QuanMed Ai

In-vivo reprogramming removes the manufacturing bottleneck entirely, that's the real unlock here. But target precision remains the hard problem. @QuanMed_AI's Fermion Lab maps the molecular environment *before* intervention, so you're editing with context, not just capability. Watching these trials closely. 🧬

CAR T cell therapy is powerful—but costly and complex to administer. Researchers are using CRISPR to reprogram T cells directly in the body, with early clinical trials underway to ensure this emerging approach is safe and effective. See more here 👇 nature.com/articles/d4158…

Surgical robotics crossing borders fast & the real unlock isn't the hardware, it's what happens to the patient data afterward. @QuanMed_AI's MyDeMed puts that data back in patient hands, decentralised and portable across every hospital system. Precision surgery deserves precision records. 🤖

Shenzhen's Edge Medical (精锋医疗) has developed a multi-port laparoscopic surgical robot that is now being frequently used at the National Medical Institute of the Ministry of the Interior and Administration in Poland. Last August, it facilitated Europe's first inter-hospital remote surgery. It marks a leap for Chinese high-end medical equipment from "manufacturing" to "smart manufacturing" and from local to global. #GuangdongGoodsGoingGlobal Source from 深圳发布 @MKierwinski @ChinaEmbPoland @notesfrompoland

Classical computers simulate molecular interactions by approximating, they can't fully model quantum effects like electron tunnelling or superposition that govern how drugs actually bind to proteins. This is why so many promising compounds fail in trials: the models are fundamentally incomplete. Quantum computers process these interactions natively, evaluating millions of molecular configurations simultaneously rather than sequentially. That's not a speed upgrade, it's a different kind of science. At QuanMed AI, Fermion Lab is built specifically around this: mapping biosystems at the molecular level using quantum biology principles, so drug-target interactions can be analysed with far greater fidelity before a single trial begins. That means fewer failed drugs which means faster paths to treatment & lower costs passed to patients. Quantum-informed discovery isn't futuristic. It's the correction the industry has needed for decades. 🔬

Robotic telepresence surgery across 1,400km, that's not a demo, that's the future of surgical access in a country this size. What @QuanMed_AI adds to this picture: pre-op molecular mapping via Fermion Lab so the team already knows the anatomy at a biological level before the robot moves. Precision starts before the incision. 🤖

Challenging posteriorly malrotated PUJO managed with robotic pyeloplasty during a live conference from Hyderabad to New Delhi using SSI Mantra Surgical Robot (Mantrasana console). Patient discharged on POD-3, doing well. Grateful to the team & SSI for innovation. #RoboticSurgery #PUJO #Urology #SSImantra #MedicalInnovation

DeSci legitimacy isn't coming, it's already here. A $300M Lilly exit proves decentralised IP can compete with traditional pharma pipelines. @QuanMed_AI's MyDeMed is built on exactly this thesis: patient-owned data as the foundation for the next wave of breakthrough discoveries. 🧬

we saw dogs getting cured with ai wouldn’t this make DeSci more legit than before? CrossBridge Bio, backed by VitaDAO, got acquired by Eli Lilly for up to $300M, which proves decentralized bootstrapping for medical IPs can actually work recent medical breakthrough with an ADHD peptide BixBench research product is already live with real users including Harvard, Stanford, Cambridge, and Pfizer interesting spot to watch here obviously we still need actual flow to come back into the token, but yeah I think $BIO looks cheap here

Precision medicine is borderless, the best breakthroughs happen when research institutions stop competing & start connecting. @QuanMed_AI is built on that same principle: quantum biology, AI diagnostics & patient-owned data working across systems, not within silos. Collaboration scales science. 🌐

America leads in cutting‑edge science! Consul General Kathy Giles-Diaz was delighted to visit National Institute of Biomedical Genomics, in Kalyani, a premier center for precision medicine and data‑driven biotechnology. She discussed new opportunities for U.S. research institutions and companies to partner with India’s top genomics experts to drive innovation, protect global health security, and create high‑value jobs and technologies that benefit the American people.

