How AI can improve healthcare decision-making with computational intelligence.

inaugural symposium on AI in healthcare, where national leaders in artificial intelligence explore how AI is transforming healthcare in practice. Discover how cutting-edge innovations are advancing patient care, reshaping medical education, and accelerating scientific discovery. This forward-looking event will feature the latest developments in AI—from clinical applications and diagnostics to education, health equity, and policy—offering insights into how technology is redefining the future of medicine.

Regenstrief Institute experts joined presenters from around the world at MedInfo 2025 in Taipei, Taiwan, to explore how AI and informatics can transform healthcare. From predictive modeling to public health visualization, Regenstrief's research reflects the Institute's global leadership in biomedical informatics and innovation. https://lnkd.in/gQxwHtZN

CRISPR-GPT helps scientists to generate designs, analyze data and troubleshoot design flaws Stanford Medicine researchers have developed an artificial intelligence tool to help scientists better plan gene-editing experiments. via News Medical Device / Technology News Feed

📢 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

Excited to see the new Special Issue on "Applications of Computational Medicine" published in the Journal of Informatics and Medical Imaging (@Lidsen Journals). This collection delves into the powerful convergence of data science, AI, and clinical practice. The research covers groundbreaking work in medical imaging informatics, AI-powered diagnostics, computational models for disease progression, and novel drug discovery methods. It's inspiring to see how computational methods are moving from theory to tangible applications that can improve patient outcomes and revolutionize healthcare delivery. A must-read for anyone in health tech, medical research, data science, or clinical practice looking to stay at the forefront of innovation. Check out the issue here: https://lnkd.in/dJxwsuCW #ComputationalMedicine #AIinHealthcare #HealthTech #MedicalImaging #DigitalHealth #AI #MachineLearning #MedTech #Innovation #Research #PrecisionMedicine

Rutgers taps biomedical informatics expert to lead new health AI center https://ow.ly/qHcb50WU4bs #RutgersUniversity #HealthAI #BiomedicalInformatics #AIResearch #HealthTech #DataScience #MedicalInnovation #HealthcareAI #DigitalHealth #AIinHealthcare

🔎 Unchecked AI in Academic Publishing: The AACR’s Warning Sign The American Association for Cancer Research (AACR) recently published sobering findings: in 2024, 23% of abstracts and 5% of peer reviews submitted to its journals contained text likely generated by large language models (LLMs), yet less than a quarter of authors disclosed that AI was used, even though disclosure is mandatory. What tools did they use? A detector created by Pangram Labs, trained on millions of human-written documents, plus synthetic “AI mirror” texts. It achieves ~99.85% accuracy (very low false positive rates). Why this matters: • Transparency isn’t just academic correctness—it’s essential for trust, reproducibility, and accountability. • Misused or undisclosed AI can introduce errors, especially when methods are paraphrased or rephrased without care. • There are equity dimensions: non-native English researchers are using LLMs more often. Proper support + disclosure matters. What we should do moving forward: 1. Enforce disclosure rules rigorously (journals, conferences, reviewers). 2. Integrate AI-detection tools in the submission/review pipeline. 3. Educate researchers on what AI can’t do: respecting nuance, domain-specific correctness, context. 4. Standardize policies across publishers so the expectations are clear. 🔍 My take: If we don’t adapt oversight and norms, we risk eroding core academic values. AI is a tool, not a substitute—but we need to treat it with full transparency. 😶🌫️ What do you think: Should journals issue retroactive notices or corrections when undisclosed AI use is found? How should authors safely use LLMs without risking misrepresentation? —- Ali Fatemi, Ph.D., MCCPM, DABMP Director of Medical Physics, Merit Health (CHS) Southeast Professor of Physics, Jackson State University, USA Adjunct Professor, Dept of Physics, Université Laval, Canada Founder & CEO, SpinTecx http://www.spintecx.com #AcademicIntegrity #AIethics #Publishing #LLMs #ResearchPolicy #MedicalPhysics #AIinHealthcare #MedTech #HealthTech #DigitalHealth #SmartHealthcare #FutureOfRadiology #ClinicalAI #ClinicalInnovation #MedicalImaging #Radiology #VendorNeutral #GlobalHealthTech #Standardization #PrecisionMedicine #HealthcareLeadership #PatientSafety #StartWithWhy #HealthcareStartups #VentureCapital #AngelInvesting #VCFunding #StartupFunding #TechStartups #HealthcareAI #AcademicEntrepreneur #ClinicalEntrepreneurship #ClinicalTranslation #AIInRadiology

