LIFE AI @LifeNetwork_AI

Bringing a single drug to market takes 12 to 15 years and costs up to $2.6 billion. Despite that investment, 90% of drug candidates that enter clinical trials never reach approval. The reason is not that the science is wrong. It is that the process was never designed to learn continuously. The drug development lifecycle has 7 stages: 1. Target Discovery 2. Drug Discovery 3. Lead Optimization & Candidate Selection 4. Preclinical Research 5. Clinical Trials 6. Regulatory Review 7. Post-Market Monitoring & Patient Access Every stage runs sequentially. Every handoff introduces delay. Every program builds its own evidence infrastructure from scratch. And the real-world signal generated in stage 7 - how a drug actually performs in real patients never flows back to inform the decisions made in stages 1 through 3. That missing feedback loop is where most of the time, cost, and failure lives.

Everything in life comes back to health.

Weekend prescription from Life AI 💚 ✓ Get some sunlight ☀️ ✓ Take a walk 🚶 ✓ Touch grass 🌱 Sometimes the most powerful health upgrade is completely free.

** NVIDIA GTC Taipei: Memorable experience ** I've passed through Taiwan in transit plenty of times, but this was my first time actually staying here. Taipei is a beautiful city. On the ride from the airport, my Uber driver asked if I was here for the NVIDIA conference. He'd doubled his shifts, he said, because so many people were flying in for NVIDIA's GTC, which runs alongside Computex this year. The whole city was buzzing. We're proud that Life AI was one of 15 startups picked to present on Startup Day. Robotics and Physical AI are everywhere right now. So is AI for Life Sciences. I had so many good conversations after our session. The question I kept getting was a simple one: why exactly is drug development so slow and expensive? Over the past year, more and more AI-led drug discovery companies, each raised hundreds of millions of dollars. It feels like the whole AI world is obsessed with drug discovery right now. And yes, drug development really is slow and expensive. Creating a promising drug candidate is hard. Proving it actually works in people is an order of magnitude harder. AI has largely cracked the first half, producing drug candidates at a fraction of the old cost. The second half, the clinical work that proves a drug actually works, is still painfully slow and expensive. That gap is exactly where we come in. AI now produces far more candidates than the industry can possibly develop, which leaves a growing pile of shelved, clinically-informed assets that nobody is taking forward. That's Life AI's wedge: asset rescue. We in-license these drug assets, finish the development in Asia at a fraction of the US cost, and share the upside. @IsomorphicLabs , the Google DeepMind spin-off, just closed a $2.1B round and is arguably leading the world in AI drug discovery. One day I'd love to sit down with their team and say: thank you, thank you for producing so many promising candidates. Life AI is here to rescue them.

LIFE AI @LifeNetwork_AI

a week ago

𝗡𝗩𝗜𝗗𝗜𝗔 𝗚𝗧𝗖 𝗧𝗮𝗶𝘄𝗮𝗻 𝟮𝟬𝟮𝟲 - 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 #𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲𝗔𝗜 𝗩𝗮𝗹𝘂𝗲 𝗖𝗵𝗮𝗶𝗻 At NVIDIA GTC Taiwan 2026, our Co-Founder & CEO Dr. Tuan Cao @tuan_lifeai presented “LifeAI Biohub: A Purpose-built AI platform for Drug Development” One signal

keyboard_arrow_left Previous keyboard_arrow_right Next

393 318 690 196K 21

If the problem is coordination across an entire healthcare value chain, the infrastructure has to be built for that from the ground up. That is what Life AI is. The shared infrastructure and coordination layer that connects pharma, hospitals, doctors, labs, regulators, auditors, and patients, with their interests aligned around each application's success. At NVIDIA GTC Taiwan 2026, our Co-Founder & CEO Dr. Tuan Cao took the stage to present exactly how Life AI is building that infrastructure and why it matters for the future of healthcare.

