BrainSightAI's AI for neurological and psychiatric conditions

The Neuroprosthetics Market is set to reach USD 61.84 billion by 2034, with an impressive CAGR of 15.5%. Technological innovation, AI integration, and growing neurological disorder cases are accelerating demand globally. #Neuroprosthetics #HealthcareInnovation #MedicalTechnology #MarketInsights 🗂 𝐄𝐱𝐩𝐥𝐨𝐫𝐞 𝐭𝐡𝐞 𝐄𝐧𝐭𝐢𝐫𝐞 𝐑𝐞𝐩𝐨𝐫𝐭: https://lnkd.in/dfQNx-EP

#AINews 🌍 Ohio State Researchers Use Advanced AI to Predict Seizure Outcomes, Opening New Frontiers in Personalized Epilepsy Treatment A groundbreaking study from Ohio State University unveils advanced AI tools that predict seizure outcomes by analyzing fine motor differences in mouse models. This marks a significant step in neuroscience and personalized healthcare. 📊 Key Implications for Your Team: • AI tools leverage motion sequencing and 3D video analysis for precise seizure outcome prediction • Identification of subtle biomarkers like head tilts, enabling detailed behavioral analysis • Integration with broader epilepsy research frameworks, enhancing data-driven approaches • Potential for rapid drug discovery and personalized treatment plans 🔍 Strategic Considerations: The ability to track minute neurobiological changes could accelerate AI applications across other neurological disorders and medical diagnostics. 💡 My Take: As AI becomes more integrated into medical diagnostics, transparency and explainability of models are paramount. Establishing clear frameworks should be prioritized to harness AI's full potential in healthcare. 🤔 How can your organization integrate such advanced AI frameworks to enhance diagnostics? #Innovation #TechLeadership #DigitalHealthcare #EnterpriseAI #HealthcareInnovation #SmarterWithAI

📣 𝐍𝐞𝐰 𝐩𝐫𝐞𝐩𝐫𝐢𝐧𝐭 𝐚𝐥𝐞𝐫𝐭! 📣 We are excited to share our work on the Brain Health Data (BHD) initiative, a new resource linking ~417K MRI and ~846K CT brain scan images and their raw text reports from NHS Scotland to electronic health records. The Brain Health Data (185TB+) from 2010-2018 offers: * Large-scale data for AI/ML research * Population-wide representation * Long-term follow-up for dementia studies * Real-world clinical variability The work of my group in this project involved using clinical NLP for curation and quality control to make this data research-ready. The dataset is now accessible to researchers via Public Health Scotland's eDRIS. This resource will accelerate AI development for neurodegenerative disease prediction and personalised medicine approaches. Read the full paper here: https://lnkd.in/eyD_Y-Ha #HealthData #AIinHealthcare #Neuroscience #Dementia #DataScience #MedicalImaging #ClinicalNLP Michael P. J. Camilleri, Dorian Gouzou, Salim Al-Wasity, Muthu R K Mookiah, María Valdes Hernandez, Sotirios (Sotos) Tsaftaris, Andrew Brooks, Ruairidh MacLeod, Honghan Wu, Brenda Bauer, Claire Grover, Parminder Singh Reel, Susan Krueger, Richard Tobin, Douglas Steele, Grant Mair, Joanna Wardlaw, Alexander Doney, Emanuele Trucco, William Whiteley Public Health Scotland This work was supported by NEURii, a collaborative partnership involving The University of Edinburgh, Gates Ventures, Eisai Co., Ltd., LifeArc and Health Data Research UK (HDR UK). We acknowledge the eDRIS team (Public Health Scotland) for their support in obtaining approvals, the provisioning and linking of data and facilitating access to the National Safe Haven. The Brain Health Data Pilot is supported by Alzheimer’s Disease Data Initiative (ADDI) and HDR UK with funding to the University of Edinburgh.

🧠 Can AI match the expertise of a trained neurologist? A UWE Bristol research team led by Dr David Western has developed advanced AI models that interpret EEG brain activity with expert-level accuracy, helping tackle the national shortage of trained interpreters for neurological conditions. The project, MED-SHED, is one of just 48 across the UK funded through UKRI’s Proof of Concept programme - designed to help cutting-edge research translate into real-world impact. Now, the team is working to bring the technology into clinical settings, using NHS data and exploring social enterprise models that prioritise transparency and public benefit. 📍Potential impact? - Faster, more accurate diagnosis - Better treatment decisions - Wider access to brain monitoring tools in critical care 📖 Read more via UWE News: 🔗 https://lnkd.in/ehZdjHRG #AIinHealthcare #BrainHealth #Neurotechnology #EEG #UKRI #ProofOfConcept #UWEBristol #Innovation #ResearchWithPurpose

