Rebecca Jennerjahn’s Post

Physicians juggle 5+ tools just to make one decision. Search evidence in one tab. Check drug interactions in another. Run the differential somewhere else. Code the visit in a fourth. Repeat, all day, every patient. Introducing Celiogen - an agentic AI workstation built to be the only tool open on a physician's desktop. Powered by Orinn-1.7, our state-of-the-art medical reasoning model, Celiogen reasons through evidence, differentials, drug decisions, and coding - as one connected system, not four disconnected ones. Built with a simple rule: your patient data is never retained. Not stored, not logged, not used to train anything. Ever. Launching early next week. We're opening registration for a limited early access, free credits, and a direct line to shape what we build next. $400 in credits for every physician who pre-registers early - link in the comments. #Celiogen #AgenticAI #PhysicianTools #ClinicalAI #HealthTech

5 tools. 1 decision. Every patient, all day ~ Physician daily pain. Search evidence in one tab. Check drug interactions in another. Run the differential somewhere else. Code the visit in a fourth. That's the reality for most physicians right now. So we built Celiogen, an agentic AI workstation designed to be the only tool open on a physician's desktop. Powered by Orinn, our medical reasoning model, it connects evidence, differentials, drug decisions, and coding into one system. And your patient data is never stored, logged, or used to train anything. Ever. $400 in free credits for physicians who pre-register early. Limited early access opens next week, link in comments.

Few months back, a physician told me: "I spend more time switching tools than thinking about the patient in front of me." That's why we built Celiogen. Not another AI search bar - one agentic system, powered by our own reasoning model Orinn, that handles evidence, differentials, drug decisions, and coding together. And your patient data is never retained. Period. Early access opens next week - and get $400 in credits to do it. Special thanks to Nader Absi, MD Dr.Sarath Kumar Vikash Rustagi, MD, FRCR(UK)

𝐀𝐈 𝐢𝐬 𝐭𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐢𝐧𝐠 𝐦𝐞𝐝𝐢𝐜𝐚𝐥 𝐥𝐚𝐛 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 𝐨𝐧 𝐭𝐡𝐞 𝐒𝐩𝐚𝐜𝐞 𝐂𝐨𝐚𝐬𝐭 🚀 Experts across the Space Coast say AI is moving from pilot to practice in medical labs. The promise is faster, clearer results for clinicians and patients. Roughly 70% of clinical decisions rely on lab data, according to industry analyses, so smarter workflows can ripple across entire care pathways. • Priority alerts: models flag critical values and route them to the right clinician. • Plain-language summaries: patients get understandable explanations with next steps. • Quality control: algorithms spot instrument drift and sample mix-ups. • Interoperability: the hurdle is clean data across LIS and EHR systems. • Guardrails: human-in-the-loop review, audit logs, and clear escalation paths. In a rollout I supported at a midsize hospital, AI result summaries cut after-hours call backs and surfaced two urgent potassium alerts sooner. It also stumbled on a rare endocrine panel until we tightened reference ranges and added expert review. If you work in lab medicine or on the Space Coast, what safeguards should labs prioritize as they scale these tools?

When the clinical model becomes a free download, the moat stops being the model. MedGemma, BioNeMo and the wave of open medical models are quietly resetting the board. For years the implicit assumption was that the hard-won asset was the trained model itself. That's eroding. If a capable clinical-grade model is something any team can pull down and fine-tune, then owning "a good model" is no longer a defensible position. So where does the advantage move? Toward the unglamorous layer. Proprietary, well-labelled clinical data. Integration into the actual workflow where a decision gets made. Validation evidence a hospital committee will accept. The trust and distribution to get through procurement at all. I think this is healthy. Open models lower the floor and let smaller teams build things that used to require a lab. But they also expose who was selling the wrapper versus who was solving the problem. The winners won't be whoever has the biggest model. It'll be whoever earns the right to sit inside the clinical decision. If the model is commoditized, what's your actual moat? Views are my own and personal. #HealthcareAI #OpenSource #ClinicalAI

