Technology

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

In January, everyone signs up for the gym, but you're not going to run a marathon in two or three months. The same applies to AI adoption. I've been watching enterprises rush into AI transformations, desperate not to be left behind. Board members demanding AI initiatives, executives asking for strategies, everyone scrambling to deploy the shiniest new capabilities. But here's the uncomfortable truth I've learned from 13+ years deploying AI at scale: Without organizational maturity, AI strategy isn’t strategy — it’s sophisticated guesswork. Before I recommend a single AI initiative, I assess five critical dimensions: 1. 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲: Can your systems handle AI workloads? Or are you struggling with basic data connectivity? 2. 𝗗𝗮𝘁𝗮 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺: Is your data accessible? Or scattered across 76 different source systems? 3. 𝗧𝗮𝗹𝗲𝗻𝘁 𝗮𝘃𝗮𝗶𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Do you have the right people with capacity to focus? Or are your best people already spread across 14 other strategic priorities? 4. 𝗥𝗶𝘀𝗸 𝘁𝗼𝗹𝗲𝗿𝗮𝗻𝗰𝗲: Is your culture ready to experiment? Or is it still “measure three times, cut once”? 5. 𝗙𝘂𝗻𝗱𝗶𝗻𝗴 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁: Are you willing to invest not just in tools, but in the foundational capabilities needed for success? This maturity assessment directly informs which of five AI strategies you can realistically execute: - Efficiency-based - Effectiveness-based - Productivity-based - Growth-based - Expert-based Here's my approach that's worked across 39+ production deployments: Think big, start small, scale fast. Or more simply: 𝗖𝗿𝗮𝘄𝗹. 𝗪𝗮𝗹𝗸. 𝗥𝘂𝗻. The companies stuck in POC purgatory? They sprinted before they could stand. So remember: AI is a muscle that has to be developed. You don't go from couch to marathon in a month, and you don't go from legacy systems to enterprise-wide AI transformation overnight. What's your organization's AI fitness level? Are you crawling, walking, or ready to run?

What happens when you combine fluid mechanics, differential equations, and public restroom design? 🚾 You get... the world's first splash-free urinal. 🚽 I'm totally geeking out over the latest from researchers at the University of Waterloo and Weber State University who have cracked a century-old design problem: urinal splashback. By solving the "isogonal curve problem" and using some seriously impressive math, the team created two new urinal designs — the Cornucopia and the Nautilus — that keep the urine stream under a critical impact angle (∼30°) to virtually eliminate splash. ✅ 95% less splash than modern urinals ✅ Better hygiene and accessibility ✅ Saves millions of liters of cleaning water daily ✅ And yes... a cleaner experience for everyone involved Even better: they also invented the "urine-no" — a hostile anti-urination wall that maximizes splashback to deter public urination. (Talk about fluid dynamics weaponized 😂.) The moral of the story? Sometimes the solutions to big problems (like global sanitation and water conservation) start by rethinking the "small" stuff — with a little physics and a lot of creativity. 🔗 Full (and fascinating) paper in the first comment below 👇 #Engineering #Water #Wastewater #innovation

As technology becomes the backbone of modern business, understanding cybersecurity fundamentals has shifted from a specialized skill to a critical competency for all IT professionals. Here’s an overview of the critical areas IT professionals need to master:  Phishing Attacks   - What it is: Deceptive emails designed to trick users into sharing sensitive information or downloading malicious files.   - Why it matters: Phishing accounts for over 90% of cyberattacks globally.   - How to prevent it: Implement email filtering, educate users, and enforce multi-factor authentication (MFA).  Ransomware   - What it is: Malware that encrypts data and demands payment for its release.   - Why it matters: The average ransomware attack costs organizations millions in downtime and recovery.   - How to prevent it: Regular backups, endpoint protection, and a robust incident response plan.  Denial-of-Service (DoS) Attacks   - What it is: Overwhelming systems with traffic to disrupt service availability.   - Why it matters: DoS attacks can cripple mission-critical systems.   - How to prevent it: Use load balancers, rate limiting, and cloud-based mitigation solutions.  Man-in-the-Middle (MitM) Attacks   - What it is: Interception and manipulation of data between two parties.   - Why it matters: These attacks compromise data confidentiality and integrity.   - How to prevent it: Use end-to-end encryption and secure protocols like HTTPS.  SQL Injection   - What it is: Exploitation of database vulnerabilities to gain unauthorized access or manipulate data.   - Why it matters: It’s one of the most common web application vulnerabilities.   - How to prevent it: Validate input and use parameterized queries.  Cross-Site Scripting (XSS)   - What it is: Injection of malicious scripts into web applications to execute on users’ browsers.   - Why it matters: XSS compromises user sessions and data.   - How to prevent it: Sanitize user inputs and use content security policies (CSP).  Zero-Day Exploits   - What it is: Attacks that exploit unknown or unpatched vulnerabilities.   - Why it matters: These attacks are highly targeted and difficult to detect.   - How to prevent it: Regular patching and leveraging threat intelligence tools.  DNS Spoofing   - What it is: Manipulating DNS records to redirect users to malicious sites.   - Why it matters: It compromises user trust and security.   - How to prevent it: Use DNSSEC (Domain Name System Security Extensions) and monitor DNS traffic.  Why Mastering Cybersecurity Matters   - Risk Mitigation: Proactive knowledge minimizes exposure to threats.   - Organizational Resilience: Strong security measures ensure business continuity.   - Stakeholder Trust: Protecting digital assets fosters confidence among customers and partners.  The cybersecurity landscape evolves rapidly. Staying ahead requires regular training, and keeping pace with the latest trends and technologies.  

