AI in Healthcare Innovation

In many parts of the world, the standard $20,000 fetal monitor simply isn't an option. This portable alternative uses AI to help health workers get accurate readings at a fraction of the cost—no specialized training required. It can spot warning signs like a slow fetal heartbeat in time to consult a specialist, begin treatment, or get a mother to a hospital. In maternal health, that can mean the difference between a dangerous complication and a healthy birth.

The Agentic Enterprise is driving profound change across every industry, but nowhere are the stakes higher than in healthcare. There is an incredible opportunity to elevate the work of healthcare professionals and deliver stronger care for patients around the world. In an essay for TIME, Murali Doraiswamy, professor of medicine at Duke University, and I discuss how AI is revolutionizing medicine, including: • Flagging subtle abnormalities in scans and slides that a human eye might miss. • Speeding up the discovery of drugs and drug targets. • Providing patients faster and more personalized support, from scheduling to flagging side effects But we’ve also seen that over-reliance on AI can lead to “deskilling” — in which medical professionals become less effective. That underscores the importance of approaches that keep humans at the center, such as the Intelligent Choice Architecture (ICA), where AI systems don’t make decisions but nudge providers to take a second look at results, weigh alternatives, and stay actively engaged in the process. The future of work is humans and AI agents working together. If we commit to designing systems that sharpen our abilities, we can combine the promise of AI with the critical thinking, compassion, and real-world judgment that only humans bring. https://lnkd.in/gqkTUfb6

AI just helped a couple get pregnant - after 19 years and 15 failed IVF cycles. The breakthrough came with an AI tool built by a team at Columbia University. It’s called STAR - the world’s first AI system trained to find sperm that embryologists can’t. The husband had azoospermia - a condition where no sperm is visible under the microscope. Dozens of attempts, surgeries, and even overseas experts had failed. But the team at Columbia didn’t give up. They spent 5 years building STAR (Sperm Track and Recovery). The system scans 8 million images per hour using a chip and computer vision, then gently isolates viable sperm missed by even the most experienced lab techs. And it worked. ▶︎ STAR found 44 sperm in a sample that had been manually searched for two full days. ▶︎ That one breakthrough led to a pregnancy that had felt impossible for nearly two decades. ▶︎ And it did so without chemicals, donor samples, or invasive extraction methods. For millions of couples dealing with infertility, this is a glimpse of what AI-assisted reproductive medicine could unlock. But more importantly - this shows us what AI in healthtech should be aiming for: Not just more data. Not just smarter models. But real clinical results that change lives. And as a healthtech investor, this is what I look for in AI-driven care: → A clear pain point → A targeted intervention → And a story no one can ignore What’s your take - could AI reshape fertility care the way it’s starting to reshape diagnostics and mental health? #entrepreneurship #healthtech #innovation

When L'Oréal uses AI to create new hair colors based on social media trends, they're in salons within weeks. Kraft Heinz—dead last in our study—still takes months to tweak a formula. After analyzing 26 major CPG companies at IMD's Center for Future Readiness, I discovered what separates winners from losers: The most future-ready companies treat consumer data like insider trading information. BACKGROUND: CPG in 2025 is brutal. Inflation persists. Gen-Z demands sustainability without premiums. Tariffs reshape supply chains daily. McKinsey & Company identified 150+ AI use cases for CPG transformation. Only 5 of 26 companies actually execute them. THE REVELATION: Coca-Cola didn't randomly launch Topo Chico Hard Seltzer. Their AI spotted the trend through social listening while competitors debated in boardrooms. By launch, they'd secured distribution nationwide. That's not innovation. That's prediction. What separates the top 5: L'Oréal (#1): 3.5% of sales to R&D. AI analyzes preferences real-time. Virtual try-on apps. Creates products from social trends. A 110-year company with startup velocity. The Coca-Cola Company (#2): Democratized AI internally. Every manager accesses demand forecasting. They analyze weather + social sentiment + sales simultaneously. These aren't tech companies selling beauty and beverages. They're prediction machines that happen to make products. THE WINNER'S FRAMEWORK: 1. AI at scale, not in pilots Winners integrate into workflows. Losers run demos. 2. Supply chains that anticipate Real-time visibility + AI forecasting = competitive firepower 3. D2C as intelligence goldmine 73% use multiple channels. Mine every interaction. 4. Disrupt yourself first Coca-Cola launched Costa Coffee, hard seltzers. Grew. Kraft Heinz protected legacy brands. Shrank. 5. Sustainable without premium Gen-Z spending hits $12T by 2030. They demand action at everyday prices. —— The inconvenient truth: Most CPG companies treat data like reporting instead of radar. Winners don't predict trends—they're already shipping products while competitors debate. Technological patience (knowing when to scale) + organizational agility (pivoting fast) = market domination. Three years from now, every CPG company operates like L'Oréal. Or they don't operate at all. P.S. Full Future Readiness Indicator here: https://bit.ly/3YTBzbX