Scaling a healthcare technology company out of university laboratories requires a deep commitment to everyday patient care. 🎓 Theoretical software code means nothing if it cannot handle the intense daily pressure of an active hospital diagnostic department facing massive patient backlogs. MyCardium AI was founded by active clinical specialists from University College London and Barts Health NHS Trust. We emerged from a collective determination to eliminate multi-reader measurement variance across frontline hospital imaging pipelines. Our journey from academic research units to an international enterprise reflects years of strict clinical validation on the ground. Connect with our clinical and commercial partnership teams to explore our diagnostic heritage: 🔗 mycardium.com/contact 📧 partnership@mycardium.com William Simner James Moon Mark Westwood Ed C. Charlotte Manisty Antony Shimmin John Ahmed Follow MyCardium AI to track how active clinical expertise is standardising global hospital datasets. #MyCardium #UCL #ClinicalWorkflow #PracticeOptimisation #Cardiovascular

As someone passionate about innovation and the impact technology can have on people’s lives, I find it truly inspiring to see how our solutions can contribute to shaping the future of Life Sciences and healthcare. 🧬💡 🚀 What if we could transform the way clinical trials are designed, conducted, and ultimately improve patient care? I’m excited to share this inspiring video showcasing a vision of the clinical trial of the future - powered by virtual twins, multimodal imaging, real-world sensor data, digital biomarkers, and AI. Andrea Falkoff, VP of Product Management at Medidata Solutions, shares how innovations such as automated oncology workflows, external control arms, and radiomics-based forecasts could help anticipate treatment response and patient outcomes. Melissa Ceruolo, VP of Engineering & Biomarker Analytics, takes this vision further with digital biomarkers and wearable-based insights, demonstrating how short monitoring windows can generate powerful predictive signals. What particularly resonates with me is the vision of bringing together multimodal data, semantic knowledge graphs, and clinically trained AI agents to enable more continuous and adaptive clinical trials. 🌍📈 This is a powerful example of how technology, data, and AI can come together to drive meaningful progress in Life Sciences — and, ultimately, make a positive impact on patients and healthcare systems. ▶️ I invite you to watch the full video and discover how virtual twins and other innovations are reshaping the future of clinical trials and patient care. http://go.3ds.com/djNC #LifeSciences #HealthcareInnovation #ClinicalTrials #VirtualTwins #AI #DigitalHealth #Innovation

Anthropic Tests Penlight Prototype for Real-Time Clinical Assistance 📌 Anthropic is reportedly developing Penlight, a specialized clinical prototype designed to revolutionize patient consultations through ambient transcription and real-time medical research. By integrating live audio processing with direct PubMed grounding, the tool aims to provide physicians with instant, evidence-based decision support during live sessions. This shift suggests Anthropic is moving beyond general AI toward building dedicated, workflow-integrated applications for the healthcare industry. 🔗 Read more: https://lnkd.in/dbWCkUJZ #Anthropic #Penlight #Claude #Clinicalassistance #Ambienttranscription

A structured digital workflow means fewer errors and better results for both clinic and lab. In this lecture, DT Sophie Dallem and DT George Zacusai show how precise data collection and smart planning in ExoCAD reduce adjustments and improve predictability from the start. Discover how micro cut back and CERABIEN MiLai support natural aesthetics, efficiency, and cost control. Align precision with productivity. Watch now. https://lnkd.in/ez6dYunk

Every day, thousands of patients slip through the cracks between appointments — the follow-up call that never happens because there aren't enough hands. María González Manso built an AI that calls every one of them. A biomedical engineer, she went from ultrasound research in Brazil to marketing at Medtronic — circling one question: how do you care for more people without burning out the people who do the caring? In 2019 she co-founded Tucuvi and built LOLA — a CE-marked medical device, not a chatbot, that follows up with patients by voice and flags the ones who need a human, fast. Today LOLA supports 60+ healthcare organizations, backed by a $20M Series A and a spot on Forbes 30 Under 30 Europe. "Healthcare is under immense pressure, and incremental tools are no longer enough," she says. She's not building a nicer waiting room. She's building capacity for a system running on empty. Congrats, María.