Yusuf Ahmad

How do you get a group of 60 clinicians, health-tech engineers, and healthcare practitioners to come up with ground-breaking healthcare AI use cases and functional demos in 36 hours? I hosted Out-Of-Pocket's first healthcare AI hackathon at IDEO for an evening of rapid-fire brainstorming and concept sketching. Teams brainstormed use cases and problem areas before going wild into divergent concepting. Along the way, I shared my thoughts on the Creative Recipe of how to design AI successfully in healthcare, including: 1️⃣ Finding new feelings for the AI collaboration: As AI takes on forms beyond the chatbot, what emotions do we want to design at the moment of collaboration and what will that teach the user? 2️⃣ Fostering empathy in the simulation: Multiple studies has shown that AI is effective at crafting compassionate messaging for patients, especially in comparison to providers that are working under heavy patient loads and facing burnout. How can AI light a path on how we can be more empathetic with each other through simulation? 3️⃣ Planning for systems-level impact: In a world where for every AI startup looking to help providers speed up Prior Auth, there is a competing AI on the payer-side being trained to deny claims at the same speed. How can we ensure AI actually pushes for systems-level impact instead of just making one side of a broken fee-for-service system temporarily faster? From these foundations, teams spent the next two days furiously coding, designing, and interrogating. I had a great time helping shape the pitches and bringing a human-centered design to the projects. My favorite pitches? 🚑 hospital bed capacity management through immediate AI transcription of EMS calls 🤖 using AI to navigate pharmacy IVR phone trees to get the most up to date medication stock 📽 helping nurses make ADL reports using multimodal voice and video observation 🧪 using clinically informed AI interpretation to decrease patient anxiety around lab results releases as consequence of the 21st Century Cures Act 📝 automatic medicaid eligibility and form filler using your personal bills and forms you have on hand Shout-out to Nikhil Krishnan and Danielle Poreh for instigating and collaborating on a fun weekend. And to other hackathon mentors Luke Van Seters, Payal Patnaik, MPH, and Matthew Sakumoto! And IDEO AI design facilitators Angela Kochoska and Ziyuan(Zoey) Zhu #hackathon #ideo #design #healthcare #innovation

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This resonated with me today: "“One flaw of designers (as people) is pretending faster is always better and quick always equals efficient. A fast pace can feel good and productive short-term, but there are many long-term consequences: losing productivity, overlooking important things, and undermining diversity. It is not efficient to move so fast that we exhaust ourselves, overlook the obvious, or break everything along the way, yet we often make that assumption.” It's from Assembling Tomorrow and I highly recommend the book. https://lnkd.in/gK43NueY

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Thanks to Microsoft and Bowmans (Law Firm) for a great “Responsible AI” session today. Lots of talk on policy, tools, compute capacity, and of course talent & skilling. Made me realize we’re often conflating what are actually 4 distinct and wicked problem statements: (1) At a labor market level, how do we avoid large-scale unemployment and dislocation as Gen AI threatens a huge range of jobs (particularly junior digital jobs)? I find this particularly scary as the digital labor market is flat and borderless, and there’s a risk that many of the most entry-level digital jobs (the ones the rising generation of African youth could fill) will be wiped out by Gen AI imminently. (2) At a broad-based professional level, how can everyone do their jobs better with AI? The reality is that all of us, across careers, sectors and seniority, can and should be making use of Gen AI to boost productivity, use data better, and make ourselves obsolete. This isn’t confined to techies and programmers; this is relevent to pretty much everyone in broad “knowledge work” positions across ops, HR, finance, consulting, and more. (3) At an African youth level, how can young people “get a job in AI”, and what does that even mean? This is more about understanding and developing the specific career pathways into the AI universe, which we’re just starting to understand. This includes engineering of course, but also a broad range of other job types around data annotation and cleaning, prompt engineering, automation engineering, and much more. (4) At a social impact level, how can we design more AI-based applications and use cases to drive more value and impact to those who need it? AI is poised to disrupt and transform a broad range of how the world works day to day, and hopefully many of those applications and use cases make life easier for those most in need — applications around health, energy access, food access, livelihoods and income generation and more. (In fact, Shortlist Futures / Shortlist Professionals is about to launch a new fund looking at positive use cases for Gen AI for women’s economic empowerment, job access and income generation - watch this space!) While there are certainly many areas of overlap between these problems, each needs its own attention, research, and creative solutions. Others in the room (Winnie Karanu, Martin Ndlovu, Isis Nyong'o Madison, Rositsa Zaimova and many more) — what did I miss?

