𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗰𝗼𝘂𝗹𝗱 𝗿𝗲𝗱𝗲𝗳𝗶𝗻𝗲 𝗵𝗼𝘄 𝗰𝗹𝗶𝗻𝗶𝗰𝗶𝗮𝗻𝘀 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁 𝘄𝗶𝘁𝗵 𝗽𝗮𝘁𝗶𝗲𝗻𝘁𝘀. 𝗕𝘂𝘁 𝗶𝘁 𝗺𝘂𝘀𝘁 𝗯𝗲 𝗱𝗼𝗻𝗲 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆. This week, I’m sharing 5 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝘄𝗼𝗿𝗸𝗳𝗹𝗼𝘄 𝗽𝗮𝘁𝘁𝗲𝗿𝗻𝘀 that show how AI agents could reshape businesses. 👉 𝗗𝗮𝘆 𝟯: 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁 𝗳𝗼𝗿 𝗜𝗻𝗶𝘁𝗶𝗮𝗹 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲 This visual outlines a conceptual workflow, not a production system. It's meant to spark ideas, not bypass medical expertise. 𝟭. 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 𝗣𝗮𝘁𝗶𝗲𝗻𝘁 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Patients share symptoms via voice, text, images, or scans, with 𝗲𝘅𝗽𝗹𝗶𝗰𝗶𝘁 𝗰𝗼𝗻𝘀𝗲𝗻𝘁 and encrypted transmission. 𝟮. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲-𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 & 𝗘𝗻𝗰𝗼𝗱𝗶𝗻𝗴: Inputs are securely transformed into structured data using text/image/audio parsing, embedding models, and feature extractors. 𝟯. 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 𝗟𝗮𝘆𝗲𝗿: Using frameworks like CrewAI and LangChain, the system routes structured inputs to specialized diagnostic agents, each focused on tasks like retrieval, reasoning, or QA. 𝟰. 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗘𝗻𝗴𝗶𝗻𝗲 + 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗕: The agent consults a vector database of similar cases, symptoms, and clinical guidelines to retrieve relevant context. 𝟱. 𝗗𝗶𝗮𝗴𝗻𝗼𝘀𝘁𝗶𝗰 𝗘𝗻𝗴𝗶𝗻𝗲 + 𝗘𝗛𝗥 𝗔𝗣𝗜𝘀: APIs fetch the patient’s clinical history through secure, hospital-linked systems, ensuring continuity of care and 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗱𝗮𝘁𝗮 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲. 𝟲. 𝗖𝗹𝗶𝗻𝗶𝗰𝗮𝗹 𝗥𝗲𝘃𝗶𝗲𝘄 & 𝗢𝘂𝘁𝗽𝘂𝘁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗶𝗻𝗴: The AI proposes structured , insightsbut always 𝗿𝗼𝘂𝘁𝗲𝗱 𝘁𝗼 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗽𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹𝘀 for review before any clinical decision is made. 𝟳. 𝗚𝘂𝗮𝗿𝗱𝗿𝗮𝗶𝗹𝘀, 𝗘𝘁𝗵𝗶𝗰𝘀 & 𝗦𝗮𝗳𝗲𝘁𝘆 𝗙𝗶𝗹𝘁𝗲𝗿𝘀: Every stage is protected by multi-layered guardrails, including algorithmic constraints, explainability checks, and fairness safeguards. 𝟴. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲𝗱 𝗢𝘂𝘁𝗽𝘂𝘁 𝗗𝗲𝗹𝗶𝘃𝗲𝗿𝘆: Final outputs are shared with care teams via text, audio, or visual dashboards, ensuring clear communication without information overload. 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀: Use cases like this illustrate how AI agents can support, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲, clinicians. The goal is to free up cognitive load, provide faster context, and surface relevant clinical patterns earlier. But it's critical to state: • This is 𝗻𝗼𝘁 a production-ready blueprint. • 𝗛𝘂𝗺𝗮𝗻-𝗶𝗻-𝘁𝗵𝗲-𝗹𝗼𝗼𝗽 reviews are non-negotiable. • Data must remain secure, compliant, and auditable. • Models must be explainable, bias-aware, and monitored. • Ethical oversight is essential at every step. • Systems must comply with regulations like HIPPA, GDPR. Please repost ♻️ if you found this useful. Follow me for more insights on Agentic AI. Join our community agentbuild.ai to learn and build together. #AIAgents #AgenticAI #HealthcareAI #ClinicalAI #AgentBuildAI | 41 comments on LinkedIn