As a part of my Final Year Project, I have been conducting research on non-invasive methods for blood pressure estimation. My work explores how biosignals such as ECG and PPG can be processed to extract features like pulse transit time (PTT) and pulse wave velocity (PWV), which are then mapped to blood pressure using machine learning models. The objective of this research is to develop a system that enables cuff-less, continuous, and affordable BP monitoring, with potential applications in remote healthcare and telemedicine. This journey has involved: - Signal acquisition & preprocessing - Feature extraction and analysis - Model training for BP prediction - Evaluating accuracy and feasibility It’s been a challenging yet exciting process, and I’m eager to continue refining the methodology to make it more robust and reliable. If you are working in biomedical engineering, health informatics, or AI in healthcare, I’d love to exchange ideas and learn from your experience. #Research #BiomedicalEngineering #HealthTech #MachineLearning #NonInvasiveMonitoring #FYP

Innovation in Healthcare AI! Presenting CardioBERT from ABES Dr. Kadambri Agarwal's research paper titled "CardioBERT: A Cardiac Identification using Fusion Features in Consumer Healthcare" has been published in the prestigious IEEE Transactions on Consumer Electronics (Impact Factor 4.3 | SCI | Q1). This pioneering work introduces CardioBERT—a novel fusion of CNNs and BERT models—offering advanced accuracy in detecting cardiovascular diseases from ECG signals. The model also leverages residue-level contact-map predictions to address dimensional mismatches, achieving an impressive 0.92% increase in accuracy over existing methods. 🗓️ Published on: June 2, 2025 🔍 DOI: 10.1109/TCE.2025.3575522 We proudly celebrate Dr. Kadambri's contribution to healthcare innovation through AI and commend her dedication to impactful research. Join us in congratulating her on this remarkable accomplishment! 👇 Let's inspire future engineers—one breakthrough at a time. #ABES #achievement #ai #healthcare #research #innovation #publication #engineering

Another great seminar opportunity on the same day! Presenter: Dr. Arsineh Boodaghian Asl Seminar Title: Network-agnostic computational approaches to capture the dynamic nonlinear behaviour of complex hospital systems. Date and time: September 19, 17:10 Place: YSU Krisp AI Lab Seminar Abstract: Hospitals are complex, highly interconnected systems where dynamic, nonlinear patient flow can create persistent bottlenecks that often originate in non-priority wards and emerge in critical care areas. The nonlinearity, evolving nature, and unpredictable changes make it challenging to perceive system behaviour. To address this, I will present two network-agnostic computational approaches capable of modelling diverse hospital networks, regardless of topological changes. The first is a hybrid modelling approach that integrates agent-based network simulation with network algorithms to identify persistent bottlenecks arising from both ward-to-ward connectivity and patient flow, supporting evidence-based decision-making and performance evaluation under evolving hospital topologies. The second is a dynamic nonlinear flow algorithm, derived from the Ford–Fulkerson and Edmonds–Karp algorithms, modified for complex hospital systems to capture bottleneck persistency, severity, and root causes, as well as to achieve better computational efficiency. Together, these approaches offer flexible, scalable, and adaptable tools for analysing and improving patient flow in complex hospital systems. Bio: https://lnkd.in/exdJkN7m Homepage: https://arsineh.me/