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From my students at Cornell Tech and Massachusetts Institute of Technology, to our work at Startups & Society Initiative and Responsible Innovation Labs, continue to see the brightest young people hungry for models of business and technology that reject 'growth at all costs' and embrace more deliberate notions of value creation. From climate, to democracy, to public health, the next generation holds deep intuition that value is not captured by 'valuation.' Its not that they outright don't believe in profit, but they know it's essential to build with intention and responsibility, mindful of broader societal impacts. It was a treat to join Keshav Rastogi — a young person already building and leading with his values — for his Podcast "Playgrounds to Pixels." Keshav asks thoughtful questions about the business motivations for responsible tech, my work at MEET - Middle East Entrepreneurs of Tomorrow, and how young people can start thinking about responsible tech. Thanks again for having me, Keshav.

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In the next episode of The Eric Ries show, I talk with Yancey Strickler, co-founder and former CEO of Kickstarter, and founder of Metalabel, a platform for releasing collective work. He’s also the author of This Could Be Our Future: A Manifesto for a More Generous World. Yancey, who started out as a music journalist, believes that creativity and humanity are implicitly connected. He’s been a forerunner in thinking about how to build companies that bring good things into the world and are also successful without devolving into extractive behaviors. He’s also had the rare experience of, as he says, “watching an idea go from impossible to explain to ubiquitous and instantly understood.” We talked about the founding and growth of Kickstarter, which has been profitable since it’s 14th month, the power of humility, past mistakes and future hopes, why he started Metalabel, and more. You can listen to or watch the episode here: - YouTube: https://lnkd.in/g76Ez6B5 - Spotify: https://lnkd.in/gSwg9kKb - Apple: https://lnkd.in/gJk5ng6H Meanwhile, here are a few takeaways from our conversation: 1. There are downsides and upsides to originating a new category: Crowdsourcing seems totally normal to us now, but only because Kickstarter led the way. At the beginning, there were years of struggle to even get people to understand the idea of conditional purchasing. But once they got the site built and people could see what it was, the excitement and adoption of this genuinely new platform were: “instantaneous – people knew they got it, they got what it was, they got how to use it.” 2. Maximizing growth isn’t always the right tactic. “We always had this thought of ‘we're not trying to be as big as possible. We're trying to be what feels right, what feels faithful in some nebulous way’,” Yancey says of Kickstarter. Staying true to their passion and their mission at the start ultimately led to success. 3. Crisis can be good. In many cases, it leads to uncovering the truth about what’s really going on. Instead of just trying to figure out how to make whatever’s going wrong go away, disaster can bring about real change. 4. Organizations are their own entities. They exert force on the people who found them and run them within them just as much as those people guide the organization. Learning to co-exist in that relationship is an art. “[Metalabel] exists beyond us without question. It has its own physics, its own personality that you need to listen to and be humble before. You're supporting it, you're serving it. And there's just so much art and humility and really hard work that comes with seeing it through.” — Lastly, a huge thank you to our sponsors. Without them, this podcast wouldn’t be possible: Mercury DigitalOcean Neo4j

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I had the pleasure of sitting down with Michael and Sharon from Builders to discuss my journey as a co-founder of orq.ai and the painful lessons I learned over the pas 20 years. What are some of your biggest learnings while building? Links also in the comments

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Adeel Khan's journey from educator to innovator truly highlights the potential of AI to revolutionize Education, while also addressing crucial challenges around ethical AI use in schools. Excited to see how MagicSchool AI continues to shape the future of Education. #ArtificialIntelligence #AIinEducation #EdTechInnovation #FutureOfEducation #MagicSchoolAI

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📣 Calling all the #emergingtech #founders and #builders out there. Do you ever worry that when the #regulators knock, they'll completely misunderstand what you're building? Do you feel like the headlines about your industry is more gossip than tech, but you're the one paying the price? Let's #disrupt this! 🚀 I've been working tirelessly to create a summit that will train you so you can lead the narrative, and stop playing defense. Join us at University of California, Berkeley on May 13th for TECHVocacy 2024: Emerging Tech Advocacy Summit! What makes this summit so different? No long winded panels to talk about the problem or to talk about the tech (#ai #crypto #web3 #av #socialmedia). We have 🔵 Experts to deliver lightning talks that will SHOW (not tell) how to talk about your emerging and disruptive tech with policymakers/regulators 🔵 Real-time media training demos Co-hosted by Berkeley Center for Law and Business Berkeley Haas Entrepreneurship Berkeley Center for Responsible Decentralized Intelligence with support from Berkeley Space Center. Admission is free, but registration is limited! Sign up soon!