Roche Receives First FDA Breakthrough Status for AI-Driven Companion Diagnostic in Lung Cancer >> 🔘 Roche’s VENTANA TROP2 device is the first AI-powered companion diagnostic (CDx) to receive FDA Breakthrough Device Designation for non-small cell lung cancer (NSCLC), combining immunohistochemistry (IHC), digital pathology, and AI to reach new levels of diagnostic precision 🔘 It uses a digital pathology algorithm (developed with AstraZeneca) to analyze whole-slide images and generate a quantitative TROP2 score, helping identify which patients might benefit from treatment 🔘 This AI-enhanced scoring could accelerate access to DATROWAY®, a TROP2-targeted antibody-drug conjugate (ADC) from AstraZeneca and Daiichi Sankyo, for patients with advanced NSCLC lacking actionable genomic alterations 🔘 The device incorporates Quantitative Continuous Scoring (QCS) to independently detect tumor cells and compute a Normalised Membrane Ratio (NMR), determining if a tumor is TROP2-positive 🔘 While AI handles the heavy-lifting, qualified pathologists still play a key role in reviewing staining, image quality, and providing clinical oversight 🔘 This is the first FDA Breakthrough designation granted to a computational pathology-based CDx, pointing to a future where AI and pathologists work hand-in-hand 👇 Source articles plus image credit in comments #DigitalHealth #AI #Pharma

The AI gave a clear diagnosis. The doctor trusted it. The only problem? The AI was wrong. A year ago, I was called in to consult for a global healthcare company. They had implemented an AI diagnostic system to help doctors analyze thousands of patient records rapidly. The promise? Faster disease detection, better healthcare. Then came the wake-up call. The AI flagged a case with a high probability of a rare autoimmune disorder. The doctor, trusting the system, recommended an aggressive treatment plan. But something felt off. When I was brought in to review, we discovered the AI had misinterpreted an MRI anomaly. The patient had an entirely different condition—one that didn’t require aggressive treatment. A near-miss that could have had serious consequences. As AI becomes more integrated into decision-making, here are three critical principles for responsible implementation: - Set Clear Boundaries Define where AI assistance ends and human decision-making begins. Establish accountability protocols to avoid blind trust. - Build Trust Gradually Start with low-risk implementations. Validate critical AI outputs with human intervention. Track and learn from every near-miss. - Keep Human Oversight AI should support experts, not replace them. Regular audits and feedback loops strengthen both efficiency and safety. At the end of the day, it’s not about choosing AI 𝘰𝘳 human expertise. It’s about building systems where both work together—responsibly. 💬 What’s your take on AI accountability? How are you building trust in it?

BREAKING! The FDA just released this draft guidance, titled Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations, that aims to provide industry and FDA staff with a Total Product Life Cycle (TPLC) approach for developing, validating, and maintaining AI-enabled medical devices. The guidance is important even in its draft stage in providing more detailed, AI-specific instructions on what regulators expect in marketing submissions; and how developers can control AI bias. What’s new in it? 1) It requests clear explanations of how and why AI is used within the device. 2) It requires sponsors to provide adequate instructions, warnings, and limitations so that users understand the model’s outputs and scope (e.g., whether further tests or clinical judgment are needed). 3) Encourages sponsors to follow standard risk-management procedures; and stresses that misunderstanding or incorrect interpretation of the AI’s output is a major risk factor. 4) Recommends analyzing performance across subgroups to detect potential AI bias (e.g., different performance in underrepresented demographics). 5) Recommends robust testing (e.g., sensitivity, specificity, AUC, PPV/NPV) on datasets that match the intended clinical conditions. 6) Recognizes that AI performance may drift (e.g., as clinical practice changes), therefore sponsors are advised to maintain ongoing monitoring, identify performance deterioration, and enact timely mitigations. 7) Discusses AI-specific security threats (e.g., data poisoning, model inversion/stealing, adversarial inputs) and encourages sponsors to adopt threat modeling and testing (fuzz testing, penetration testing). 8) And proposed for public-facing FDA summaries (e.g., 510(k) Summaries, De Novo decision summaries) to foster user trust and better understanding of the model’s capabilities and limits.