Using light as a neural network, as this viral video depicts, is actually closer than you think. In 5-10yrs, we could have matrix multiplications in constant time O(1) with 95% less energy. This is the next era of Moore's Law. Let's talk about Silicon Photonics... The core concept: Replace electrical signals with photons. While current processors push electrons through metal pathways, photonic systems use light beams, operating at fundamentally higher speeds (electronic signals in copper are 3x slower) with minimal heat generation. It's way faster. While traditional chips operate at 3-5 GHz, photonic devices can achieve >100 GHz switching speeds. Current interconnects max out at ~100 Gb/s. Photonic links have demonstrated 2+ Tb/s on a single channel. A single optical path can carry 64+ signals. It's way more energy efficient. Current chip-to-chip communication costs ~1-10pJ/bit. Photonic interconnects demonstrate 0.01-0.1pJ/bit. For data centers processing exabytes, this 200x improvement means the difference between megawatt and kilowatt power requirements. The AI acceleration potential is revolutionary. Matrix operations, fundamental to deep learning, become near-instantaneous: Traditional chips: O(n²) operations. Photonic chips: O(1) - parallel processing through optical interference. 1000×1000 matmuls in picoseconds. Where are we today? Real products are shipping: — Intel's 400G transceivers use silicon photonics. — Ayar Labs demonstrates 2Tb/s chip-to-chip links with AMD EPYC processors. Performance scales with wavelength count, not just frequency like traditional electronics. The manufacturing challenges are immense. — Current yield is ~30%. Silicon's terrible at emitting light and bonding III-V materials to it lowers yield — Temp control is a barrier. A 1°C change shifts frequencies by ~10GHz. — Cost/device is $1000s To reach mass production we need: 90%+ yield rates, sub-$100 per device costs, automated testing solutions, and reliable packaging techniques. Current packaging alone can cost more than the chip itself. We're 5+ years from hitting these targets. Companies to watch: ASML (manufacturing), Intel (data center), Lightmatter (AI), Ayar Labs (chip interconnects). The technology requires major investment, but the potential returns are enormous as we hit traditional electronics' physical limits.

Gone are the days when the only way to know something was wrong with your machinery was the ominous clunking sound it made, or the smoke signals it sent up as a distress signal. In the traditional world of maintenance, these were the equivalent of a machine's cry for help, often leading to a mad dash of troubleshooting and repair, usually at the most inconvenient times. Today, we're witnessing a seismic shift in how maintenance is approached, thanks to the advent of Industry 4.0 technologies. This new era is characterized by a move from the reactive "𝐈𝐟 𝐢𝐭 𝐚𝐢𝐧'𝐭 𝐛𝐫𝐨𝐤𝐞, 𝐝𝐨𝐧'𝐭 𝐟𝐢𝐱 𝐢𝐭"  philosophy to a proactive "𝐋𝐞𝐭'𝐬 𝐟𝐢𝐱 𝐢𝐭 𝐛𝐞𝐟𝐨𝐫𝐞 𝐢𝐭 𝐛𝐫𝐞𝐚𝐤𝐬" mindset. This transformation is powered by a suite of digital tools that are changing the game for industries worldwide. 𝐓𝐡𝐫𝐞𝐞 𝐍𝐮𝐠𝐠𝐞𝐭𝐬 𝐨𝐟 𝐖𝐢𝐬𝐝𝐨𝐦 𝐟𝐨𝐫 𝐄𝐦𝐛𝐫𝐚𝐜𝐢𝐧𝐠 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞: 𝟏. 𝐌𝐚𝐤𝐞 𝐅𝐫𝐢𝐞𝐧𝐝𝐬 𝐰𝐢𝐭𝐡 𝐈𝐨𝐓 By outfitting your equipment with IoT sensors, you're essentially giving your machines a voice. These sensors can monitor everything from temperature fluctuations to vibration levels, providing a continuous stream of data that can be analyzed to predict potential issues before they escalate into major problems. It's like social networking for machines, where every post and status update helps you keep your operations running smoothly. 𝟐. 𝐓𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐞 𝐂𝐫𝐲𝐬𝐭𝐚𝐥 𝐁𝐚𝐥𝐥 𝐨𝐟 𝐀𝐈 By feeding the data collected from IoT sensors into AI algorithms, you can uncover patterns and predict failures before they happen. AI acts as the wise sage that reads tea leaves in the form of data points, offering insights that can guide your maintenance decisions. It's like having a fortune teller on your payroll, but instead of predicting vague life events, it provides specific insights on when to service your equipment. 𝟑. 𝐒𝐭𝐞𝐩 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐅𝐮𝐭𝐮𝐫𝐞 𝐰𝐢𝐭𝐡 𝐌𝐢𝐱𝐞𝐝 𝐑𝐞𝐚𝐥𝐢𝐭𝐲 Using devices like the Microsoft HoloLens, technicians can see overlays of digital information on the physical machinery they're working on. This can include everything from step-by-step repair instructions to real-time data visualizations. It's like giving your maintenance team superhero goggles that provide them with x-ray vision and super intelligence, making them more efficient and reducing the risk of errors. ******************************************** • Follow #JeffWinterInsights to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

