"New Sights of Machine Learning in Biomedicine: Special Issue Open for Submissions"

🩻 The Power of Bioimaging in Modern medicine Bioimaging has become one of the most transformative tools in healthcare. From MRI and CT scans to cutting-edge molecular and optical imaging, these technologies allow us to see inside the body with unprecedented clarity. But the real shift is happening at the intersection of imaging and artificial intelligence. Machine learning algorithms are now able to detect patterns invisible to the human eye, assisting clinicians in identifying diseases earlier, guiding treatment decisions, and monitoring outcomes with greater precision. Bioimaging is more than just pictures — it is the bridge between engineering, data, and medicine, enabling a deeper understanding of health and disease. As innovation continues, the future holds even more powerful imaging solutions that will make diagnostics faster, smarter, and more accessible. #Bioimaging #BiomedicalEngineering #AIinHealthcare #MedicalInnovation #HealthcareTechnology

✨ Excited to share some of our recent research contributions in the field of medical imaging and AI for intracranial aneurysm management! 📌 Journal Publications  – "Computationally efficient dilated residual networks for segmentation of major cerebral vessels in MRA." Network Modeling Analysis in Health Informatics and Bioinformatics 14, no. 1 (2025): 95.  – "Machine learning analysis of integrated ABP and PPG signals towards early detection of coronary artery disease." Scientific Reports 15, no. 1 (2025): 1-9.  – "Computer-Aided Volumetric Quantification of Pre- and Post-Treatment Intracranial Aneurysms in MRA" IET Image Processing, (2025). These works reflect our ongoing efforts in developing AI-driven tools for diagnosis, quantification, and post-treatment monitoring of intracranial aneurysms. Special thanks to my co-authors, supervisors, and neurointerventional radiologists who contributed their invaluable expertise, clinical insights, and continuous support in making this research possible. Together, we are working towards bridging the gap between AI innovation and clinical practice to improve patient outcomes. #AI #MedicalImaging #Aneurysm #DeepLearning #IEEE #Research #Teamwork

Artificial Intelligence + Network Medicine = The Next Leap in Precision Medicine A recent NEJM AI review (Altucci et al., Aug 28, 2025) highlights how the convergence of Network Medicine (NM) and Artificial Intelligence (AI) is reshaping the way we understand and treat disease. -Over the last 20 years, NM has advanced our ability to map disease mechanisms, identify therapeutic targets, and personalize interventions. -AI — particularly deep learning — is now accelerating this process by extracting mechanistic insights from massive multiomic datasets. -Together, NM and AI form a powerful framework for tackling biomedical complexity, promising faster, more precise, and biologically grounded predictions. The review emphasizes: ✅ How AI enhances NM’s ability to model molecular interactions. ✅ Real-world applications already delivering clinically meaningful precision therapies. ✅ Persistent challenges — from data integration to validation — that require collaborative innovation. This synergy between NM and AI is more than a technical evolution: it is a paradigm shift towards truly personalized medicine. 👉 What do you think? Will AI + NM become the standard backbone of precision medicine in the next decade, or will we face barriers that slow its clinical adoption? #algorethics #AIinMedicine #IAenMedicina #algorética #PrecisionMedicine #MedicinadePrecisión

📢 From @BME_Horizon A Multi-Modal Medical Image Fusion Framework Based on Two-Scale Decomposition and Saliency Detection 🧠🖼️ Authors: Harmanpreet Kaur, Renu Vig, Naresh Kumar Abstract: This research proposes a novel fusion approach for multimodal medical images utilizing mean filtering and maximum symmetric surround saliency detection. The method decomposes images into base and detail layers, fuses the detail layers using guided filtering, and employs linear summation for the base layer. The fused images demonstrate superior performance in visual appearance and quantitative assessment compared to existing methods, effectively preserving edges and energy information across modalities. Key Findings: ✅ 𝗜𝗺𝗮𝗴𝗲𝘀 𝗱𝗲𝗰𝗼𝗺𝗽𝗼𝘀𝗲𝗱 into 𝗯𝗮𝘀𝗲 𝗮𝗻𝗱 𝗱𝗲𝘁𝗮𝗶𝗹 𝗹𝗮𝘆𝗲𝗿𝘀 𝘂𝘀𝗶𝗻𝗴 𝗺𝗲𝗮𝗻 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴. ✅ 𝗗𝗲𝘁𝗮𝗶𝗹 𝗹𝗮𝘆𝗲𝗿𝘀 fused via guided filtering; base layers fused using 𝗹𝗶𝗻𝗲𝗮𝗿 𝘀𝘂𝗺𝗺𝗮𝘁𝗶𝗼𝗻. ✅ Evaluated on a 𝘄𝗶𝗱𝗲𝗹𝘆 𝘂𝘀𝗲𝗱 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗲 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 with 𝗶𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲. ✅ 𝗘𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗹𝘆 𝗽𝗿𝗲𝘀𝗲𝗿𝘃𝗲𝘀 𝗲𝗱𝗴𝗲𝘀 and energy information in fused images. ✅ Enhances accuracy and reliability for diagnostic imaging and 𝗶𝗻𝘁𝗿𝗮-𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗴𝘂𝗶𝗱𝗮𝗻𝗰𝗲. Full study available here:  https://lnkd.in/gBsHXAmJ #MedicalImaging #ImageFusion #MultimodalImaging #SaliencyDetection #PixelLevelFusion #HealthcareAI #ComputerVision

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/

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

🔎 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

Everyone I know in medicine is using OpenEvidence, as am I - multiple times a day! But we could argue that OE's success is not because it has reached a fundamentally new level of agentic cognition. Rather, it has carefully designed - a domain-specific environment (medical and scientific literature) - restricted source querying (Pubmed and trusted databases, helps limit hallucinations) - improved user experience (search, summarization, citations -> streamlines trust) But OE has important gaps. It does not query guidelines or compendia like the NCCN. It does not support more complex queries, including comparative trials and broader evidence synthesis. In a new op-ed in JAMA Oncology, we argue that guidelines like the NCCN compendia that oncologists use every day to guide their clinical prescribing can benefit from such modernization (query, summarization) alongside choice architecture (nudges, prompt engineering) to influence high-value decision making. Op-ed linked in comments, PDF attached here. Such improvements would allow these clinical decision support tools to evolve into true copilots that would equip oncologists for the modern era. Debra Patt, MD PhD MBA Kanan Shah

I know your time is valuable, so here’s the gist of this article. Oncology care has become increasingly complex, with new drugs and treatment options emerging faster than clinicians can keep up. Current tools—often static PDF guidelines—no longer meet the need for truly personalized decisions. The authors propose an “augmented oncologist”: an AI-driven assistant that integrates genetic, clinical, and historical data to suggest the most relevant therapies. Physicians would interact with it directly, receiving clear, up-to-date insights to guide discussions with patients while ensuring AI is applied responsibly.

**Computational Biomedical Intelligence in AI-Powered Decision Support Systems** 🧠💡 This work explores how advanced computational intelligence can empower healthcare decision-making, improve diagnostic accuracy, and support clinicians with AI-driven insights. Looking forward to connecting with peers, researchers, and professionals interested in **AI, biomedical engineering, and healthcare innovation**. 🚀 \#ArtificialIntelligence #BiomedicalIntelligence #DecisionSupportSystems #HealthcareAI #Research