This is the first AI research that made my doctor friends text me like wild. Researchers asked: can ChatGPT diagnose patients better than doctors? And what if a doctor was using ChatGPT for help? Doctors with ChatGPT assistance scored 76% in diagnostic accuracy, barely above those without it (74%). ChatGPT alone nailed 90%. The study shares two challenges: 1️⃣ Overconfidence: Doctors often ignored ChatGPT’s correct diagnoses if they conflicted with their own. How can we get AI to explain the why and influence better without manipulating? 2️⃣ Underuse: Doctors are undertrained on AI and treated it like fancy Google (rather than copying and pasting the whole patient history in and “talking” to the data). AI could revolutionize diagnostics, but only if doctors learn to trust, verify, and utilize its capabilities. To doctors reading this, take a course on how to be an AI superuser—even if it’s not for the medical field. Mine is in my bio. You need to know what’s coming. Full study here: https://lnkd.in/eEHwsGzg

The NYT just reported that patients are uploading entire medical records into chatbots - but the risks are not what most people think. Patients are pasting labs, imaging, clinical notes, and oncology reports directly into LLMs. • 26-year-old told her labs “most likely” indicated a pituitary tumor. MRI: normal • 63-year-old advised to escalate to catheterization. Found ~85% LAD stenosis Because of how the chatbot responds, many assume the AI reasons about their symptoms and medical record the same way a clinician does. But AI systems are capable of both meaningful help and serious error, without any calibration signal visible to the user. Most worry about wrong AI recommendations. But the bigger risk is what the AI does not say. 📊 Harm preprint study A new Stanford-Harvard study (David Wu, MD, PhD, Fateme (Fatima) Nateghi, Adam Rodman, Jonathan H. Chen et al.) evaluated 31 models on 100 real outpatient eConsult cases across 10 specialties: - 4,249 management actions - 12,747 expert ratings Severe harm per 100 cases: - Best models: ~12–15 - Worst models: ~40 ~77% of severe harms were omissions: - Not ordering a critical test - Missing a needed referral - Neglecting follow-up suggestions 🔷 Additional findings: 1) Top models outperformed generalists using conventional resources (though these were difficult eConsult cases that PCPs were posing to specialists) 2) No link between safety and model size, recency, “reasoning modes,” or standard benchmarks 3) Multi-agent + RAG approaches reduced harm; heterogeneous ensembles had ~6× higher odds of top-quartile safety 📌 Implications When a patient asks AI for medical advice, the primary risk is not incorrect recommendations. It's neglecting critical actions a clinician might suggest (notably, humans also make a lot of mistakes). ⚠️ Why this matters 1) 2/3 of US physicians report using LLMs, and millions of patients. Errors will become more subtle as models get better. Both harms of omissions and commission will become harder for clinicians (and especially patients) to detect. 2) Sampling a few outputs is not enough: clinical AI evaluation needs explicit, systematic harm measurement on real cases, not just performance or accuracy on knowledge benchmarks. 3) If we don’t measure omission harms, we will systematically underestimate risk. 🔴 Open Call: State of Clinical AI Report (Jan 2026) The ARISE Network (Stanford + Harvard) is compiling a State of Clinical AI Report for 2026. Audience: health system leaders, clinicians, researchers, tech/pharma, media, investors 2025 peer reviewed and preprint studies within scope: • Clinical AI (doctor- or patient-facing) • Benchmarks, evaluations, real-world deployments, prospective trials • Workflow, outcomes, and implementation studies 📅 Submission deadline: Dec 21, 2025 - Comment with study link + 1–2 sentences on key findings and why it matters - We will follow up with a one-slide reference example for invited submissions

Today in Cell, we published new research showing how AI can help accelerate cancer discovery. With GigaTIME, we can now simulate spatial proteomics from routine pathology slides, enabling population-scale analysis of tumor microenvironments across dozens of cancer types and hundreds of subtypes.   Developed in partnership with Providence and the University of Washington, our hope is that this work helps scientists move faster from data to insight, revealing new links between genetic mutations, immune activity, and clinical outcomes, and ultimately improving health for people everywhere. https://lnkd.in/dSpPdtzz