation, not chasing unicorns. Early traction is crucial – think $300K-$2M in annual sales depending on your niche. Demonstrate efficient customer acquisition (sub-$30 CAC) and cultivate loyalty (lifetime value exceeding CAC). Most importantly, show product-market fit. A passionate user base willing to pay a premium is a winning formula. 🚥 Valuation: Seed-stage valuations are concept-driven. Expect a range of $3M-$15M, with a typical multiple of 5x-10x revenue. This considers market size, innovation, and team experience. 🚥 Series A ($10M-$20M): Scale-up time! We look for 2x-4x YoY revenue growth and a minimum of $2M in annual sales. This validates your customer acquisition strategy and a repeatable sales engine. Retention remains king! High customer retention signifies a strong brand connection and sustainable growth. 🚥 Valuation: Early traction and a clear path to profitability are essential. Series A valuations can range from $10M-$30M, with a typical multiple of 4x-8x sales. Revenue growth, market potential, brand strength, and team expertise influence this multiple. 🚦 Decoding Valuation in Global Health Tech: ⚡ Market Potential: A large addressable market with room for significant brand capture is key. Think millions of potential customers! ⚡ Profitability Path: Growth is exciting, but we need a data-driven roadmap to profitability, consider factors like insurance reimbursement models, pricing strategies etc. ⚡ Brand Strength: Show us metrics like social media engagement, brand mentions, and positive user sentiment. A strong brand can command a premium valuation. ⚡ Customer Empathy: Consumers are value-conscious and diverse. Deeply understand your target audience and tailor your product accordingly. ⚡ Passion is Key: Demonstrate your passion for transforming healthcare delivery on a global scale. 🚥 Key Areas of Opportunity: These areas hold immense potential: ⚡ Telehealth & Remote Patient Monitoring: Simplifying access to quality healthcare for remote populations. ⚡ Chronic Disease Management: Offering personalized solutions for managing chronic diseases like diabetes and heart disease. ⚡ Mental Health & Wellness: Address growing need for mental health support through accessible and affordable solutions. ⚡ AI-powered Diagnostics & Treatment: AI to improve healthcare accuracy, efficiency, and affordability. ⚡ Preventive Health & Personalized Medicine: Fostering proactive health through personalized preventive care and wellness solutions. 🗣 Love to hear from HealthTech Startups! 🏥

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🚀 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

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