AI and Network Medicine: A New Era in Precision Medicine

📢 The Special Issue "New Sights of Machine Learning and Digital Models in Biomedicine" is open for submissions! 🥼 This Special Issue is guest-edited by Prof. Dr. Hatem Alhadainy. 💡 This Special Issue explores the intersection of machine learning (ML), digital modeling, and biomedicine, highlighting innovative approaches that leverage advanced computational techniques to enhance medical research, diagnosis, treatment, and patient care. 🔗 Click the link to access more details about the Special Issue: https://lnkd.in/gxs_Gybe 🕑 The deadline for manuscript submissions is 31 January 2026. 🎉 Welcome to join us as authors and reviewers! 👏 And welcome to follow our LinkedIn account @Bioengineering MDPI. #Machine_Learning #Digital_Models #Biomedicine

The NEJM AI published this week, "Artificial Intelligence and Network Medicine: A Path to Precision Medicine". It’s impressive to see how the convergence of technologies — from multi-omics and imaging to AI and network science — is steadily bringing precision health from concept to reality. These approaches move us beyond reductionist views and integrate genomic, phenotypic, and environmental diversity. Credit to Lucia Altucci et al., led by Joseph Loscalzo from the Brigham and Women's Hospital, for their contribution, which highlights how, in the coming years, integration will redefine the way we prevent, diagnose, and treat diseases on a global scale. https://lnkd.in/gJumB2mm #genomicmedicine #precisionhealth #precisionmedicine #publichealth #personalizedmedicine #AI #Artificialintelligence #multiomics Genómica Médica Instituto Nacional de Medicina Genómica

I am truly excited to see how Retro Biosciences, in collaboration with OpenAI, is pushing the boundaries of longevity and regenerative medicine: • The experimental drug RTR-242, designed to reactivate cellular autophagy, is moving toward its first human clinical trials with the aim of tackling neurodegeneration and improving healthy lifespan. • At the same time, by leveraging advanced AI models, Retro achieved a 50× increase in the expression of stem-cell reprogramming markers (Yamanaka factors), opening new avenues for safer and more efficient tissue-regeneration therapies. Regardless of the ultimate success of this specific trial, it clearly signals the dawn of a new era in medicine — one where the goal is no longer just treatment, prevention or incremental improvement, but true longevity. Artificial intelligence and biomedical research are converging to transform decades-old concepts into practical solutions for human healthspan and longevity. #Longevity #RegenerativeMedicine #ArtificialIntelligence #RetroBiosciences #Innovation

🔍 AI + Chest X-rays: Global Momentum in Cost-Effective Osteoporosis Screening Following the recent acceptance of our US cost-effectiveness manuscript, we are thrilled to share yet another milestone: our Singapore cost-effectiveness study has been officially accepted as a late-breaking abstract at ASBMR 2025!✨ 👉https://lnkd.in/ehhFZFvz 📌 Presentation Details Title: From X-Rays to Early Intervention: Cost-Effectiveness of Deep Learning-Powered Opportunistic Osteoporosis Screening in an Asian country Presentation #: Sat-558 Session: Poster Session I: Late-Breaking Abstracts Date/Time: Saturday, September 6, 2025 / 2:00–3:30 PM This acceptance highlights the growing recognition of AI-based opportunistic screening as both clinically valuable and economically viable across diverse healthcare systems from the US to Asia. 📍 Meet us at Booth #315 at the Seattle Convention Center throughout ASBMR 2025 to learn more about our research and solutions. 🙏 Special thanks to Manju Chandran, Mickaël Hiligsmann, Jean-Yves REGINSTER 🌿and research partners for making this possible. We look forward to sharing more at ASBMR 2025 in Seattle!

It always amazes me truly how long it takes to communicate about innovation until solid clincal data and patient outcomes actually change daily practice and consultation pathways. Rayner’s 2025 ESCRS Symposium was no doubt the most seeked scientific event this year - no other topic but AI spiral optics and outcomes set the trend. This technology has it all: a degree of sexiness, data robustness, a degree of cheekiness to challenge the status quo and clinical outcomes to win every surgeons in a storm ❤️

BioASQ 2025 advances biomedical AI with six innovative tasks. 💡 These advancements can streamline drug discovery and personalized medicine, potentially saving billions in R&D costs. 🇬🇷 🇮🇹 Arxiv paper: 📄 https://lnkd.in/ekwxYUyN Author: Anastasios Nentidis, Georgios Katsimpras, Anastasia Krithara, Martin Krallinger, Miguel Rodríguez-Ortega, Eduard Rodriguez-López, Natalia Loukachevitch, Andrey Sakhovskiy, Elena Tutubalina, Dimitris Dimitriadis, Grigorios Tsoumakas, George Giannakoulas, Alexandra Bekiaridou, Athanasios Samaras, Giorgio Maria Di Nunzio, Nicola Ferro, Stefano Marchesin, Marco Martinelli, Gianmaria Silvello, Georgios Paliouras University of Padua #ComputerScience #ComputationandLanguage #arXiv