LIFE AI @LifeNetwork_AI

a week ago

𝗡𝗩𝗜𝗗𝗜𝗔 𝗚𝗧𝗖 𝗧𝗮𝗶𝘄𝗮𝗻 𝟮𝟬𝟮𝟲 - 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 #𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲𝗔𝗜 𝗩𝗮𝗹𝘂𝗲 𝗖𝗵𝗮𝗶𝗻 At NVIDIA GTC Taiwan 2026, our Co-Founder & CEO Dr. Tuan Cao @tuan_lifeai presented “LifeAI Biohub: A Purpose-built AI platform for Drug Development” One signal

keyboard_arrow_left Previous keyboard_arrow_right Next

393 318 690 196K 21

Building a better AI model will not fix healthcare. The real challenge is coordinating an entire value chain: pharma, hospitals, doctors, labs, regulators, auditors, and patients. Each with its own incentives, priorities, compliance obligations, and trust requirements. No single model can coordinate all of that. No single organization can either. Better healthcare comes from better infrastructure for the value chain. That is the problem Life AI was built to solve. #NVIDIAGTC #LifeAI #BioHub #HealthcareAI

LIFE AI @LifeNetwork_AI

a week ago

𝗡𝗩𝗜𝗗𝗜𝗔 𝗚𝗧𝗖 𝗧𝗮𝗶𝘄𝗮𝗻 𝟮𝟬𝟮𝟲 - 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 #𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲𝗔𝗜 𝗩𝗮𝗹𝘂𝗲 𝗖𝗵𝗮𝗶𝗻 At NVIDIA GTC Taiwan 2026, our Co-Founder & CEO Dr. Tuan Cao @tuan_lifeai presented “LifeAI Biohub: A Purpose-built AI platform for Drug Development” One signal

keyboard_arrow_left Previous keyboard_arrow_right Next

393 318 690 196K 21

Healthcare has AI in every vertical. AI doctors. AI diagnostics. AI copilots. AI imaging. AI drug discovery. AI trial matching. AI revenue cycle. Yet healthcare still looks mostly the same. Cancer is not yet cured. Drug prices are not yet lowered. Care is not yet personalized. AI is transforming every industry. Why not yet healthcare? That is the question Dr. Tuan Cao, Co-Founder & CEO of Life AI, posed at NVIDIA GTC Taiwan 2026 and the problem Life AI is building to solve.

LIFE AI @LifeNetwork_AI

a week ago

𝗡𝗩𝗜𝗗𝗜𝗔 𝗚𝗧𝗖 𝗧𝗮𝗶𝘄𝗮𝗻 𝟮𝟬𝟮𝟲 - 𝗠𝗮𝗽𝗽𝗶𝗻𝗴 𝘁𝗵𝗲 #𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲𝗔𝗜 𝗩𝗮𝗹𝘂𝗲 𝗖𝗵𝗮𝗶𝗻 At NVIDIA GTC Taiwan 2026, our Co-Founder & CEO Dr. Tuan Cao @tuan_lifeai presented “LifeAI Biohub: A Purpose-built AI platform for Drug Development” One signal

keyboard_arrow_left Previous keyboard_arrow_right Next

393 318 690 196K 21

** When you think of AI, think of humanity too. ** At the GStar AI & Humanity Summit, I was asked during our panel: what can AI do for humanity? It reminded me of a dinner I had with Thang Luong @lmthang, Director of Research at Google DeepMind. We were deep in the technical side, World Models for healthcare, AI in drug discovery, and we kept coming back to one simple fact: humans are still used as test subjects in drug development. As AI makes drug discovery faster and cheaper, more and more candidates will go into clinical trials. And those trials run on people. About 90% of drug candidates that reach clinical trials fail, so most of the time people take on real risk, including serious side effects, for a candidate that never makes it. So, what can AI do for humanity? Here's one thing I'd love to see: more AI companies building simulation environments that test drug candidates before they ever reach a real trial. Ideally, anything that passes the simulation would have a 99% chance of passing the actual one. It's a hard problem. Of course it is. But it's not so different from how engineers built flight simulators for pilots, which have probably saved thousands of lives. Fingers crossed that experts at Anthropic, OpenAI, and especially Google DeepMind solve this soon. I'd love to help. Tagging Jeff Dean @JeffDean , Quoc Le @quocleix , Thang Luong @lmthang , Ed Chi @edchi , Yi Tay @YiTayML .

