Tommi Vaskivuo

Nina Capital is pleased to formally announce our investment in Datamonk. Datamonk has successfully raised pre-seed funding to address the significant challenge of migrating decades of medical #imaging #data, by developing an agentic AI-powered platform that automates migrations. The funding round was co-led by Healthy.Capital and Nina Capital, with participation from angel investors including Jeroen Tas, former Chief Innovation & Strategy Officer at Philips, and Harm-Jan Wessels. The capital will support platform development, the advancement of AI capabilities, and team growth. *What's the need?* Hospitals maintain decades of imaging data in Picture Archiving and Communication Systems (#PACS). PACS vendors and healthcare providers face operational and financial pressure to migrate massive volumes of #radiology data—often hundreds of terabytes per hospital—from legacy on-premise PACS systems to modern #cloud-based infrastructures, yet today’s migration processes remain largely manual (consultant-led), expensive, and time-consuming, often taking a year or more. *What's the solution?* Datamonk’s solution leverages agentic AI to automate these migrations and concurrently improve data quality. The software agents detect and resolve metadata issues, standardize study naming, and validate data integrity while the migration runs. This allows hospitals to go live faster with data that is immediately ready for clinical workflows, research, and AI applications. At Nina's, we are excited about joining efforts towards a scalable way to standardize and migrate imaging data, creating the foundation for interoperability and future innovation. The Datamonk team was founded by Jaap Gielink (serial entrepreneur), Jai Bhatia (co-founder of Viewics Inc., acquired by Roche Diagnostics), and Matthew Condron (former CTO of a leading healthcare data migration consultancy). We are delighted to partner with Datamonk as they build the data layer for modern healthcare and solve this fundamental infrastructure challenge. Link: https://lnkd.in/dwdH7vew #wearenina #healthcare #HealthTech #MedicalImaging #AIinHealthcare at Tech.eu by Tamara Djurickovic

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Five critical barriers are killing AI’s potential in the European Health Data Space. The main challenge - interoperability. No interoperability, no AI breakthrough. Siloed data means AI models stay blind and ineffective. Even the EHDS, can’t solve them all. Without clear frameworks that balance trust, privacy, and data quality, AI models will never reach their full potential within the EHDS regulation. Here are 5 Critical Barriers to AI Success in the EHDS AI promises to revolutionize healthcare, but five overlooked challenges threaten to stall progress in the EHDS. 1 Data Access & Governance Without clear, trusted frameworks controlling how health data is accessed and shared, AI can’t get the quality data it needs. 2 Interoperability Siloed IT systems block seamless data flow, limiting AI’s ability to analyze diverse and comprehensive datasets. 3 Ethical & Privacy Concerns Patient rights and data privacy must be safeguarded, or AI adoption will face resistance and legal hurdles. 4 Data Quality & Variability Inconsistent and poor-quality data leads to unreliable AI predictions and biased outcomes. 5 Financial Incentives Lack of aligned funding and investment slows AI deployment, especially in resource-constrained public healthcare. What issues are you seeing in benefiting from AI within the EHDS?

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3 Healthcare AI papers published in last 3 months i'm reading now: 1. 📚NEJM AI - MedAgentBench: A Virtual EHR Environment to Benchmark Medical LLM Agents. By Jonathan H. Chen et al https://lnkd.in/eUCnXqgv This paper introduces MedAgentBench, a benchmark with 300 clinically derived tasks and 100 realistic patient profiles in a FHIR-compliant EHR simulation. It evaluates state-of-the-art LLMs on agentic medical tasks, with the best achieving ~70% success, highlighting both potential and current limitations in clinical reliability. 2. 📚 Preprint - Toward the Autonomous AI Doctor: Quantitative Benchmarking of an Autonomous Agentic AI Versus Board-Certified Clinicians in a Real World Setting. By Adam Oskowitz et al https://lnkd.in/eMGy6vWU This preprint reports the first large-scale validation of an autonomous AI “doctor” (Doctronic Inc.) across 500 telehealth urgent-care cases, showing 81% diagnostic concordance and 99.2% treatment plan alignment with human clinicians. Expert review found the AI outperformed doctors in over a third of discordant cases, highlighting its potential to help address global clinician shortages. 3. 📚 NPJ Digital Medicine - The algorithmic consultant: a new era of clinical AI calls for a new workforce of physician-algorithm specialists. By Gabriel Brat, MD, MPH, FACS et al https://lnkd.in/eb6w_mRb This paper argues for a new specialist role - algorithmic consultants - to guide safe, effective integration of AI into clinical decision-making, much like clinical pharmacists manage medications. They would provide point-of-care model selection and interpretation, and oversee hospital-wide AI governance, ensuring tools are validated, aligned with patients, and used responsibly.

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Continuous sensing + multimodal AI can redefine how we gauge metabolic risk A new Nature Medicine study (https://lnkd.in/dDWixwga) followed > 1,100 adults who wore continuous glucose monitors, activity trackers, and provided gut-microbiome and genomic samples. An ensemble model blended those data streams to predict glycemic patterns far better than HbA1c alone and, importantly, exposed large inter-individual differences among people with the same HbA1c value. Three observations stand out: 1. Resting heart-rate and sleep metrics were tightly linked to how quickly glucose excursions resolved, suggesting that cardiovascular fitness may be a modifiable lever for glucose control. 2. Lower microbial diversity tracked with higher mean glucose, reinforcing growing evidence that gut ecology and metabolic health are intertwined. 3. The study recruited nearly half its participants from populations typically underrepresented in digital-health research, showing that decentralized designs can improve representation. For clinicians: single laboratory snapshots are giving way to continuous, context-rich phenotyping that can highlight risk and opportunities for intervention—well before disease thresholds are crossed. For developers and data scientists, the work illustrates the value of combining physiological, behavioral, microbial, and genomic signals into a single risk model, rather than treating each in isolation. Next steps will need to tackle practical questions: How do we integrate these composite scores into existing workflows? Which components drive actionable change? And can such approaches prove cost-effective in routine primary care? Still, this paper is an encouraging sign that multimodal digital phenotyping is moving from concept to implementation—and that more personalised prevention may soon be within reach. #AI #digitalhealth #diabetes #prevention

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