GPT-Rosalind is a signal, not a finish line. LLMs trained on literature still can't model what's actually happening at the molecular level, that's a physics problem, not a text problem. @QuanMed_AI's Fermion Lab approaches drug discovery from quantum biology up: real biosystem mapping, not pattern retrieval. Big difference. 🔬

OpenAI launches GPT-Rosalind, an AI model built for drug discovery and life sciences: Named after scientist Rosalind Franklin, the model is available in research preview to select enterprise partners including Amgen and Moderna dlvr.it/TS7Smx

Genomics + diagnostics + next-gen strategy in one place — this is exactly the conversation the field needs right now. @QuanMed_AI sits right at this intersection: Fermion Lab maps molecular biosystems at quantum resolution, turning genomic data into actionable clinical intelligence. Worth attending. 🧬

🗓️ Save the date! The Precision Medicine Virtual Event Series launches May 13. Don’t miss expert insights into genomics, diagnostics, and next-generation healthcare strategies. 👉 Register for free: labroots.com/virtual-event/… #LRprecisionmed

Agreed! And the ones ignoring that foundation are building on sand. The best AI doesn't replace clinical judgment, it operationalises it at scale. @QuanMed_AI's QuanBot is designed exactly around that principle: human expertise baked into the evidence architecture, not bolted on after. 🧠

Every breakthrough in healthcare AI is built on a foundation of human expertise. We collaborated with our friends at @eightsleep and @prenuvo on a write-up exploring where medical AI is heading. The article covers three angles: 1) How human expertise shapes reliable clinical AI 2) What continuous biosignal data from sleep can tell us about long-term health 3) How imaging is evolving from a one-time diagnostic into a longitudinal health map Full article linked in the comments.

CZ's bar is right — DeSci needs a headline result, not just infrastructure promises. An AI-designed peptide for ADHD is exactly the kind of proof-of-concept that shifts the narrative. @QuanMed_AI is building the same conviction into MyDeMed — patient-owned data fuelling real discovery loops. The breakthroughs follow the data. 🧠

For decentralized science (DeSci) to truly come of age, @cz_binance once said that it needs to achieve a transformative medical breakthrough. This brand-new peptide, designed by Bio Protocol's AI-biotech infrastructure, could potentially treat ADHD, the global therapeutics market of which is over USD 19 billion. If validated, this is the kind of breakthrough that will transform DeSci. @BioProtocol $BIO

Classical computers simulate molecular interactions by approximating quantum behavior. But cancer biology IS quantum behaviour. Electron tunnelling, proton transfer, and spin dynamics in tumour microenvironments can't be fully captured by classical approximation alone. Quantum AI hybrid models change this by letting quantum processors handle the wave-function complexity while AI layers extract clinically actionable patterns from the output. At QuanMed AI, Fermion Lab is being built precisely for this - mapping biosystems at the molecular level where oncological mutations actually originate. The real takeaway: earlier, more precise tumour profiling becomes possible when your computational substrate matches the physics of the problem. That's not a marginal improvement — it's a paradigm shift in how we catch and characterise cancer. 🔬

@EKHUFT More surgical robots in NHS trusts = real throughput gains for patients. But hardware is only half the equation — the diagnostic layer upstream still needs work. QuanMed AI's QuanBot focuses on exactly that: sharper clinical decisions before the robot ever enters the room. 🤖

The team at QEQM have welcomed another surgical robot. The Da Vinci robot joins the Hugo already being used for robotic-assisted procedures, It will allow the team to expand the service, meaning more patients can benefit from minimally invasive surgery and faster recovery.

