How Multimodal AI can Transform Healthcare

🔬 Multi-Modal, Real-Time, Human-Aware Healthcare AI Agents –Interactive Demos MetaBrain Labs has developed a new class of intelligent systems: multi-modal, real-time, human-aware AI agents capable of interpreting neurobiological signals to deliver emotionally adaptive and context-aware digital experiences. These AI agents are designed to be embedded seamlessly across healthcare, wellness, research, and performance platforms—transforming passive digital tools into responsive, personalized, and human-centered interfaces. These agents leverage live biosignal streams—including EEG, HRV, voice tone, and activity level—to detect emotional and physiological states in real time. They can then respond with adaptive coaching, behavioral nudges, and personalized interventions, while also feeding predictive analytics into clinical dashboards or trial workflows for continuous tracking and collaborative decision-making. To showcase its versatility, MetaBrain Labs has launched a rich library of live, interactive AI agent demos, grouped by sector. Explore how these agents operate in real-time: 🩺 1. Patient Engagement & Remote Health Monitoring Emotion-aware agents for vitals, monitoring, and recovery Clinical Intake Agent: Smart vitals and history collection with adaptive tone Post-surgery Care: Personalized recovery support using calming cues Threshold Alert: Real-time engagement when metrics spike Routine Check-in: Ongoing mood and biometric tracking Remote Monitoring: Predictive coaching from home sensors 🧪 2. Clinical Trials & Real-World Evidence AI for decentralized, participant-centered research AI Assistant: Freeform RWD capture AI Assistant (With Prompts): Guided, protocol-aligned data entry Profile Builder: Persistent trial identity and data continuity 🧠 3. Mental Health, Coaching & Behavioral Support Real-time adaptive coaching for emotional wellbeing Anxiety Program: Reframing and calming under stress Imposter Syndrome: Confidence-building dialogues Impatience Support: Mindfulness-driven regulation Self-Worth Aid: Emotional validation and support 🏅 4. Sports & Performance Analytics Neuroadaptive tools for athletic insight and growth Nexia AI: Emotion-aware brain-performance interface Check-in: Readiness and emotional prep Profile: Cumulative biometric + mental stats Review: Sensor-based post-event insights Growth Tracker: Long-term performance trends Feedback: Real-time stress recovery support 🌱 5. Lifestyle, Nutrition & Personal Optimization Emotion-informed agents for daily health and engagement Food Awareness: Mood-linked nutrition guidance User Journey Tuning: Emotion-aware UX design Fan Engagement: Personalized digital experiences We’re actively seeking partnerships with healthtech companies and visionary investors who are exploring: Embedding advanced AI agents into their devices or platforms Co-developing differentiated, human-aware healthcare solutions Investing in the next generation of AI-driven healthcare innovation

What happened with AI in healthcare this year? Time to cut through the noise and recap a few results and remaining challenges: 1/ The big surprise: The latest AI models are outperforming most doctors in medical reasoning tests. But there's a crucial catch - these evaluations used comprehensive pre-written patient information in the prompts. Real medicine is messier. 2/ Even with this limitation, we're seeing real impact. A recent survey found 1 in 5 US doctors are already using AI tools, mostly for admin work. My favorite example: Dragon copilot, which transcribes and summarizes patient conversations in real-time, is getting rave reviews from both doctors and patients. 3/ Remember all those stories about people diagnosing themselves with ChatGPT? Turns out they weren't flukes. But here's the real challenge ahead: 95% of healthcare data isn't even text - it's medical images, protein structures, sensor readings, and so on. Most of this isn't public, so generic AI models can't learn from it. 4/ The solution? An ecosystem of domain-specific AI models trained on real medical data. Many are proprietary but others are open source and available on platforms like GitHub, HuggingFace or Azure Model Catalog. In 2024, the community churned out new multimodal models. Many were featured in journals like Nature and downloaded thousands of times for research and development worldwide. 5/ These domain-specific models can understand real world raw data and help construct grounded prompts containing the full patient context that AI models excel at, enabling superhuman disease detection and ultimately personalized treatment plans. Leading healthcare providers like MGB, UW, Providence and others are starting to evaluate the clinical impacts of these multimodal healthcare AI models. We’ll start seeing additional results later next year, but it seems like a matter of “when”, not “if”. 6/ And these same models, along with biological models like Google’s AlphaFold or NVIDIA’s BioNeMo, also unlock pre-clinical use cases in drug discovery and development. 7/ All 10 of the largest big pharma players globally have partnered with AI drug discovery startups since 2023. Bringing a drug to market costs an average of $1.3B today. AI could dramatically accelerate new drug design and development while reducing costs. 8/ Here's a concrete win: Amgen used AI to boost their success rate for antibody programs from 50% to 90%, while cutting research time from 2 years to 9 months. That's not just faster - it could make new treatments possible for previously "undruggable" diseases. 9/ What's next? Dozens of other AI-designed drugs are now entering trials. In 2025, we'll see the first attempts at “agent” systems that can actually handle the messy reality of healthcare data - combining insights from imaging, lab results, and doctor's notes. None of this is about replacing scientists or doctors - it's about giving them better tools to help patients.

