Key Applications of Multimodal AI

🚀 Introducing Multi-Modal Emotion-Aware AI Agents in Healthcare 🧠 Unlike traditional chatbots or scripted virtual assistants, these AI agents synthesize signals across multiple channels—voice tone, facial expressions, biometric data (like EEG or heart rate), language patterns, and behavior—to understand how a person feels, not just what they say. This emotional intelligence enables them to interact with patients more naturally, empathetically, and effectively. 💡 Where are they making a difference? • Mental Health & Digital Therapeutics: Supporting patients through CBT, trauma recovery, or anxiety management with emotionally adaptive dialogue. • Decentralized Clinical Trials: Ensuring consent comprehension, real-time symptom tracking, and emotionally-informed protocol engagement. • Remote Patient Monitoring: Detecting early signs of distress, disengagement, or health deterioration in chronic care. • Patient Intake & Triage: Recognizing emotional cues like stress or confusion to guide better clinician interactions. • Pediatrics & Elder Care: Responding to non-verbal distress where verbal communication may be limited. • Workplace Wellness & Resilience: Enhancing cognitive performance and emotional regulation in high-stakes professional settings. • Population Health & Digital Twins: Linking emotional states and behavioral patterns with disease trajectories for public health insight. 🌐 The future of healthcare will be intelligent, yes—but also emotionally attuned. #AIinHealthcare #AIAgents #EmotionAwareAI #MultimodalAI #DigitalHealth #MentalHealth #ClinicalTrials #PatientEngagement 

🧠 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

Your AI Will See You Now: Unveiling the Visual Capabilities of Large Language Models The frontier of AI is expanding with major advancements in vision capabilities across Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini, and Anthropic’s Claude. These developments are transforming how AI interacts with the world, combining the power of language with the nuance of vision. Key Highlights: •#ChatGPTVision: OpenAI’s GPT-4V introduces image processing, expanding AI’s utility from textual to visual understanding. •#GeminiAI: Google’s Gemini leverages multimodal integration, enhancing conversational abilities with visual data. •#ClaudeAI: Anthropic’s Claude incorporates advanced visual processing to deliver context-rich interactions. Why It Matters: Integrating visual capabilities allows #AI to perform more complex tasks, revolutionizing interactions across various sectors: •#Robots and Automation: Robots will utilize the vision part of multimodality to navigate and interact more effectively in environments from manufacturing floors to household settings. •#Security and Identification: At airports, AI-enhanced systems can scan your face as an ID, matching your image against government databases for enhanced security and streamlined processing. •#Healthcare Applications: In healthcare, visual AI can analyze medical imagery more accurately, aiding in early diagnosis and tailored treatment plans. These advancements signify a monumental leap towards more intuitive, secure, and efficient AI applications, making everyday tasks easier and safer. Engage with Us: As we continue to push AI boundaries, your insights and contributions are invaluable. Join us in shaping the future of multimodal AI. #AIRevolution #VisualAI #TechInnovation #FutureOfAI #DrGPT 🔗 Connect with me for more insights and updates on the latest trends in AI and healthcare. 🔄 Feel free to share this post and help spread the word about the transformative power of visual AI!

MIT and Harvard Medical School researchers just unlocked interactive 3D medical image analysis with language! Medical imaging AI has long been limited to rigid, single-task models that require extensive fine-tuning for each clinical application. 𝗩𝗼𝘅𝗲𝗹𝗣𝗿𝗼𝗺𝗽𝘁 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘃𝗶𝘀𝗶𝗼𝗻-𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗲𝗻𝗮𝗯𝗹𝗲𝘀 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲, 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗼𝗳 𝟯𝗗 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝘀𝗰𝗮𝗻𝘀 𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗻𝗮𝘁𝘂𝗿𝗮𝗹 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀. 1. Unified multiple radiology tasks (segmentation, volume measurement, lesion characterization) within a single, multimodal AI model. 2. Executed complex imaging commands like “compute tumor growth across visits” or “segment infarcts in MCA territory” without additional training. 3. Matched or exceeded specialized models in anatomical segmentation and visual question answering for neuroimaging tasks. 4. Enabled real-time, interactive workflows, allowing clinicians to refine analysis through language inputs instead of manual annotations. Notably, I like that the design includes native-space convolutions that preserve the original acquisition resolution. This addresses a common limitation in medical imaging where resampling can degrade important details. Excited to see agents being introduced more directly into clinician workflows. Here's the awesome work: https://lnkd.in/ggQ4YGeX Congrats to Andrew Hoopes, Victor Ion Butoi, John Guttag, and Adrian V. Dalca! I post my takes on the latest developments in health AI – 𝗰𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗺𝗲 𝘁𝗼 𝘀𝘁𝗮𝘆 𝘂𝗽𝗱𝗮𝘁𝗲𝗱! Also, check out my health AI blog here: https://lnkd.in/g3nrQFxW