$1.3B vs $14B+. That's the stark difference in VC funding between Atlanta and other tier-2 tech hubs in 2023. Despite our Fortune 500 presence, diverse talent pool, and lower cost of living, Atlanta is falling behind cities like Austin, Boston, and Miami. This was no clearer than at this year’s Venture Atlanta Conference Alumni Dinner earlier this month. The event was great and full of 250+ investors and founders, but I could count the number of Black folks on two hands. Coincidentally, Goodie Nation was hosting its Happy Hour, which was full of founders from all backgrounds just a mile away. This contrast perfectly captures Atlanta's challenges and opportunities. We have amazing, diverse communities that often operate in separate worlds. From my perspective, here are 3 changes that could transform Atlanta into the tech hub for ALL founders: 1. Embrace the city’s diversity     Atlanta will never be San Francisco or New York, and that’s fine. Atlanta's cultural diversity is its superpower – we should be positioning ourselves as America's most inclusive tech ecosystem. Not the city for tech bros (Miami) or for young founders who can share a 200 square ft. apartment while they build (SF and/or NYC)     2. Break down community silos       Our tech communities (Goodie Nation, Atlanta Tech Village, ATDC, etc.) are strong individually but isolated. It’d be cool to see more collaboration between the communities so founders can connect more with folks outside their normal workplaces.     3. Expand access to early-stage funding     I wrote about this last month, but Atlanta VCs’ requirements are too strict for early-stage startups. $250k ARR for a pre-seed company will just push founders to build their companies in traditional hubs instead of here. We need more accessible funding opportunities to attract and retain innovative founders. This is clearly much easier said than done. The city is making great progress toward establishing itself as a tech hub, but there’s more work to do. For the Atlanta founders, investors, or others in the ecosystem, what do you think? What other changes does Atlanta need to compete with emerging tech hubs? What did I miss? - - - If you enjoyed this post, I wrote in detail about Atlanta’s potential as a tech hub in the first Equity Shift newsletter. It goes out today at 12pm ET. You can subscribe and check it out here 👉🏾 https://lnkd.in/dYFy-XTg

Powering Cities with Every Step: Japan’s Smart Energy Innovation ⚡🚶♂️ What if your daily walk could help power your city? In Japan, it already does. Train stations, sidewalks, and bridges are being fitted with piezoelectric sensors—materials that generate electricity from movement. 🔹 How It Works – Every footstep applies pressure, creating a tiny electric charge. Multiply that by thousands of daily commuters, and it’s enough to power LED screens, lights, and signage. 🔹 Real-World Impact – Tokyo train stations track how much energy passengers generate, turning commutes into a live science experiment. Bridges capture vibrations from cars to power streetlights. 🔹 The Big Picture – While this won’t replace traditional energy sources, it’s a step toward greener, self-sustaining infrastructure. 💡 Could this technology be scaled for more cities? Where else could we harvest untapped energy? Let’s discuss! 👇 #Innovation #SustainableEnergy #SmartCities #GreenTech #FutureInfrastructure