𝗖𝗟𝗜𝗡𝗜𝗖𝗜𝗔𝗡-𝗧𝗥𝗔𝗜𝗡𝗘𝗗 𝗜𝗡𝗧𝗘𝗟𝗟𝗜𝗚𝗘𝗡𝗖𝗘 — 𝗪𝗛𝗘𝗥𝗘 𝗗𝗔𝗧𝗔 𝗔𝗡𝗗 𝗘𝗫𝗣𝗘𝗥𝗧𝗜𝗦𝗘 𝗖𝗢𝗟𝗟𝗔𝗕𝗢𝗥𝗔𝗧𝗘 The next generation of clinical AI won’t be built in isolation — it will be trained by physicians who understand the data behind the diagnosis. In neurology, questions rarely have binary answers. Is it demyelination or degeneration? Autoimmune or metabolic? Transient or progressive? AI can’t decide these alone — but it can learn to think like a neurologist when taught by one. At the Premier Clinical & Research Neuromuscular Institute (PCRNI), we collaborate with data scientists and technology innovators to build systems that: 🔹 Integrate EMG, MRI, and antibody data into unified disease models. 🔹 Predict disease trajectory and therapeutic response with adaptive learning. 🔹 Support evidence-driven second opinions that merge computational logic with human reasoning. 💡 𝗔𝗜 𝗶𝘀𝗻’𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗶𝗻𝗴 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 — 𝗶𝘁’𝘀 𝗿𝗲𝗰𝗮𝗹𝗶𝗯𝗿𝗮𝘁𝗶𝗻𝗴 𝗰𝗮𝗿𝗲. When engineers and neurologists co-train these models, the outcome isn’t automation — it’s augmented cognition: medicine that learns with us, not without us. #AIinMedicine #DigitalHealth #Neurology #PrecisionNeurology #PharmaInnovation #Biomarkers #ClinicalAI #AANEM2025 #PCRNI

🧠 Can Machine Learning Predict ICU Readmission After a Stroke? Discharging a stroke patient from the ICU always feels like walking a tightrope. They look stable… until they are not. A few days later, they bounce back — longer stays, higher costs, worse outcomes. A new study I came across looked at this exact problem using data from the MIMIC-IV critical care database. And here’s the twist — the best-performing model was not a deep learning powerhouse. It was logistic regression — the simplest, most transparent model in the bunch. It identified four key predictors: 🩸 Red blood cell count 🧂 Serum potassium levels 💊 Inpatient steroid (glucocorticoid) use 🩺 Peptic ulcer disease These are everyday clinical variables — no black box magic. Just clear, interpretable risk signals that could actually help clinicians plan safer ICU discharges. What I really like about this study is its practicality. You could embed this model right into an EHR or a bedside checklist. It is not perfect (AUC 0.68), but it is usable, explainable, and a good step toward smarter post-ICU workflows. 🔗 Read the full breakdown here → https://lnkd.in/gPCPVq52 #MachineLearning #HealthcareAI #CriticalCare #StrokeCare #ClinicalAI #MIMICIV #DataScience #ICU #Neurology #DigitalHealth #MedTech

Can AI Outsmart Neurological Diseases? As Artificial Intelligence begins to decode brain networks, predict degeneration, and assist in diagnosing complex neurological disorders, we are witnessing a paradigm shift—from reactive care to anticipatory intervention. But even as we marvel at what’s technically possible, some tough questions loom: If a model predicts onset of Alzheimer’s disease or Parkinson’s disease years in advance, who bears the responsibility—and how do we act on it ethically? Can we trust “black‐box” AI systems when the stakes are the human mind and the clinical outcome? Are we preparing our health systems, regulation, and society for a world where machine-driven neurology may outpace human oversight? And finally: If AI can detect brain disease before symptoms emerge, should it—and at what cost to privacy, equity and consent? Explore deeper: https://lnkd.in/dQcA_WzA The promise is huge—improved diagnostics, personalized therapies, earlier interventions—but so are the responsibilities. As medical affairs professionals, tech-innovators, neuroscientists, and global pharma stakeholders, we must ask: Are we ready? I invite you to share your view: What’s the biggest barrier to adopting AI in neurology—from data bias and explainability, to regulation, reimbursement and clinical trust? Please hit “Like” if you found this relevant and leave your comments below—I’d love to hear your insights. #AI #Neurology #Neurotechnology #PharmaInnovation #MedicalAffairs #HealthTech #MachineLearning #EthicsInAI #BrainHealth #FutureOfMedicine