Key Applications of Multimodal AI

Multimodal AI is shaping a shift in healthcare by combining different kinds of patient data to improve care across diagnostics, treatment, and monitoring. 1️⃣ It links data from imaging, wearables, clinical notes, genomics, and more to create a fuller picture of patient health. 2️⃣ Imaging, physiological signals, and clinical notes are the most commonly used data types, especially in oncology, cardiovascular, and neurological disorders. 3️⃣ Intermediate fusion is the most used integration method, combining data at the feature level for better balance between complexity and interpretability. 4️⃣ These systems enable early diagnosis, prognosis, treatment planning, and real-time monitoring, with growing applications in areas like digital twins and automated reporting. 5️⃣ Personalized medicine is a major driver, with multimodal models supporting tailored treatment decisions by analyzing combined molecular, physiological, and behavioral data. 6️⃣ Despite progress, challenges remain: data heterogeneity, privacy concerns, lack of benchmarks, and regulatory constraints slow adoption. 7️⃣ Explainability is key for clinical trust. Emerging models include attention maps, concept attribution, and human-in-the-loop feedback for better transparency. 8️⃣ Energy demands of training large models have sparked interest in "green AI", focusing on efficiency and scalability in clinical settings. 9️⃣ Future systems may rely more on self-supervised and federated learning to handle data gaps and maintain privacy across institutions. 🔟 Clinical validation and regulatory reform are needed for multimodal systems to move from labs into widespread practice. ✍🏻 Florenc Demrozi, Mina Farmanbar, Kjersti Engan. Multimodal AI for Next-Generation Healthcare: Data Domains, Algorithms, Challenges, and Future Perspectives. Current Opinion in Biomedical Engineering. 2025. DOI: 10.1016/j.cobme.2025.100632 (pre-proof)

🧠 Part 3 of My Gemini AI Series: Real-World Impact In this third installment of my ongoing series on Google’s Gemini AI, I shift focus from architecture and strategy to real-world results. 💡 This article highlights how leading organizations are applying Gemini’s multimodal capabilities—connecting text, images, audio, and time-series data—to drive measurable transformation across industries: 🏥 Healthcare: Reduced diagnostic time by 75% by integrating medical images, patient notes, and vitals using Gemini Pro on Vertex AI. 🛍️ Retail: Achieved 80%+ higher conversions with Gemini Flash through real-time personalization using customer reviews, visual trends, and behavioral signals. 💰 Finance: Saved $10M+ annually with real-time fraud detection by analyzing call audio and transaction patterns simultaneously. 📊 These use cases are not just proof of concept—they’re proof of value. 🧭 Whether you're a CTO, a product leader, or an AI enthusiast, these case studies demonstrate how to start small, scale fast, and build responsibly. 📌 Up Next – Part 4: A technical deep dive into Gemini’s architecture, model layers, and deployment patterns. Follow #GeminiImpact to stay updated. Let’s shape the future of AI—responsibly and intelligently. — Dr. Veera B. Dasari Chief Architect & CEO | Lotus Cloud Google Cloud Champion | AI Strategist | Multimodal AI Evangelist #GeminiAI #VertexAI #GoogleCloud #HealthcareAI #RetailAI #FintechAI #LotusCloud #AILeadership #DigitalTransformation #AIinAction #ResponsibleAI

A challenge with AI is the division of labor between language-based systems that analyze text and sensor-based systems like computer vision that visualize our environment. #Multimodal AI trains algorithms in a fused way that allows us to manage complex AI tasks as a single workstream. Multimodal AI refers to systems capable of processing and integrating multiple types of data—such as text, images, audio, video, and sensor data—to generate comprehensive insights and perform complex tasks. Unlike traditional #AI, which specializes in one modality, multimodal AI combines these capabilities, allowing machines to "see," "hear," "read," and "understand" across various formats simultaneously. For federal leaders, it means AI can operate in environments that mirror the multifaceted, real-world challenges agencies face. For example, it can be used in the aftermath of natural disasters to analyze satellite imagery, combine it with real-time social media data and audio reports from first responders, and rapidly generate actionable maps of affected areas. One well-known multimodal AI algorithm is Contrastive Language-Image Pre-Training (CLIP), which is a key algorithm used in generating AI art. CLIP jointly trains image and text data using two neural networks called transformers, each acting as an encoder. These encoders code data into a latent space representing the features of the image and text separately. The dataset's class names (e.g., dog, cat, car) form potential text pairings to predict the most likely image-text pairs. CLIP is trained to predict if an image and text are paired in its dataset. The image encoder calculates the image's feature representation, while the text encoder trains a classifier specifying the visual concepts in the text. The key takeaway is that CLIP "jointly trains" or fuses by integrating two data types into a single training pipeline, unlike unimodal algorithms trained independently. Booz Allen is working to identify innovative applications for this technology. For example, we supported the National Institutes of Health (NIH) in developing cancer pain detection models fusing facial imagery, three-dimensional facial landmarks, audio statistics, Mel spectrograms, text embeddings, demographic, and behavioral data. For law enforcement and telemedicine, we created an acoustic #LLM tool enabling automated detection and analysis of multi-speaker conversations. We also published original research on multimodal AI algorithms that trained visible and long-wave infrared for applications in telemedicine and automated driving. Multimodal AI is no longer a vision of the future—it’s a capability ready to address today’s challenges. Federal leaders must think strategically about how to leverage this transformative technology to drive their missions forward while ensuring governance frameworks keep pace with innovation.