“I am CONCERNED about this patient.” 🔔🔔🔔 When I hear this comment from one of our experienced and knowledgeable Neonatal ICU nurses or NNPs, my ears perk up and I quickly move to the bedside to gather more information; vitals, labs, exam, images etc.. Critical care nursing INTUITION is real. Critical care nursing INTUITION is really valuable as insightful and as a warning system.  But we as busy, and sometimes aloof or deaf physicians, often do not take the time to listen, evaluate, and act on a nurses' intuition.  A new Nature Portfolio Medicine trial by Sarah Rossetti, RN, PhD, Kenrick D Cato (He, Him) PhD, RN, CPHIMS, FAAN, FACMI et al. from NewYork-Presbyterian Hospital, Brigham and Women's Hospital, University of Pennsylvania, and Children's Hospital of Philadelphia (https://lnkd.in/g2swDpGB) puts tangible data behind nursing intuition and concern. By harnessing real‑time (hourly) nursing documentation patterns using a machine‑learning model and natural language processing to screen nursing documentation for "concerns"—the authors created the CONCERN Early Warning System—that: 🟢 Reduced in‑hospital mortality by 36% 🟢 Shortened length of stay by 11 % 🟢 Decreased sepsis risk by 7% 🟢 Increased timely ICU transfers by 25 % This isn’t just another algorithm—it’s a nurse‑centered alert that recognizes and elevates the “gut instinct” nurses have relied on for decades. They used a unique and intriguing approach by identifying when an increased nursing frequency and unusual timing of documentation occurred (more frequent than standard etc.) or a missed medication--- as they may signs that a patients clinical status is concerning or worsening. In the current wave of AI, not a lot has been focused on improving nursing capabilities and documentation burden. 👏🏽 It is refreshing and important to see a study such as this one that puts nurses and patients at the center.👏🏽 🧏🏽♂️ We should learn to listen, to study, and to integrate nursing intuition into our care pathways—not as a checkbox, but as a trusted signal that drives earlier, life‑saving interventions. Nurses spend the most time with patients, every shift, in every hospital in the country, and they usually “know” when there is something different with their patient, but they do not always know why or what.  They just "feel something is off." Somethings are difficult to explain in words, but it may be easier and better to explain with computer models that can make the "subjective feeling" into something more objective, tangible, and actionable. I’m excited to see if  more hospitals could adopt CONCERN‑style tools,  partner with our nursing colleagues, and evaluate such models to refine how we capture invaluable nursing expertise. How have you or your team operationalized nursing intuition in your unit? Sara Deakyne Davies, CT Lin MD, FACP, FAMIA, George Ferzli, MD, MBOE, EMBA, Lindsey Knake, Brynne Sullivan   #UsingWhatWeHaveBetter

AI’s ability to make tasks not just cheaper, but also faster, is underrated in its importance in creating business value. For the task of writing code, AI is a game-changer. It takes so much less effort — and is so much cheaper — to write software with AI assistance than without. But beyond reducing the cost of writing software, AI is shortening the time from idea to working prototype, and the ability to test ideas faster is changing how teams explore and invent. When you can test 20 ideas per month, it dramatically changes what you can do compared to testing 1 idea per month. This is a benefit that comes from AI-enabled speed rather than AI-enabled cost reduction. That AI-enabled automation can reduce costs is well understood. For example, providing automated customer service is cheaper than operating human-staffed call centers. Many businesses are more willing to invest in growth than just in cost savings; and, when a task becomes cheaper, some businesses will do a lot more of it, thus creating growth. But another recipe for growth is underrated: Making certain tasks much faster (whether or not they also become cheaper) can create significant new value. I see this pattern across more and more businesses. Consider the following scenarios: - If a lender can approve loans in minutes using AI, rather than days waiting for a human to review them, this creates more borrowing opportunities (and also lets the lender deploy its capital faster). Even if human-in-the-loop review is needed, using AI to get the most important information to the reviewer might speed things up. - If an academic institution gives homework feedback to students in minutes (via autograding) rather than days (via human grading), the rapid feedback facilitates better learning. - If an online seller can approve purchases faster, this can lead to more sales. For example, many platforms that accept online ad purchases have an approval process that can take hours or days; if approvals can be done faster, they can earn revenue faster. This also enables customers to test ideas faster. - If a company’s sales department can prioritize leads and respond to prospective customers in minutes or hours rather than days — closer to when the customers’ buying intent first led them to contact the company — sales representatives might close more deals. Likewise, a business that can respond more quickly to requests for proposals may win more deals. I’ve written previously about looking at the tasks a company does to explore where AI can help. Many teams already do this with an eye toward making tasks cheaper, either to save costs or to do those tasks many more times. If you’re doing this exercise, consider also whether AI can significantly speed up certain tasks. One place to examine is the sequence of tasks on the path to earning revenue. If some of the steps can be sped up, perhaps this can help revenue growth. [Edited for length; full text: https://lnkd.in/gBCc2FTn ]