NexusMed AI: Advanced Multi-Agent Clinical Research Platform

@AZTURK1

Hi there, I understand NexusMed AI needs a production-grade Literature Review Agent that retrieves peer-reviewed evidence (PubMed/Google Scholar) and returns live, citation-backed syntheses; my systems-to-ML background (Deno, WebSockets, RAG, agent orchestration) makes me well-suited to lead this phase. - Integrate Entrez (PubMed) API + a compliant Google Scholar pipeline (SerpAPI or dedicated scraper with rate-limits) and return structured citation metadata. - Implement advanced RAG: semantic retrieval, citation anchoring, and output formatter that inserts clickable PubMed/DOI links for every claim. - Harden WebSocket streaming: chunked synthesis, backpressure handling, reconnection and frontend live render hooks for React/Vite panels. - Biomedical reflection pass: chain the fine-tuned Llama SLM via LangGraph/LangChain to perform evidence-quality checks and flag uncertainty; include rollback/testing and staged deploy with validation suite. Skills: ✅ Deno v2 (TypeScript) ✅ WebSockets, React, Vite, Tailwind ✅ LangGraph / LangChain retrieval-RAG workflows ✅ Deployment: containerised services, local Ollama / Groq integration ✅ Security & compliance: rate-limits, audit logging, least-privilege API keys Certificates: ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ cPanel® & WHM Certified CWSA-2 I’m available to start immediately. Do you have PubMed Entrez API credentials and a preferred approach for Google Scholar (SerpAPI key vs controlled scraping) given compliance and rate-limit co

@bilal0356

Hi, We went through your project description and it seems like our team is a great fit for this job. We are an expert team which have many years of experience on Python, Machine Learning (ML), Node.js, React.js, Artificial Intelligence, Typescript, Full Stack Development, API Integration, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) Lets connect in chat so that We discuss further. Regards

@simar2176

Hello, I’m a Senior AI & Backend Engineer with 8+ years of experience, including enterprise experience at TCS, and I’m a Top-Level Seller on other professional platforms where I’ve delivered complex AI systems and research-oriented data platforms. Relevant Expertise • Multi-agent orchestration (LangGraph / LangChain architectures) • Retrieval-Augmented Generation with citation grounding • Academic database integrations (PubMed / Entrez / scholarly APIs) Approach for Your Next Phase Literature Review Agent • Direct PubMed integration via Entrez API • Structured parsing of abstracts, MeSH terms, and clinical metadata • Google Scholar querying with citation extraction layer Advanced RAG Layer • Indexed medical literature store Reflection Agent Improvements • Secondary evaluation pass using biomedical SLM • Confidence scoring for clinical outputs Real-Time Research Streaming • WebSocket event streaming • Incremental synthesis updates to frontend panels I understand the sensitivity and accuracy requirements when building tools for medical professionals and would focus on reproducibility, traceable citations, and model validation. Happy to review the current LangGraph state graph and help finalize the research agent architecture. Best regards, Mohit Sharma Senior AI Engineer Agentic RAG Systems | Clinical Research AI | TCS Experience | Top-Level Seller

@sanketpatil51515

Hi, I’m Sanket, a full-stack developer with hands-on experience building AI-driven systems, RAG pipelines, and multi-agent orchestration workflows. I’ve worked on structured AI platforms where deterministic outputs, citation grounding, and real-time streaming were critical. NexusMed AI is already well-architected — Deno + LangGraph + WebSockets + biomedical SLM is a strong base. Your next phase is clearly about deep evidence-grade retrieval + validation, not UI polish. How I’d Approach This 1. PubMed + Scholar Integration • Entrez (E-utilities) robust wrapper • Structured metadata extraction (PMID, DOI, abstract, authors) • Indexed citation storage for fast retrieval • Scholar scraping proxy or API-safe integration 2. Advanced RAG Layer • Dual-layer retrieval: – Vector embeddings for semantic relevance – Deterministic metadata filtering (year, trial type, impact) • Citation binding engine → every claim linked to source • Structured citation block generation 3. Real-time Streaming • Optimized WebSocket streaming (chunked synthesis) • Progressive citation attachment 4. Reflection Agent Upgrade • Biomedical Llama secondary validation • Claim–evidence consistency scoring • Flagging unsupported statements I understand medical sensitivity — hallucination control and evidence traceability are essential. If aligned, I’d like to review your current graph state + RAG layer to define upgrade milestones. — Sanket

@digitat

Although we are new to this platform, we bring solid hands-on experience building agentic RAG systems and integrating academic research APIs. For NexusMed AI, I will implement a robust PubMed (Entrez API) integration, replacing generic web search with structured, peer-reviewed literature retrieval. I will enhance your existing LangGraph pipeline to support citation-aware synthesis with real, clickable PubMed references and refine the Reflection Agent to verify claim-source consistency. The implementation will integrate seamlessly into your Deno + WebSocket architecture while preserving real-time streaming performance. Our focus is on delivering clean, production-ready engineering with medical data sensitivity in mind, establishing a strong evidence-based research foundation that can scale into advanced validation and confidence scoring layers.

@prakasht4

Hello, Your NexusMed AI platform looks like a strong foundation, and I’m interested in helping advance the Literature Review Agent. I have experience building RAG-based systems, integrating academic APIs, and working with agent orchestration frameworks like LangChain/LangGraph. I can implement reliable integrations with PubMed via the Entrez API and structure retrieval pipelines that prioritize peer-reviewed studies and clinical trials. I’ll enhance the RAG layer so generated outputs include accurate, clickable citations. I can also refine the Reflection Agent using the biomedical Llama model for secondary validation and improve the WebSocket streaming so research synthesis appears live in the UI. Happy to review the repository and discuss the next steps. Best regards, Prakash Timilsina