I built a multimodal AI Agent that explains medical scans in simple English. And I'm sharing ALL the code. Here's what it can do: 1. Comprehensive Image Analysis ↳ Identifies scan types (X-ray, MRI, CT, ultrasound) ↳ Detects anatomical regions automatically ↳ Highlights potential abnormalities 2. Smart Diagnostic Support ↳ Provides systematic observations ↳ Lists potential diagnoses ↳ Includes severity assessments 3. Web Search Capability ↳ Searches medical databases online ↳ Provides relevant search results as URL ↳ Supports clinical decisions 4. Technical Implementation ↳ Built with Gemini 2.0 Flash ↳ Runs on phidata framework ↳ Uses DuckDuckGo for web search Want to try it yourself? Here's the code 100% opensource 🌟 GitHub Repo: https://lnkd.in/dW6b_dEn This is STRICTLY for education and not for real diagnosis. P.S. I create these tutorials and opensource them for free. Your 👍 like and ♻️ repost keeps me going. So don't shy and share this post with your friends. Don't forget to follow me Shubham Saboo for daily tips and tutorials on LLMs, RAG and AI Agents.

For years, AI in pathology has primarily focused on narrow tasks such as classification, segmentation, and detection. While these approaches have shown tremendous value, they often depend heavily on large annotated datasets and predefined labels. Vision-Language Models (VLMs) introduce a fundamentally different paradigm. By combining image understanding with natural language reasoning, VLMs can interpret histopathology images in a far more contextual and human-like manner. Instead of simply identifying pixels or regions, these models can begin to describe morphological patterns, correlate findings, summarize observations, and support pathologists with explainable insights. Potential applications in Digital Pathology include: - Automated interpretation of histologic patterns - AI-assisted generation of descriptive findings - Context-aware triaging of whole slide images - Natural language querying of pathology datasets - Enhanced explainability of AI outputs - Multi-modal integration of pathology, genomics, and clinical data - Educational and training support for pathology workflows The future of computational pathology is likely not just “seeing” images, but understanding and communicating pathology in clinically meaningful language. We are entering the era of multimodal pathology AI. #DigitalPathology #ComputationalPathology #ArtificialIntelligence #VisionLanguageModels #Pathology #MachineLearning #HealthcareAI #GenerativeAI #MultimodalAI #CancerResearch #DrugDevelopment