🔎 How do we make artificial intelligence in healthcare truly trustworthy? Trust in medical AI is not just about algorithms and accuracy. It is about people. It grows when doctors, nurses, patients, ethicists, and legal experts are part of the process from day one, ensuring that AI tools are safe, fair, understandable to all those involved, and meet real-world needs. In a new article, experts from four European research projects (EuCanImage, #INTERVENE, @BIOMAP and @Bigpicture) — share a step-by-step approach to building trust in AI across its entire lifecycle, from design to deployment. Our message is clear: 🤝 Involve diverse stakeholders at every stage 🩺 Prioritise human relationships and transparency ⚖️ Balance technical excellence with ethical responsibility 📄 Read the full article here: https://lnkd.in/eCtuV7bC #MedicalAI #TrustworthyAI #DigitalHealth #INTERVENE #PatientEngagement Institute for Molecular Medicine Finland (FIMM) EMBL Università di Siena Norwegian University of Science and Technology (NTNU) University of Tartu BBMRI-ERIC Technical University of Munich CSC - IT Center for Science Hasso Plattner Institute Aalto University University of Cambridge Mass General Hospital Università degli Studi di Torino Cancer Patients Europe - CPE Queen Mary University of London Kimmo Mattila Reedik Mägi Kristi Läll Christoph Lippert Marttinen Pekka Aoife McMahon Michael Inouye Kaya Akyüz Dr. Mónica Cano Abadía Marie-Christine F. Martha Gilbert

Artificial intelligence is transforming healthcare — but trust in medical AI depends on more than technology. At the recent Building Trust in Medical AI workshop, organised by EuCanImage, #INTERVENE, BIOMAP, and Bigpicture, the key recommendation was clear: AI must be developed with patients, healthcare professionals, ethicists, and legal experts involved at every stage — from design to deployment. As a proud partner in #INTERVENE, CPE is working to ensure the patient perspective is central to building trustworthy, fair, and human-centred AI that reflects real needs. 🔗 Read more here: https://lnkd.in/dz8rp-yK 📝 Explore the key outcomes here: https://lnkd.in/egP5rFGT #MedicalAI #PatientCentred #INTERVENE #CPE Nismo same FEMINA M Cancer Care Albania Asociația Sănătate pentru Comunitate (SpC)/ Community Health Association Fundacja EuropaColon Polska European Liver Patients' Association - ELPA Ελληνική Αντικαρκινική Εταιρεία- Hellenic Cancer Society Hodgkin en non-Hodgkin vzw Italian Kidney Cancer Association (ANTURE) Nierenkrebs-Netzwerk Deutschland e.V. Lung Cancer Europe Lymfklierkanker Vereniging Vlaanderen vzw Tackle Prostate Cancer OSCAR'S ANGELS France & Italia Ruch Onkologiczny PARS Asarga Nätverket mot Cancer Genç Birikim Derneği | Young Accumulation Association One Cancer Place the cancer collaborative (colab) le collaboratoire cancer ESMO - European Society for Medical Oncology Violeta Pirana Women Association fighting Breast Cancer Hlas pacientů

Across industries, the true promise of #AI lies not in automating what exists today, but in rethinking how complex decisions are made. In #healthcare, that need is especially urgent as cardiovascular disease continues to claim millions of lives each year, and timely, accurate diagnosis can mean the difference between prevention and crisis. Our latest whitepaper on AI-Enabled Assessment of Carotid Stenosis, authored by Srinivas Kudavelly and Venkat Sudheer, shows how clinical insight and engineering depth can come together to make diagnostics more precise and consistent. By combining dual-scan ultrasound views with intelligent algorithms, it points to a future where earlier detection leads to better outcomes where they matter most. https://lnkd.in/dWJHaCeh For me, the lesson goes beyond this use case. It reflects the larger opportunity we’re pursuing at @Cyient: applying deep engineering, “from silicon to software, platforms to AI”, to help industries turn fragmented data into confident decisions. That’s how we build not just smarter products, but stronger lifecycles and enduring value. Particularly in Life Sciences, it could become a game-changer in saving lives. As AI becomes central to decision-making, the differentiator will be how we balance speed of adoption with responsibility and oversight. #IntelligentEngineering #HealthcareInnovation #MedicalImaging #DecisionIntelligence #EngineeringExcellence #Cyient #DigitalHealth #LifeSciences #DataToDecisions #FutureOfHealthcare #StrokeAwareness

Thrilled to share that our review paper has been published in the International Journal of Research Publication and Reviews! 📄 “Redefining Molecular Diagnostics: A Study on Human-AI Collaboration” Our paper highlights how artificial intelligence and automation are transforming clinical diagnostics, with a special focus on molecular diagnostics. While upstream processes like extraction and sequencing are already automated, interpretation and reporting remain largely manual. Through workflow observations and literature analysis, we propose a tri-zonal framework that identifies which tasks are best suited for automation, human-AI collaboration, or must remain human-exclusive. This work sheds light on the growing need for explainable AI and human-in-the-loop models to make diagnostics faster, more reliable, and scalable—without replacing clinical expertise. I am truly grateful to my co-authors Avani Thakare, Rushika Dandare, and Miheka Mali for their collaborative efforts, and to our institutions MIT-WPU and MIT ADT University for providing support and research facilities. This publication represents an exciting step toward bridging the gap between AI technology and real-world clinical practice. I’d love for my network to check it out and share your thoughts! DOI: https://lnkd.in/dCHaUTud #ResearchPublication #MolecularDiagnostics #ArtificialIntelligence #AutomationInHealthcare #ExplainableAI #HumanInTheLoop #BiomedicalResearch #DigitalHealth #Genomics #ClinicalDiagnostics #AIinHealthcare #HealthcareInnovation #ResearchCollaboration #Bioinformatics #FutureOfDiagnostics