How AI is Bridging the Gap Between Vision and Language with Multimodal? Imagine an AI that can understand text and analyze images and videos! Multimodal: These advanced models are breaking new ground by integrating vision and language capabilities. Merging Text & Vision: They transform both textual and visual data into a unified representation, allowing them to connect the dots between what they see and what they read. Specialized Encoders: Separate encoders handle text and visuals, extracting key features before combining them for deeper processing. Focused Attention: The model learns to focus on specific parts of the input (text or image) based on the context, leading to a richer understanding. So, how can we leverage this exciting technology? The applications are vast: Image Captioning 2.0: MM-GPTs can generate detailed and insightful captions that go beyond basic descriptions, capturing the essence of an image. Visual Q&A Master: Imagine asking a question about an image, and MM-GPTs can analyze the content and provide the answer! Smarter Search: MM-GPTs can revolutionize image search by allowing users to find images based on textual descriptions. Immersive AR/VR Experiences: MM-GPTs can dynamically generate narratives and descriptions within AR/VR environments, making them more interactive and engaging. Creative Text Generation: Imagine MM-GPTs composing poems or writing scripts inspired by images, blurring the lines between human creativity and machine generation. Enhanced Accessibility: MM-GPTs can generate detailed audio descriptions of images, making the digital world more inclusive for visually impaired users. The future of AI is undeniably multimodal, and MM-GPTs are at the forefront of this exciting new era. #AI #MachineLearning #NaturalLanguageProcessing #ComputerVision #MultimodalLearning #Innovation #FutureofTechnology

Brain Boost Drop #21 𝐌𝐮𝐥𝐭𝐢𝐦𝐨𝐝𝐚𝐥 𝐑𝐀𝐆 𝐄𝐱𝐩𝐥𝐚𝐢𝐧𝐞𝐝 𝐕𝐢𝐬𝐮𝐚𝐥𝐥𝐲 ! Retrieval-Augmented Generation (RAG) is revolutionizing AI-powered search and retrieval systems, but it's no longer limited to just text! With the integration of multimodal capabilities, we can now combine both text and images to enhance the retrieval process, making AI systems more context-aware and capable of providing richer, more accurate responses. How does Multimodal RAG work? 1️⃣ A custom knowledge base is built using both text and images. 2️⃣ Images are converted into embeddings using specialized image embedding models and stored in a vector database. 3️⃣ Similarly, text is processed using text embedding models and indexed for retrieval. 4️⃣ When a query is made, it is converted into embeddings using text embedding models. 5️⃣ A similarity search is performed in the vector database to fetch the most relevant images and text. 6️⃣ The retrieved content is combined and used as context to prompt a multimodal large language model (LLM). 7️⃣ The LLM generates a response, leveraging both textual and visual data to provide a more accurate and contextualized answer. Why does this matter? Multimodal RAG enables AI to go beyond traditional text-based retrieval and integrate visual understanding, making it ideal for applications such as: ✅ AI-powered search engines ✅ Advanced chatbots with better context awareness ✅ Medical and scientific research assistance ✅ E-commerce and recommendation systems ✅ Legal and financial document analysis The future of knowledge retrieval is multimodal! If you're building AI applications that rely on enhanced retrieval mechanisms, Multimodal RAG is something you should explore. What are your thoughts on the future of AI-powered retrieval? Let's discuss! Follow Nikhil Kassetty for more Brain Boost Drops. #AI #MachineLearning #MultimodalRAG #LLM #KnowledgeRetrieval #AIInnovation #DeepLearning

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.