𝗙𝗶𝗿𝘀𝘁 𝗔𝗻𝘆-𝘁𝗼-𝗔𝗻𝘆 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝗳𝗼𝗿 𝗘𝗮𝗿𝘁𝗵 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝘁𝗶𝗼𝗻 Satellite data just became infinitely more useful. Johannes Jakubik et al. have built the first truly generative multimodal foundation model that can create any type of Earth observation data from any other type. 𝗧𝗵𝗲 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵: TerraMind represents the first any-to-any generative, and large-scale multimodal model for Earth observation, trained on 1 trillion tokens from global geospatial data spanning 9 million samples worldwide. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 𝗻𝗼𝘄: Satellite data is often incomplete due to cloud coverage, sensor failures, or low revisit times. Environmental monitoring, agriculture planning, and disaster response all suffer from these data gaps. TerraMind can fill these gaps by generating missing data types from whatever sensors are available. 𝗞𝗲𝘆 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻𝘀 𝘁𝗵𝗮𝘁 𝘀𝗲𝘁 𝗶𝘁 𝗮𝗽𝗮𝗿𝘁: - 𝗗𝘂𝗮𝗹-𝘀𝗰𝗮𝗹𝗲 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: TerraMind encodes high-level contextual information in tokens to enable correlation learning and scaling, while additionally capturing important fine-grained representations using pixel-level inputs - 𝗧𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗶𝗻 𝗠𝗼𝗱𝗮𝗹𝗶𝘁𝗶𝗲𝘀: TerraMind introduces "thinking in modalities" (TiM)—the capability of generating additional artificial data during finetuning and inference to improve the model output - 𝗨𝗻𝗶𝘃𝗲𝗿𝘀𝗮𝗹 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Can produce optical imagery from radar data, create elevation maps from satellite photos, or generate land-use classifications from any input type 𝗛𝗶𝘀𝘁𝗼𝗿𝗶𝗰 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗺𝗶𝗹𝗲𝘀𝘁𝗼𝗻𝗲: TerraMindv1-B outperforms all other GeoFMs by at least 3pp avg. mIoU. Importantly, TerraMind is the only foundation model approach in EO that, across the PANGAEA benchmark, outperforms task-specific U-Net models 𝗥𝗲𝗮𝗹-𝘄𝗼𝗿𝗹𝗱 𝗮𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Generate missing Sentinel-2 optical data during cloudy periods using available Sentinel-1 radar data, create comprehensive land-use maps from minimal inputs, or produce elevation models for areas lacking topographic surveys. The model, dataset, and code are all open-sourced under permissive licensing. How could generative satellite data transform your industry or research? paper: https://lnkd.in/eNAz6Vfa models: https://lnkd.in/eMeTRRiP blog: https://lnkd.in/ewrtjv9h #EarthObservation #RemoteSensing #AIFoundationModels #GenerativeAI #SatelliteData #GeospatialAI #MachineLearning #DeepLearning #MultimodalAI — Subscribe to 𝘊𝘰𝘮𝘱𝘶𝘵𝘦𝘳 𝘝𝘪𝘴𝘪𝘰𝘯 𝘐𝘯𝘴𝘪𝘨𝘩𝘵𝘴 — weekly briefings on making vision AI work in the real world → https://lnkd.in/guekaSPf

𝗡𝗲𝘄 𝗽𝘂𝗯𝗹𝗶𝘀𝗵𝗲𝗱 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵! Breast cancer isn’t a single disease, it’s a complex spectrum of molecular subtypes, each demanding a tailored treatment. But the gold-standard diagnostic tools, like immunohistochemistry, can be invasive and may miss the full tumor picture. That’s why Chaima Ben Rabah, Eng-PhD, Aamenah Sattar and I asked: 𝐂𝐚𝐧 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈, 𝐮𝐬𝐢𝐧𝐠 𝐣𝐮𝐬𝐭 𝐚 𝐦𝐚𝐦𝐦𝐨𝐠𝐫𝐚𝐦 𝐚𝐧𝐝 𝐚 𝐟𝐞𝐰 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐜𝐥𝐮𝐞𝐬, 𝐢𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐛𝐫𝐞𝐚𝐬𝐭 𝐜𝐚𝐧𝐜𝐞𝐫 𝐬𝐮𝐛𝐭𝐲𝐩𝐞𝐬 𝐦𝐨𝐫𝐞 𝐚𝐜𝐜𝐮𝐫𝐚𝐭𝐞𝐥𝐲—𝐚𝐧𝐝 𝐧𝐨𝐧-𝐢𝐧𝐯𝐚𝐬𝐢𝐯𝐞𝐥𝐲? We built a multimodal deep learning model that integrates mammography images with clinical metadata, trained on 4K images from 1.7K patients, to classify five distinct breast cancer subtypes. The results? • Our 𝐦𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐀𝐈 model achieved 𝟖𝟗% 𝐀𝐔𝐂 in classifying the five subtypes. • A unimodal image-only model? Just 61% AUC. • That’s a leap of over 27%—by simply letting AI listen to more than just pixels. This work shows how combining visual and clinical data through AI can unlock new levels of diagnostic precision—bringing us one step closer to personalized, non-invasive breast cancer care. 📄 Paper: https://lnkd.in/efDm46rB 💻 Code: https://lnkd.in/edB4tddF Special thanks to Ahmed Ibrahim and all the AI Innovation Lab team. Weill Cornell Medicine Weill Cornell Medicine - Qatar Cornell University Cornell Tech #AI #Innovation #MultimodalAI #DeepLearning #BreastCancer #MedicalImaging #WomenInHealth #HealthcareInnovation #DigitalHealth #MDPI #PersonalizedMedicine #HealthTech #HealthcareAI #MachineLearning #Qatar #MENA #MiddleEast #NorthAfrica #MENAIRegion #MENAInnovation #UAE #UnitedArabEmirates #SaudiArabia #KSA #Egypt