LIFE AI @LifeNetwork_AI

a week ago

A great day at GStar Summit 2026: AI + Humanity. Our Co-Founder & CEO, Dr. Tuan Cao, joined the AI for Healthcare & Life Sciences session to discuss how AI can be applied responsibly in healthcare, where innovation must be matched with trust, safety, and real-world impact. A

keyboard_arrow_left Previous keyboard_arrow_right Next

40 128 224 11K 3

A great day at GStar Summit 2026: AI + Humanity. Our Co-Founder & CEO, Dr. Tuan Cao, joined the AI for Healthcare & Life Sciences session to discuss how AI can be applied responsibly in healthcare, where innovation must be matched with trust, safety, and real-world impact. A sincere thank you to the organizers, New Turing Institute and Pacific Gateway Partners, and to Google and FPT Corporation as strategic partners, for bringing together leaders, researchers, and innovators working at the intersection of AI and humanity. We’re grateful for the conversations, connections, and shared commitment to building AI that serves people and improves lives. @tuan_lifeai @newturing @lmthang

keyboard_arrow_left Previous keyboard_arrow_right Next

Welcome to a new month of better health, better habits, and a better life. One step at a time. 💚

GLife Catch it if you can 👇

AI learns. Humans decide. We didn’t build Life AI to replace clinicians. We built it to change what is possible for human health — infrastructure that coordinates across biology, institutions, and real human life. The AI is the coordination layer. The human is the reason it exists.

From individuals managing their health to governments building national programs. Life AI BioHub is the infrastructure layer for all of them. File that away.

The patient journey no longer starts at intake. It starts with search, chat, and AI-generated health information. Patients now use AI before they enter the healthcare system: to interpret symptoms to compare treatments to prepare questions to assess medication concerns to decide whether care is urgent to form expectations before the visit This changes the clinical encounter. Clinicians are no longer only responding to symptoms. They are also responding to AI-shaped beliefs, fears, and assumptions that formed before the appointment. That creates a new responsibility for healthcare systems. They need to understand the pre-visit AI layer. Ignoring it leaves a blind spot in patient trust, adherence, shared decision-making, and diagnosis timing. Healthcare AI strategy cannot stop at EHR workflows. It must account for how patients use AI before care begins.

AI in prior authorization is not a productivity issue. It is an access-to-care issue. When automation is used to review, delay, or deny care, the stakes are different from ordinary back-office AI. The system is no longer just processing paperwork. It is influencing whether patients receive treatment, how long they wait, and how much administrative burden is pushed onto clinicians and families. This is where healthcare AI needs a higher governance standard. Speed is not enough. AI used in prior authorization must be transparent, appealable, auditable, and clinically accountable. Health systems and regulators should ask: Who reviews adverse decisions? Can patients and clinicians understand why care was delayed? Are denial patterns monitored? Is there human oversight when medical necessity is involved? Can the system prove it improves efficiency without restricting appropriate care? AI that sits between a patient and treatment cannot be governed like ordinary automation. It must be governed as part of the care access infrastructure.

Preventive care does not fail because people dislike prevention. It fails because healthcare systems are built around episodes. A visit. A claim. A lab result. A discharge. A diagnosis. But health risk builds continuously. Sleep changes. Biomarkers drift. Behavior shifts. Medication adherence changes. Inflammation rises. Symptoms appear before diagnosis. Preventive healthcare needs infrastructure that can detect trajectory changes before they become clinical events. That requires longitudinal data, feedback loops, and earlier intervention pathways.

How many words can you find? 👀 Drop the first one you spot ⬇️

Healthcare AI adoption is moving faster than healthcare AI governance. That gap is not theoretical. If a hospital deploys generative AI inside the EHR, it also needs capacity to monitor: ☑️ accuracy ☑️ bias ☑️ clinical safety ☑️ workflow impact ☑️ model drift ☑️ failure modes ☑️ patient trust Healthcare AI cannot be treated like ordinary SaaS. Deployment is not the finish line. In healthcare, deployment is when the monitoring obligation begins.

Healthcare was built around sickcare. Life AI BioHub is built around lifecare. Real-world learning. Early intervention. Integrated biology. A coordination layer for human health.