This blog highlights the launch and significance of Microsoft’s Healthcare Agent Orchestrator, a powerful AI-driven platform designed to support complex, multidisciplinary medical workflows—most notably in cancer care. Key Significance:    •   Cancer treatment is highly personalized, but <1% of patients currently benefit from fully personalized care plans due to the high cost, time, and effort involved.    •   Multimodal Agentic AI can dramatically reduce the hours clinicians spend on reviewing complex patient data.    •   Microsoft’s platform enables orchestrated collaboration among specialized AI agents to streamline these workflows and integrate into tools clinicians already use (e.g., Microsoft Teams, Word, Copilot).    •   The goal is to scale precision medicine, speed up decision-making, and augment—rather than replace—human experts. Examples of Specialized Agents: 1. Patient History Agent – Builds a chronological patient timeline using Universal Medical Abstraction. 2. Radiology Agent – Provides a “second read” of medical imaging, using models like CXRReportGen/MAIRA-2. 3. Pathology Agent – Can link with external pathology agents like Paige.ai’s Alba, analyzing tumor slides. 4. Cancer Staging Agent – Applies AJCC clinical guidelines to accurately determine cancer stages. 5. Clinical Guidelines Agent – Uses NCCN guidelines to recommend treatments. 6. Clinical Trials Agent – Matches patients to trials, improving recall over baseline models. 7. Medical Research Agent – Synthesizes research findings into actionable clinical insights. 8. Report Creation Agent – Generates integrated, formatted reports for tumor boards. Real-World Impact & Collaborators:    •   Stanford Health Care, Johns Hopkins, UW Health, Mass General Brigham, and Providence Genomics are actively piloting or integrating these agents.    •   Real use cases include enhancing tumor board meetings, streamlining clinical trial matching, and deepening pathology insight via conversational interfaces (e.g., Paige.ai’s Alba in preview). Bottom Line: The healthcare agent orchestrator marks a pivotal step in democratizing precision oncology, accelerating collaboration between AI and human experts, and scaling care excellence through modular, customizable AI agents. It’s already in the hands of top institutions and could revolutionize how we approach cancer treatment at scale.

Aspiring AI Engineers who want to stand out should consider building their portfolio with use cases that most aren’t thinking about. Take multimodal systems, for example. Multimodal AI efficiently combines text, images, audio, and sensor data to create intelligent solutions. Bhavishya Pandit provides a look at 10 unusual multimodal use cases, hope you give it a go! 1️⃣ Artifact Restoration & Analysis: AI reconstructs ancient manuscripts, restores broken artifacts, and analyzes historical texts to uncover lost civilizations. 2️⃣ Extraterrestrial Resource Extraction: AI-powered satellites and rovers detect mineral-rich zones on the Moon, Mars, and asteroids, optimizing space mining operations. 3️⃣ Sunken City Exploration: Sonar, LIDAR, and AI-driven underwater drones help archaeologists discover and reconstruct submerged civilizations. 4️⃣ Wildlife Anti-Poaching Surveillance: Drones, thermal imaging, and acoustic monitoring detect poachers and protect endangered species. 5️⃣ Code Quality & Security Review: AI analyzes source code, developer comments, and runtime logs to detect vulnerabilities and optimize performance. 6️⃣ Noise Pollution Monitoring: Smart sensors map urban noise levels, integrating traffic and weather data to suggest mitigation strategies. 7️⃣ Predictive Infrastructure Maintenance: AI-powered drones and sensors monitor bridges, railways, and buildings to predict and prevent structural failures. 8️⃣ Risk Pattern Recognition: AI analyzes transactions, user behavior, and dark web intelligence to detect fraud in real time. 9️⃣ Legal Risk & Compliance Monitoring: AI scans contracts, case law, and regulatory updates to identify compliance risks. 🔟 Supply Chain Optimization: AI tracks carbon footprints, optimizes routes, and evaluates suppliers for sustainability. Check out the doc and find a few interesting ones you can work on. #genai #technology #artificialintelligence