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Join me Thursday, May 9 at 9amET, and Microsoft for Startups company founders, Stead Burwell, Lana Feng, Ph.D. Andrew Gostine, MD, MBA and Ankur Teredesai for a lively, *real-world* conversation about applying #AI to improve #healthcare. In addition to hearing these leading #innovators discuss the challenges, value, and future of AI, you'll learn how: 💲 Outbound AI uses AI to improve #RevenueCycleManagement 💊 Huma.AI uses #GenAI to provide meaningful and actionable #insights for #lifesciences companies 🛌Artisight uses #AI and #cognitiveservices to streamline #patientcare and #safety 😷 CueZen uses #AI and #GenAI for hyper-personalized management of #chronicconditions 👉 Registration is below. Stelios Hatzakis Tony Kountz Derek Haynes Bethany Cordes Tom Davis Ted Roduner Microsoft for Healthcare

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AI. A big idea that could reshape everything. But what about ethics? How do they fit together? In the latest episode of Pivotal Clarity,  I spoke to Maya Sherman, AI policy researcher and Ethicist at the Embassy of Israel.  She's doing interesting work at this intersection. A few things stood out in our conversation - Ethics isn't just about restrictions. It's about actively shaping the future we want. - The gap between tech innovators and regulators is closing. Slowly. - Our cultural narratives about AI often hinder rather than help. - Bias in AI is trickier than we think. We can't eliminate it entirely. The most important point? We need people who can think across disciplines. The big breakthroughs will happen at the intersections. If you're young and ambitious, don't just learn about building in AI. Learn how it connects to everything else. Links to episode in the comments

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As an investor in AI-powered hiring solutions, NYC's law mandating AI bias audits caught my attention. Here's why it matters for all of us in tech: While AI has revolutionized hiring by processing thousands of applications efficiently, we must acknowledge its potential dark side. My portfolio company has shown me firsthand how AI can streamline recruitment, but also taught me a crucial lesson: without proper oversight, AI can perpetuate and amplify existing biases. NYC's law requiring annual third-party bias audits is a step in the right direction, but it highlights a broader need across ALL AI applications. Think about it: 1. AI in lending decisions could discriminate based on historical patterns 2. Healthcare AI might provide different quality of care across demographics 3. AI-powered content recommendations could create echo chambers 4. Customer service AI could offer varying service levels based on profiles The implications? We need robust bias assessment frameworks not just for hiring, but across the AI ecosystem. I see a massive opportunity here: dedicated AI bias assessment services could become as fundamental as cybersecurity audits. To my fellow investors and entrepreneurs: this isn't just about compliance. It's about building AI that truly serves everyone. Companies that proactively address AI bias will win in the long run - both ethically and commercially. Would love to hear your thoughts: How is your organization ensuring AI fairness? What challenges are you facing in implementing bias controls? #ArtificialIntelligence #Ethics #Innovation #TechInvesting #AIBias #FutureTech Image speaks to how AI systems reflect our own biases while appearing objective, and the need for human oversight in maintaining fairness.

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Love love love this Pioneers of AI podcast conversation between Rana el Kaliouby, Ph.D. and Dr. Joy Buolamwini a scientist, poet, and digital activist. I am in awe of Dr. Joy and her impactful work to combat bias, inequity and injustice in AI. Going back a good decade, In the early days of Affectiva, we did a lot of work to ensure the ethical development and deployment of our AI, and we dove deep on how we should mitigate data and algorithmic bias. And, now with Blue Tulip Ventures, Rana and I are investing in human-centric AI ... AI that's in service of humans and with that we mean ALL humans. As Rana and Joy explore in their conversation, building AI the right way comes down to the people, processes and design decisions that are made every step along the way. To anyone building or deploying AI, listen to this conversation! https://lnkd.in/eHfPK5Aa

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And as a follow-up to my last post[1], since I mentioned Neuro-Symbolic AI... the 3rd Annual Neuro-Symbolic AI Summer School is coming up in September, 2024. The event is fully remote and participation is free. For more details, see: https://lu.ma/xxyof4bg [1]: https://lnkd.in/et8zeD2m #AI #ArtificialIntelligence #NeuroSymbolicAI