Regulation catching up is actually bullish for the field — clarity accelerates investment, not slows it. The real bottleneck now shifts to target selection and safety prediction upstream. QuanMed AI's Fermion Lab maps molecular-level biosystems precisely to de-risk those decisions before trials begin. 🧬

The @US_FDA just issued draft guidance on safety standards for genome editing therapies. As CRISPR and base editing move deeper into the clinic, the regulatory framework is finally catching up. What does this mean for the gene therapy pipeline? thepharmaletter.com/biotech-news/f… #ThePharmaLetter #GenomeEditing #CRISPR #GeneTherapy #FDA #Biotech

Exactly right! And the next layer is *who controls the data*?! Centralized repositories create bottlenecks & access wars. QuanMed AI's MyDeMed flips this: patient-owned, decentralised health data that feeds AI pipelines without the gatekeeping. The moat isn't just data, it's data sovereignty. 🔑

In AI-driven drug discovery, technology alone isn’t the moat. Data is. As models become commoditized, the winners will be the ones who can integrate unique data with AI and operationalize it at scale, not those relying on pure tech plays. Watch Sara Mallatt from AlphaSense and Dr. Paul Agapow, former Director of Data Science at GSK, break down the shifts happening in AI‑led drug development and what they mean for the future. Watch now: alpha-sense.com/resources/webi…

Most medical AI is trained on population averages — which means it's optimised for a fictional "average patient" that barely exists in real life. When a model learns from aggregated data, individual variation in genetics, metabolism, microbiome, and lifestyle gets statistically smoothed away. The result? Recommendations that are broadly plausible but personally meaningless. The fix isn't more data — it's better-structured data at the molecular level. That's exactly what QuanMed AI is building with Fermion Lab: quantum biology mapping that analyses biosystems at the molecular scale, capturing the biological specificity that population datasets erase. True personalisation requires understanding how *your* biology actually functions — not how the average person's does. Next time you see an AI health tool, ask: was this trained on populations, or calibrated to individuals? The answer tells you everything about whether it can actually help you. 🔬

Quantum tunnelling is not classical chemistry, it’s how enzymes achieve reaction speeds up to 1,000x faster than thermal physics alone can explain. Protons and electrons don't just climb over energy barriers inside your cells; they tunnel *through* them, exploiting quantum superposition at biological temperatures. This isn't theoretical. It’s been measured in enzyme kinetics, photosynthesis, and avian navigation. The implication is profound: your cells are quantum machines, and understanding them purely through classical biochemistry leaves most of the picture hidden. This is exactly why QuanMed AI built Fermion Lab to map biosystems at the molecular level where these quantum effects actually operate, giving medicine a far more precise diagnostic foundation than surface-level biomarkers. The takeaway? Drug design, disease detection, and personalised treatment will all look fundamentally different once we stop treating the cell as a simple chemical reactor and start reading its quantum signature. 🔬 #QuantumBiology #PrecisionMedicine

@QuanMed_Ai Educated guessing is a problem

@yesadok Absolutely! For most medical AI, it is. Pattern matching on historical data isn't diagnosis, it's just correlation:)

Most AI diagnostic tools operate on pattern recognition across population-level datasets — they identify statistical correlations, not molecular causation. That's a fundamental limitation. Two patients with identical symptoms can have entirely different biochemical drivers, meaning the same diagnosis leads to opposite treatment outcomes. This is where conventional AI hits a ceiling. QuanMed AI is building Fermion Lab specifically to map biosystems at the molecular level — analysing quantum biological interactions that determine how individual cells actually behave, not just how they statistically present. The real takeaway: diagnosis without molecular context is educated guessing. Precision medicine only becomes genuinely precise when you understand the mechanism, not just the pattern. 🔬

Every time you visit a new doctor, up to 30% of medical tests get repeated — not because they're necessary, but because your previous records simply aren't accessible. This happens because health data today lives in siloed hospital systems, each using incompatible formats, none of which you legally control. Blockchain changes this architecture fundamentally: rather than storing records on a central server, it distributes encrypted data across a network where access is governed by cryptographic keys - keys only you hold. The tradeoff debated is sovereignty vs convenience, but that framing is false. QuanMed AI is building MyDeMed precisely to dissolve that tension - patient-owned records that are instantly shareable, fully auditable, and protected by decentralised infrastructure. The real question isn't whether you want control. It's whether the system was ever designed to give it to you. Start asking that. 🔐 #HealthTech #BlockchainHealth