Superhuman AI agents will undoubtedly transform healthcare, creating entirely new workflows and models of care delivery. In our latest paper from  Google DeepMind Google Research Google for Health, "Towards physician-centered oversight of conversational diagnostic AI," we explore how to build this future responsibly. Our approach was motivated by two key ideas in AI safety: 1. AI architecture constraints for safety: Inspired by concepts like 'Constitutional AI,' we believe systems must be built with non-negotiable rules and contracts (disclaimers aren’t enough). We implemented this using a multi-agent design where a dedicated ‘guardrail agent’ enforces strict constraints on our AMIE AI diagnostic dialogue agent, ensuring it cannot provide unvetted medical advice and enabling appropriate human physician oversight. 2. AI system design for trust and collaboration: For optimal human-AI collaboration, it's not enough for an AI's final output to be correct or superhuman; its entire process must be transparent, traceable and trustworthy. We implemented this by designing the AI system to generate structured SOAP notes and predictive insights like diagnoses and onward care plans within a ‘Clinician Cockpit’ interface optimized for human-AI interaction. In a comprehensive, randomized OSCE study with validated patient actors, these principles and design show great promise: 1. 📈 Doctors time saved for what truly matters: Our study points to a future of greater efficiency, giving valuable time back to doctor. The AI system first handled comprehensive history taking with the patient. Then, after the conversation, it synthesized that information to generate a highly accurate draft SOAP note with diagnosis - 81.7% top-1 diagnostic accuracy 🎯 and > 15% absolute improvements over human clinicians - for the doctor’s review. This high-quality draft meant the doctor oversight step took around 40% less time ⏱️ than a full consultation performed by a PCP in a comparable prior study. 2. 🧑⚕️🤝 A framework built on trust: The focus on alignment resulted in a system preferred by everyone. The architecture guardrails proved highly reliable with the composite system deferring medical advice >90% of the time. Overseeing physicians reported a better experience with the AI ✅ compared to the human control groups, and (actor) patients strongly preferred interacting with AMIE ⭐, citing its empathy and thoroughness. While this study is an early step, we hope its findings help advance the conversation on building AI that is not only superhuman in capabilities but also deeply aligned with the values of the practice of medicine. Paper - https://lnkd.in/gTZNwGRx Huge congrats to David Stutz Elahe Vedadi David Barrett Natalie Harris Ellery Wulczyn Alan Karthikesalingam MD PhD Adam Rodman Roma Ruparel, MPH Shashir Reddy Mike Schäkermann Ryutaro Tanno Nenad Tomašev S. Sara Mahdavi Kavita Kulkarni Dylan Slack for driving this with all our amazing co-authors.

At ViVE I had the opportunity to discuss how Generative AI (Gen-AI) is reshaping healthcare along with Dan Sheeran (he/him) Nina Kottler, MD, MS, FSIIM and Monique Rasband. AI in imaging has been around, but Gen-AI brings new intelligence, adaptability, and efficiency. What Sets Gen-AI Apart? ✅ Multimodal Capabilities – Health data exists in many forms: transcripts, images, audio, and device readings. Traditional AI struggles with this diversity, but Gen-AI seamlessly integrates and analyzes it all. ✅ Faster Model Development – Traditional AI models take years— can go over two for a single brain region like the hippocampus. Foundation models leverage zero- and few-shot learning, accelerating this dramatically. Research from SonoSam (ULS FM) showed 90%+ accuracy on anatomies it wasn’t trained on, like fetal head and breast lesions. Imagine starting at 90% baseline performance! ✅ Explainability & Reasoning – Unlike traditional AI’s “black box,” foundation models explain their decisions, making them more transparent and interactive. ✅ Lower IT Costs & Scalability – Instead of managing hundreds of specialized models, healthcare organizations can use a few highly capable models, reducing IT complexity and streamlining updates. Real-World Impact and ROI: AI in Action A key ViVE discussion was how these technologies are transforming patient care and delivering ROI: ➡️ AI-Powered Command Centers – Acting as real-time intelligence hubs, they optimize patient flow, predict ICU admissions, and reduce length of stay using predictive analytics. Hospitals can proactively improve efficiency and outcomes. ➡️ Full-Body X-ray Foundation Models – These models can potentially enable opportunistic screening, using existing imaging data to detect conditions beyond the original scan purpose, helping reduce costs and improve preventive care. ➡️ Auto-Segmentation on CT Scans – Gen-AI cuts radiation therapy planning time from hours/days to minutes, ensuring faster, more precise treatment. Securing AI in Healthcare As we integrate these advancements, security remains critical: 1️⃣ Data Privacy & Compliance – HIPAA/GDPR compliance, encryption, and anonymization. 2️⃣ Adversarial Protection – Preventing prompt injections, model manipulation, and poisoning attacks. 3️⃣ Deployment Security – API authentication, access controls, and real-time validation. 4️⃣ Regulatory Oversight – Audit logs, explainability, and robust risk assessment. The ViVE discussions reinforced that Gen-AI isn’t just about efficiency—it’s reshaping patient care. #ViVE2025 #AI #HealthcareAI #Radiology #GenAI #DigitalTransformation

Explainable AI is essential for precision medicine—but here's what many are missing My latest blog post unpacks a fascinating Nature Cancer paper from showing multimodal AI outperforming traditional clinical tools by up to 34% in predicting outcomes. What surprised me most? Elevated C-reactive protein—typically a concerning marker—actually indicates LOWER risk when combined with high platelet counts. Some physicians may do this in their heads but they simply cannot do this same analysis across thousands of variables systematically.  With the right multimodal data and AI systems, we can create a fundamental shift in how we develop therapies and treat patients. Here's the twist: many argue we need randomized trials before implementing these AI tools. But that’s the wrong framework entirely. Google Maps doesn't drive your car—it gives you better navigation. Similarly, clinical AI doesn't treat patients—it reveals biological patterns that already exist. The real question: Can we afford to ignore these multimodal patterns and connections in precision medicine? Or should we use AI as a tool to uncover them and help inform our decision making? Read my full analysis here: https://lnkd.in/gGA4KTip -- I'd love to hear from others working at this intersection: How is your organization approaching multimodal data integration in precision medicine? #PrecisionMedicine #HealthCareAI #CancerCare