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Nearly half (if not more) of YC's F24 healthcare startups are building AI agent tools for doctors. Using agents to answer phones, write notes, prescreen patient intakes, etc. Sounds great right? Well, not exactly. That's because what YC and other investors are getting wrong about this generation of physicians versus those of the past (who did nothing to stop consolidation or electronic health records), is that they aren't just going to sit around and wait for someone else to build something for them anymore. They're builders who want to solve their own problems, and they're willing and eager to learn. Just look at telemedicine adoption data vs AI to explain what I mean: - Prior to COVID telemedicine was a <19% - During COVID it grew to > 90% - Post COVID it went back down to <40% But AI? - In Jan 24 it was 38% - Latest survey in Oct shows it >68% - By this time next year it should be near 100% This is the fastest adoption of any tech in healthcare in history, and it's only the beginning. Every day, new models, new features, and new tools are created for non-developers to build software using AI. What makes people believe that doctors can't or won't figure out how to use zero shot/few shot prompting in environments like cursor or claude just like everyone else? So if your bet is that AI doctors will make the same purchase decisions as their non-AI counterparts of the past, you're most likely making the wrong bet. This generation of doctor are builders. #HealthcareOnLinkedIn #AIagentsForDoctors

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As I look at startups and incumbents using LLMs / Generative AI in healthcare for provider workflows, I am trying to separate fact from hope, and current reality vs. the likely progress in the next 1 - 2 years. Wondering if there is anything in the "it's real now" bucket or in the "it seems very likely in the near future" one. 3 basic questions I am hoping to be educated on - if you have a strong POV, I'd love to learn from you: 1. Ambient listening: Given Nuance knows a thing or two about dictation and speech, and Msft about LLMs, what is the real angle for new players? Is it fewer hallucinations? More accuracy? More actionable guidance based on proprietary medical knowledge? I am assuming everything needs to go back into the EHR so why is this not simply Epic + Msft wins? I get the stuff about elephants not dancing and such but still...? 2. Clinical Diagnoses: Is this for real? Will we really see a day that my historical and real time medical record, (not just the stuff I see on Apple Health) can be fed into a fine-tuned model with all the RAG, RLHF bells and whistles, and out will pop helpful guidance for medication or other meaningful interventions I should take? And for all the talk of "provider in the loop / AI is an assistant not the agent" talk, is there a real example of this capability saving time / $$ / creating access? (Outside of radiology - I get how images are a well solved problem) 3. Workflow integration: Related to 2. above, has anyone seen a real LLM use case integrated into nursing / clinician / other provider workflow in a useful way that doesn't make them use multiple screens, create new and often artificial alerts and triggers, and add burden as opposed to take it away? Above all, are hospitals and staff buying into / trusting any specific use cases? I'd love to see a case study or two of real ROI delivered with ecstatic users who would hate to lose the capability. This is revealing my ignorance, so please educate me on the reality and near term (1 - 2 year) potential, I get all the stuff about the long term promise. Thanks in advance!

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Unaligned #18: The Education Accelerant Ross Cohen is working with teachers, students, and parents all over the world to bring AI into the classroom. His initiative/tool, “Tech in Schools” enables teachers to build lesson plans and radically improve their classrooms with a variety of different AI models. Here we have a wide-ranging conversation about how education is starting to change due to AI and how he’s seeing the challenges of new kinds of student cheating, to the opportunities of new kinds of projects thanks to the new Large Language Models that are shipping literally every day. For more about Tech in Schools, see https://getmytsi.org

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Access to #AI tools and literacy has not been equitable. "While many workers are independently exploring and mastering generative AI tools like ChatGPT, self-driven experimentation is not going to solve the AI literacy challenge. Studies show that AI use is already divided by historic indicators of marginalization, like income and college attainment. When individuals are tasked with finding the time and money to invest in upskilling or reskilling, too many are left behind." https://lnkd.in/gmqxxbqb

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Disagree with Gary Marcus The nature of the development of these models is one of punctuated steps. Between generations, we should only expect increments. And that is what we have seen so far—increments which has still yielded measurable improvements in capabilities, speed and cost. The real test will be that next generation of models. It was 3 years from GPT3 to GPT4. It hasn’t been 18 months since GPT4. So let’s avoid the sleight of hand when analysing these technologies. #ai

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What do you think about AI in senior care? Personally, I think we have to be careful with it. When AI presents itself as human, it can be confusing for seniors who may not have strong digital literacy skills. In fact, if they think they're talking to a person and not an AI chatbot, it can be dangerous if they're having a medical emergency or issue. However, there are some helpful use cases. I think Kristen Fischer's exploration of AI here is really interesting. "Artificial intelligence may be a useful communication tool to help older adults with cancer communicate with their doctors and have more of a say in their treatment," she says. AI can provide more context or explanation about certain treatment plans and translate the patient’s needs to clinicians in situations where the patient doesn't feel understood or heard. AI will never replace professionals, but it could help patients and clinicians better understand each other. Feel free to share your thoughts on this—I'd love to know if you think AI would be an effective tool for these situations. #AI #SeniorCare #FutureOfHealthcare

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