🚀 AI Video Analysis in Healthcare: A Glimpse Into the Future The latest study from my colleague Anaïs Rameau and her team explores how Google Gemini—one of the only commercial AI models capable of video analysis—can interpret laryngoscopy videos. This work highlights the growing role of multimodal large language models (LLMs) in processing real-world medical video data. 💡 Key insight: Unlike traditional AI trained on static medical images, multimodal LLMs like Gemini can analyze real-time video without task-specific training, demonstrating strong procedure recognition but variable accuracy across diagnostic tasks. Notably, it could also generate procedural narrations, correctly describing key surgical steps while leaving room for refinement. Beyond video interpretation, the true power of multimodal LLMs lies in their ability to integrate multiple types of data—combining video, text, and even structured patient records to provide richer insights than single-modality AI models. 📌 What’s next for AI video analysis in healthcare? There are many potential applications: 🔷 ICU patient monitoring – Detecting respiratory distress, seizures, or patient deterioration. 🔷 Surgical AI – Identifying key steps in procedures and generating post-op summaries. 🔷 Neurology & movement disorders – Tracking Parkinson’s progression or tremor severity. 🔷 Rehabilitation & physical therapy – AI-powered motion tracking to personalize recovery plans. 🔷 Endoscopy & colonoscopy AI – While specialized AI solutions already exist for polyp detection, multimodal LLMs could take this further by combining real-time analysis with text-based insights, such as clinical notes or prior reports. As AI models continue improving, video analysis could become an important tool in clinical decision support and procedural automation. This study provides a compelling example of how AI can engage with dynamic medical data, with applications well beyond laryngoscopy. 🔗 Read the full study here (paywalled, DM for PDF): https://lnkd.in/emYp-8fJ

AI innovation in healthcare and life sciences is accelerating quickly, with leading experts from Google identifying several milestones anticipated by 2025. ➡️ Multimodal AI By synthesizing multiple data types (e.g., imaging and genomic data), multimodal AI is enabling a shift toward more precise, personalized medicine. Its ability to integrate context leads to greater accuracy and more natural outcomes ➡️ AI Agents and Multi-Agent Systems AI agents are evolving beyond basic chatbots to autonomously reason, plan, and adapt. When multiple agents collaborate, they unlock greater workflow capabilities, including administrative tasks such as nurse handoffs, freeing healthcare professionals for higher-value work. ➡️ Assistive Search Contextual AI-based search helps medical professionals navigate large volumes of research or patient data more efficiently. By understanding specialized terminology and abbreviations, systems can quickly return relevant, actionable information. ➡️ AI-Powered Customer Experience Generative AI solutions are creating seamless interactions across biotech, pharma, and patient-facing services. Organizations can personalize customer journeys, accelerate regulatory documentation, and streamline approvals. ➡️ Security Reinforcement AI is bolstering security by aiding threat detection and streamlining incident response. As AI matures, privacy and security measures become even more crucial for healthcare providers and life sciences companies. Bayer  - AI with medical imaging - Transforms large quantities of data into actionable insights for radiologists. Elanco - Gen AI framework on Vertex AI - Estimated ROI of $1.9M from improved business processes (e.g., Pharmacovigilance) Mayo Clinic - AI-powered Vertex AI search - Streamlined data access across millions of patient records for researchers Google Cloud | Aashima Gupta | Shweta Maniar

New research in JACC: Advances shows that the eye may offer a powerful, noninvasive window into coronary artery disease detection. In a multicenter study of 383 patients, deep learning models trained on retinal images were able to identify CAD with strong performance, outperforming traditional clinical risk scores, particularly in intermediate risk patients where clinical uncertainty is highest. When retinal imaging was combined with clinical indicators using a multimodal AI approach, diagnostic accuracy improved further, achieving an AUC of 0.91 with over 92 percent sensitivity. Because retinal and coronary vessels share similar vascular origins, microvascular changes captured by OCT and OCTA appear to reflect underlying coronary disease. AI enables these subtle patterns to be translated into scalable, radiation free screening and risk stratification tools. This work points toward a future where cardiovascular risk can be assessed earlier, more safely, and more equitably, especially in settings where invasive testing is limited. Multimodal AI may be key to shifting CAD detection upstream and personalizing prevention before clinical events occur. 🔗 https://lnkd.in/gWJUU447 Follow Zain Khalpey, MD, PhD, FACS for more on Ai & Healthcare. #AIinHealthcare #Cardiology #CoronaryArteryDisease #PreventiveCardiology #DigitalHealth #MedicalAI #MultimodalAI #DeepLearning #NonInvasiveDiagnostics #RetinalImaging #OCTA #OCT #CardiovascularHealth #RiskStratification #PrecisionMedicine #ClinicalInnovation #HealthEquity #CVImaging